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<strong>NCSB</strong> <strong>2012</strong> <strong>Symposium</strong><br />
<strong>Abstract</strong> <strong>Book</strong>
TABLE OF CONTENTS<br />
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Table of contents<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 2 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Table of contents ..................................................................................................................................... 2<br />
Programme ............................................................................................................................................. 5<br />
Attendance list ......................................................................................................................................... 9<br />
<strong>Abstract</strong>s ............................................................................................................................................... 25<br />
Akhtar - CGC - Poster Flash ................................................................................................................. 26<br />
Angelopoulos – CGC (AddOn) - Poster ................................................................................................ 27<br />
Astola - CBSG - Oral ............................................................................................................................. 28<br />
Bakker - TIFN - Poster .......................................................................................................................... 29<br />
Besten - TIFN - Oral .............................................................................................................................. 30<br />
Boas - NISB - Poster ............................................................................................................................. 31<br />
Boer – External (VU, Amsterdam) - Poster ........................................................................................... 32<br />
Bosdriesz - NISB - Oral ......................................................................................................................... 34<br />
Chen – External (VU, Amsterdam) - Poster .......................................................................................... 35<br />
Dijk - CBSG - Poster Flash ................................................................................................................... 36<br />
Driel - <strong>NCSB</strong> - INVITED LECTURE ...................................................................................................... 37<br />
Dutta - NBIC - Oral ................................................................................................................................ 38<br />
Dutta - NBIC - Poster ............................................................................................................................ 39<br />
Ercan - TIFN - Poster ............................................................................................................................ 40<br />
Eunen - TIFN - Poster ........................................................................................................................... 41<br />
Fleck – External (WCSB, Wageningen) - Poster .................................................................................. 42<br />
Girolami - External (London, UK) - KEYNOTE ..................................................................................... 43<br />
Heemskerk - CMSB - Poster ................................................................................................................. 44<br />
Hendrickx – External (UvA, Amsterdam) - Poster Flash ....................................................................... 45<br />
Hettling - NBIC - Poster......................................................................................................................... 46<br />
Horst - NBIC - Poster ............................................................................................................................ 47<br />
Hugenholtz - TIFN – Poster .................................................................................................................. 48<br />
Janssens - CMSB - Poster .................................................................................................................... 49<br />
Khandelwal - NISB – Poster Flash ........................................................................................................ 50<br />
Klinken - CMSB - Oral ........................................................................................................................... 51<br />
Kuipers - KC - KEYNOTE ..................................................................................................................... 52<br />
Kutmon - NBIC - Poster ........................................................................................................................ 53<br />
Lam - External (WCSB, Wageningen) - Poster Flash ........................................................................... 54<br />
Lange - TIFN - Poster ........................................................................................................................... 55<br />
Le Dévédec – CMSB (AddOn) - Poster ................................................................................................ 56<br />
Mooij – External (RUN, Nijmegen) - Poster .......................................................................................... 57<br />
Nicholson - External (London, UK) - KEYNOTE ................................................................................... 58<br />
Nijveen - NBIC - Poster ......................................................................................................................... 59<br />
Palm - NISB – Poster Flash .................................................................................................................. 60<br />
Price - KC – Oral ................................................................................................................................... 61<br />
Rao - External (SBC-EMA, Groningen) - Poster ................................................................................... 62<br />
Rienksma - External (WCSB, Wageningen) - Oral ............................................................................... 63<br />
Rooij - CBSG - Poster ........................................................................................................................... 64<br />
Rossell – External (CSBB, Nijmegen) – Poster Flash .......................................................................... 65<br />
Schmitz - NISB - Oral ............................................................................................................................ 66<br />
Sips - CMSB - Poster Flash .................................................................................................................. 67<br />
Smits - External (UvA, Amsterdam) - Poster ........................................................................................ 68<br />
Steijaert - TIFN - Oral ............................................................................................................................ 69<br />
Su - External (La Jolla, US) - KEYNOTE .............................................................................................. 70<br />
Suarez-Diez - External (WCSB, Wageningen) - Poster ........................................................................ 71<br />
Supandi - NBIC - Oral ........................................................................................................................... 72<br />
Szabo - NISB - Poster ........................................................................................................................... 73<br />
Thijssen – External (CSBB, Nijmegen) – Poster Flash ......................................................................... 74<br />
Tsivtsivadze - External (WCSB, Wageningen) - Poster ........................................................................ 75<br />
Unen - NISB .......................................................................................................................................... 76<br />
Vanlier - CMSB - Oral ........................................................................................................................... 77<br />
Venema - TIFN - Poster ........................................................................................................................ 78<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Verbruggen - NISB - Oral ...................................................................................................................... 79<br />
Waagmeester - NBIC – Poster Flash .................................................................................................... 80<br />
Yuan – External (TUE, Eindhoven) - Poster ......................................................................................... 81<br />
Acknowledgements ............................................................................................................................... 82<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 4 / 83
PROGRAMME<br />
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Programme<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
From models and data to real life applications<br />
Dates: Thursday, <strong>2012</strong> November 01 & Friday, <strong>2012</strong> November 02<br />
Venue:<br />
Websites:<br />
'Cenakel', Kontakt der Kontinenten, Amersfoortsestraat 20, 3769AS<br />
Soesterberg<br />
www.ncsb.nl/ncsb<strong>2012</strong> & www.kontaktderkontinenten.nl<br />
Thursday, <strong>2012</strong> November 01<br />
10:00 - 10:30 Registration & coffee In former monastery 'Cenakel'<br />
10:30 - 10:45 Roel van Driel <strong>NCSB</strong> director<br />
Welcome<br />
Theme 1:<br />
Systems biology in biotechnology, including synthetic biology<br />
Chair: Natal van Riel Room: Cecilia Chapel (in Cenakel)<br />
10:45 - 11:30 Oscar Kuipers Netherlands (Dept of Molecular Genetics, University of<br />
Groningen)<br />
Highly modified peptides by synthetic biology approaches<br />
11:30 - 11:55 Claire Price KC / RUG (postdoc)<br />
Global regulators play a pivotal role in the evolution of Lactococcus lactis under<br />
constant growth conditions<br />
11:55 - 12:20 Joep Schmitz NISB / VU (postdoc)<br />
Towards a consensus model of yeast glycolysis<br />
12:20 - 12:45 Evert Bosdriesz NISB / VU (PhD-student)<br />
Escherichia coli implements a robust regulatory network motif that maximises<br />
growth rate<br />
12:45 - 14:00 Lunch Coffee corner / winter garden (in Cenakel)<br />
Theme 2:<br />
Data integration<br />
Chair: Chris Evelo<br />
Room: Cecilia Chapel (in Cenakel)<br />
14:00 - 14:45 Andrew Su United States (Dept of Molecular and Experimental<br />
Medicine, The Scripps Research Institute)<br />
Crowdsourcing biology: the Gene Wiki, BioGPS and biological games<br />
14:45 - 15:10 Anwesha Dutta NBIC / UM (PhD-student)<br />
Visualise your models: pathway based visualisation for integrative systems<br />
biology<br />
15:10 - 15:35 Rienk Rienksma External (WCSB) / WUR (PhD-student)<br />
A method to classify metabolic enzyme regulation from gene and protein<br />
expression data<br />
15:35 - 16:00 Joep Vanlier CMSB / TUE (PhD-student)<br />
Designing optimal experiments to identify progressive adaptations in biological<br />
systems<br />
16:00 - 16:30 Coffee, tea Coffee corner / winter garden (in Cenakel)<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Poster flash:<br />
2.5 minute pitches with 1-2 slides of selected posters<br />
16:30 - 17:00 Chair: Frank Bruggeman Room: Cecilia Chapel (in Cenakel)<br />
1 Waseem Akhtar A TRIP through the genome: a high-throughput<br />
method to study the influence of chromatin context<br />
on gene regulation<br />
2 Bram Thijssen Bayesian data integration of time-series omics data<br />
through dynamical models of the yeast cell cycle<br />
3 Diana Hendrickx Exploring cellular decision-making principles by timeresolved<br />
metabolomics<br />
4 Ruchir Khandelwal Predicting fluxes in microbial communities through<br />
physiological properties of participating microbes<br />
5 Carolyn Lam A systems biology approach to decipher the<br />
interactions in a microbial community during<br />
conversion of toxic 4-chlorosalicylate into potentially<br />
useful metabolites<br />
6 Aalt-Jan van Dijk Modelling the floral transition: linking changes in<br />
gene expression with changes in flowering-time<br />
phenotype<br />
7 Margriet Palm Cell-based modelling of angiogenesis suggests that<br />
tip cell selection via lateral inhibition enables vessel<br />
stabilisation by repressing tip cell fate in branches<br />
8 Sergio Rossell Inferring metabolic states in uncharacterised<br />
environments using gene-expression measurements<br />
9 Fianne Sips A computational framework to analyse<br />
heterogeneous plasma lipoprotein metabolism<br />
10 Andra Waagmeester Pathway curation: from isolated pathway knowledge<br />
to search-click-and-grow<br />
17:00 - 18:30 Poster session Room Steyl (1st floor in main building)<br />
Drinks & bites<br />
18:30 - 20:00 Dinner Restaurant (in main building)<br />
Round table:<br />
Best practices in systems biology<br />
20:00 - 21:00 Moderator: Bert Groen Room: Cecilia Chapel (in Cenakel)<br />
Panel members: invited speakers & session chairs<br />
21:00 - 23:00 Socialising over drinks Bar / Winter Garden (in Cenakel)<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Friday, <strong>2012</strong> November 02<br />
08:00 - 09:00 Breakfast Restaurant (in main building)<br />
Theme 3:<br />
Systems and personalised medicine<br />
Chair: Dirk-Jan<br />
Reijngoud<br />
Room: Cecilia Chapel (in Cenakel)<br />
09:00 - 09:45 Jeremy Nicholson United Kingdom (Biological Chemistry, Dept of Surgery<br />
and Cancer, Imperial College, London)<br />
Phenotyping the patient journey: systems medicine in the real world<br />
09:45 - 10:10 Jan-Bert van Klinken CMSB / LUMC (postdoc)<br />
A systems biology approach for enriching genetic association studies of<br />
metabolite profiles with prior pathway knowledge<br />
10:10 - 10:35 Paul Verbruggen NISB / UvA (postdoc)<br />
Dynamics of an in vivo chromatin associated system: nucleotide excision DNA<br />
repair<br />
10:35 - 11:05 Coffee, tea Coffee corner / winter garden (in Cenakel)<br />
11:05 - 11:30 Farahaniza Supandi NBIC / VU (PhD-student)<br />
Computational prediction of changes in cerebral metabolic fluxes from mRNA<br />
expression data<br />
11:30 - 11:55 Roel van Driel University of Amsterdam (Nuclear Organisation & Synthetic<br />
Systems Biology)<br />
The MetSyn initiative: can the life sciences live up to the expectations?<br />
11:55 - 12:00 Announcement of Poster Prize<br />
12:00 - 13:00 Lunch Coffee corner / winter garden (in Cenakel)<br />
Theme 4:<br />
Systems biology in nutrition<br />
Chair: Frank Bruggeman Room: Cecilia Chapel (in Cenakel)<br />
13:00 - 13:45 Mark Girolami United Kingdom (Dept of Statistical Science, University<br />
College London)<br />
Thomas Bayes FRS: The 17th Century English Parson that Assists with Modern<br />
Day Systems Biology<br />
13:45 - 14:10 Marvin Steijaert TIFN / TNO (postdoc)<br />
Integrative analysis of bacterial short chain fatty acid production<br />
14:10 - 14:35 Laura Astola CBSG / WUR (postdoc)<br />
A missing link in the network, can we ever find it?<br />
14:35 - 15:00 Gijs den Besten TIFN / UMCG (PhD-student)<br />
In vivo fluxes rather than concentrations of short-chain fatty acids distinguish the<br />
physiological effect of nutritional fibers<br />
15:00 - 15:15 Coffee, tea Coffee corner / winter garden (in Cenakel)<br />
15:15 - 16:15 Meeting of <strong>NCSB</strong> Principal Investigators<br />
Chair: Roel van Driel Room: Cecilia Chapel (in Cenakel)<br />
<strong>NCSB</strong>-PI04-<strong>2012</strong>1102 meeting (by invitation only)<br />
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ATTENDANCE LIST<br />
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Attendance list<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Dr Waseem Akhtar<br />
Van Lohuizen Laboratory, Division of Molecular Genetics, Netherlands Cancer Institute (NKI)<br />
Plesmanlaan 121, 1066CX Amsterdam, The Netherlands<br />
Email: w.akhtar@nki.nl; Tel: +31-20-512 1766<br />
Petra J. Alkema, BSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: p.j.alkema@student.tue.nl; Tel:<br />
Dr Nicos Angelopoulos, PhD<br />
Bioinformatics & Statistics, Division of Molecular Biology, Netherlands Cancer Institute (NKI)<br />
Plesmanlaan 121, 1066CX Amsterdam, The Netherlands<br />
Email: n.angelopoulos@nki.nl; Tel: +31-20-512 2088<br />
Lisette C.M. Anink, MSc<br />
Synthetic Systems Biology and Nuclear Organisation Group (NOG), Swammerdam Institute for Life Sciences<br />
(SILS), Faculty of Science (FNWI), University of Amsterdam (UvA)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: l.c.m.anink@uva.nl; Tel: +31-20-525 7943<br />
Dr Laura J. Astola<br />
Biometris / Mathematics and Statistical Methods, Department of Plant Sciences, Wageningen University &<br />
Research Centre (WUR)<br />
Postbus 100, 6700AC Wageningen, The Netherlands<br />
Email: laura.astola@wur.nl; Tel: +31-317-482 384<br />
Prof. Dr Barbara M. Bakker<br />
Medical Systems Biology, Department of Pediatrics, Centre for Liver, Digestive and Metabolic Diseases,<br />
University Medical Centre Groningen (UMCG)<br />
PO Box 30.001, 9700RB Groningen, The Netherlands<br />
Email: b.m.bakker@med.umcg.nl; Tel: +31-50-361 1542<br />
Dr Hans H.G.M. van Beek<br />
Section Medical Genomics, Department of Clinical Genetics, VU University Medical Centre (VUmc)<br />
Van der Boechorststraat 7, 1081BT Amsterdam, The Netherlands<br />
Email: hans.van.beek@vu.nl; Tel: +31-20-598 7460<br />
Dr Judy R. van Beijnum<br />
Department of Medical Oncology, VU University Medical Center (VUmc)<br />
De Boelelaan 1118, 1081HV Amsterdam, The Netherlands<br />
Email: j.vanbeijnum@vumc.nl; Tel: +31-20-444 2406<br />
Drs Gijs den Besten<br />
Quantitative Systemsbiology, Departments of Pediatrics, Centre for Liver, Digestive and Metabolic Disease,<br />
University Medical Centre Groningen (UMCG)<br />
PO Box 30001, 9700RB Groningen, The Netherlands<br />
Email: g.den.besten@med.umcg.nl; Tel: +31-50-391 1409<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 10 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Lyske Gais de Bildt<br />
Haarlemmerhouttuinen 167, 1013GM Amsterdam, The Netherlands<br />
Email: lyskegais@gmail.com; Tel:<br />
Ir Bo Blanckenburg<br />
Bioinformatica, Cluster Techniek, Hogeschool Leiden<br />
Postbus 382, 2300AJ Leiden, The Netherlands<br />
Email: blanckenburg.b@hsleiden.nl; Tel: +31-71-518 8310<br />
Sonja E.M. Boas, MSc<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group, Centrum Wiskunde &<br />
Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: s.e.m.boas@cwi.nl; Tel: +31-20-592 9333<br />
Dr Wim P.H. de Boer<br />
Department Medical Oncology, VU University Medical Centre (VUmc)<br />
p/a Jozef Israelslaan 333, 2282TJ Rijswijk, The Netherlands<br />
Email: wdeboer@ziggo.nl; Tel: +31-70-399 6929<br />
Dr Sacha Bohler<br />
Research Group Environmental Biology, Centre for Environmental Sciences, Hasselt University<br />
(Campus Diepenbeek, Building D) Agoralaan, 3590 Diepenbeek, Belgium<br />
Email: sacha.bohler@uhasselt.be; Tel: +32-11- 268 225<br />
Evert Bosdriesz, MSc<br />
Department of Molecular Cell Physiology, Faculty of Earth and Life Sciences (FALW), VU University<br />
Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: e.bosdriesz@vu.nl; Tel:<br />
Dr Ir Bernd W. Brandt<br />
Preventive Dentistry, Academisch Centrum Tandheelkunde Amsterdam (ACTA)<br />
Gustav Mahlerlaan 3004, 1081LA Amsterdam, The Netherlands<br />
Email: bbrandt@acta.nl; Tel:<br />
Prof. Dr Frank J. Bruggeman<br />
NISB Junior Group Leader, Systems Biology of Molecular Regulatory Networks, Life Sciences Group,<br />
Netherlands Institute for Systems Biology (NISB) & CWI & UvA-SILS<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: frank.bruggeman@sysbio.nl; Tel: +31-20-592 4132<br />
Guang Quan Chen, PhD<br />
Animal Ecology group, VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: g.chen@vu.nl; Tel: +31-20-598 ????<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Ceylan Colmekci-Oncu, MSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
(WH 3.109) Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: c.colmekci.oncu@tue.nl; Tel: +31-40-247 5573<br />
Dr Susan Coort<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: susan.coort@maastrichtuniversity.nl; Tel: +31-43-388 ???<br />
Jesse C. van Dam, MSc<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: jesse.vandam@wur.nl; Tel:<br />
Dr Pascale A.S. Daran-Lapujade<br />
Industrial Microbiology, Department of Biotechnologie, Faculty of Applied Sciences (TNW), Delft University<br />
of Technology (DUT)<br />
Julianalaan 67, 2628BC Delft, The Netherlands<br />
Email: p.a.s.daran-lapujade@tudelft.nl; Tel:<br />
Mark Davids, MSc<br />
Systems & Synthetic Biology, Microbiology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: mark.davids@wur.nl; Tel: +31-317-483 112<br />
Marijke A. Dermois, BSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: m.a.dermois@student.tue.nl; Tel:<br />
Dr Aalt-Jan van Dijk<br />
Applied Bioinformatics (AB), Bioscience, Plant Research International (PRI), Wageningen University &<br />
Research Centre (WUR)<br />
Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands<br />
Email: aaltjan.vandijk@wur.nl; Tel: +31-317-480 994<br />
Dr Tjeerd M.H. Dijkstra<br />
Institute for Computing and Information Sciences (ICIS), Radboud University Nijmegen (RU)<br />
Postbus 9010, 6500GL Nijmegen, The Netherlands<br />
Email: t.dijkstra@science.ru.nl; Tel:<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Prof.em. Dr Roel van Driel<br />
<strong>NCSB</strong> Director, c/o <strong>NCSB</strong> Bureau, University of Amsterdam, Netherlands Consortium for Systems Biology<br />
(<strong>NCSB</strong>)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: roel.vandriel@ncsb.nl; Tel: +31-20-525 5150<br />
Anwesha Dutta, MSc<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
(UNS50 Box 19) Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: anwesha.dutta@maastrichtuniversity.nl; Tel: +31-43-388 1187<br />
Robbin van den Eijnde, BSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: h.r.m.v.d.eijnde@student.tue.nl; Tel:<br />
Onur Ercan, MSc<br />
Health Division, NIZO Food Research<br />
Postbus 20, 6710BA Ede, The Netherlands<br />
Email: onur.ercan@nizo.com; Tel: +31-318-659 668<br />
Dr Karen van Eunen<br />
Quantitative Systemsbiology, Department of Pediatrics, Centre for Liver, Digestive and Metabolic Disease,<br />
University Medical Centre Groningen (UMCG)<br />
PO Box 30001, 9700RB Groningen, The Netherlands<br />
Email: k.van.eunen@med.umcg.nl; Tel: +31-50-361 1409<br />
Dr Chris T.A. Evelo<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: chris.evelo@maastrichtuniversity.nl; Tel: +31-43-388 1231<br />
Martin A. Fitzpatrick<br />
Centre for Translational Inflammation Research, Queen Elizabeth Hospital, University of Birmingham<br />
Mindelsohn Way, B15 2WB Birmingham, United Kingdom<br />
Email: mxf793@bham.ac.uk; Tel: +44-789-980 2998<br />
Dr Christian Fleck<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: christian.fleck@wur.nl; Tel:<br />
Raoul Frijters<br />
R&D Bioinformatics, Rijk Zwaan<br />
Eerste Kruisweg 9, 4793RS Fijnaart, The Netherlands<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Email: r.frijters@rijkzwaan.nl; Tel: +31-168-468 600<br />
Prof. Mark Girolami, PhD<br />
Department of Statistical Science, University College London (UCL)<br />
Torrington Place 1-19, WC1E 7HB London, United Kingdom<br />
Email: m.girolami@ucl.ac.uk; Tel: +44-20-7679 1861<br />
Remko Gouw, Ing.<br />
Bioinfomics<br />
Colijnstraat 38, 2221AE Katwijk aan Zee, The Netherlands<br />
Email: remko@bioinfomics.nl; Tel:<br />
Dr Ir Albert A. de Graaf, PhD<br />
Department of Biosciences, Cluster Earth, Environment and Life Sciences, Netherlands Organisation for<br />
Applied Scientific Research (TNO)<br />
Postbus 360, 3700AJ Zeist, The Netherlands<br />
Email: albert.degraaf@tno.nl; Tel: +31-88-866 5023<br />
Prof. Dr Bert K. Groen<br />
Quantitative Systems Biology, Laboratory of Pediatrics, Department of Pediatrics, University Medical Centre<br />
Groningen (UMCG)<br />
Postbus 30001, 9700RB Groningen, The Netherlands<br />
Email: a.k.groen@med.umcg.nl; Tel: +31-50-363 2669<br />
Drs Michael A. Guravage<br />
Centrum Wiskunde & Informatica (CWI), Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project),<br />
Life Sciences Group, Centrum Wiskunde & Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: guravage@cwi.nl; Tel: +31-20-592 4028<br />
Dicle Hasdemir, MSc<br />
BioSystems Data Analysis (BDA), Swammerdam Institute for Life Sciences (SILS), Faculty of Science (FNWI),<br />
University of Amsterdam (UvA)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: d.hasdemir@uva.nl; Tel: +31-20-525 7936<br />
Ir Marit Hebly<br />
Industrial Microbiology, Department of Biotechnology, Faculty of Applied Sciences (TNW), Delft University<br />
of Technology (DUT)<br />
Julianalaan 67, 2628BC Delft, The Netherlands<br />
Email: m.heblij@tudelft.nl; Tel: +31-15-278 2439<br />
Ir Mattijs M. Heemskerk<br />
Department of Human Genetics (S4-P), Leiden University Medical Centre (LUMC)<br />
Postbus 9600, 2300RC Leiden, The Netherlands<br />
Email: m.m.heemskerk@lumc.nl; Tel: +31-71-526 9452<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Diana M. Hendrickx, MSc<br />
BioSystems Data Analysis (BDA), Swammerdam Institute for Life Sciences (SILS), Faculty of Science (FNWI),<br />
University of Amsterdam (UvA)<br />
Science Park 904, 1098XH Amsterdam, The Netherlands<br />
Email: d.m.hendrickx@uva.nl; Tel: +31-20-525 7936<br />
Hannes Hettling, MSc<br />
Centre for Integrative Bioinformatics (IBIVU), Faculty of Sciences (FEW) & Faculty of Earth and Life Sciences<br />
(FALW), VU University Amsterdam (VU)<br />
De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands<br />
Email: j.hettling@vu.nl; Tel: +31-20-598 3716<br />
Dr Ir Guido J.E.J. Hooiveld<br />
Nutrition, Metabolism & Genomics Group, Division of Human Nutrition, Department of Agrotechnology and<br />
Food Science, Wageningen University & Research Centre (WUR)<br />
Postbus 8129, 6700EV Wageningen, The Netherlands<br />
Email: guido.hooiveld@wur.nl; Tel: +31-317-485 788<br />
Dr Eelke van der Horst<br />
Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics (KEBB),<br />
Academic Medical Centre (AMC)<br />
Postbus 22700, 1100DE Amsterdam, The Netherlands<br />
Email: e.vanderhorst@amc.uva.nl; Tel: +31-20-566 ????<br />
Ir Sharon Janssens<br />
Section of Biomedical NMR, Department of Biomedical Engineering, Faculty of Biomedical Technologie<br />
(BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: s.janssens@tue.nl; Tel: +31-40-247 4705<br />
Johann de Jong<br />
Bioinformatics & Statistics, Netherlands Cancer Institute (NKI)<br />
Plesmanlaan 121, 1066CX Amsterdam, The Netherlands<br />
Email: j.d.jong@nki.nl; Tel: +31-20-512 2099<br />
Prof. Dr Antoine H.C. van Kampen<br />
Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics (KEBB),<br />
Academic Medical Centre (AMC)<br />
Postbus 22700, 1100DE Amsterdam, The Netherlands<br />
Email: a.h.vankampen@amc.uva.nl; Tel: +31-20-566 7096<br />
Mannus Kempe, MSc<br />
Synthetic Systems Biology and Nuclear Organisation Group (NOG), Swammerdam Institute for Life Sciences<br />
(SILS), Faculty of Science (FNWI), University of Amsterdam (UvA)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: h.kempe@uva.nl; Tel:<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Ruchir A. Khandelwal, MSc<br />
Molecular Cell Physiology - Systems Bioinformatics, Faculty of Earth and Life Sciences (FALW), VU University<br />
Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: r.a.khandelwal@vu.nl; Tel: +31-598 6966<br />
Dr Jan-Bert van Klinken<br />
Scientific Researcher, Department of Human Genetics (S4P), Leiden University Medical Centre (LUMC)<br />
Postbus 9600, 2300RC Leiden, The Netherlands<br />
Email: j.b.van_klinken@lumc.nl; Tel: +31-71-526 9472<br />
Prof. Dr Oscar P. Kuipers<br />
Department of Molecular Genetics, Faculty of Mathematics and Natural Sciences & Groningen Institute of<br />
Biomolecular Sciences and Biotechnology (GBB), University of Groningen (RUG)<br />
Postbus 11103, 9700CC Groningen, The Netherlands<br />
Email: o.p.kuipers@rug.nl; Tel: +31-50-363 2093<br />
Martina Kutmon, MSc<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: martina.kutmon@maastrichtuniversity.nl; Tel: +31-43-388 1993<br />
Dr Carolyn Lam<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: carolyn.lam@wur.nl; Tel:<br />
Katja Lange, Dipl. Troph.<br />
Human Nutrition, Department of Agrotechnology and Food Science, Wageningen University & Research<br />
Centre (WUR)<br />
Postbus 8129, 6700EV Wageningen, The Netherlands<br />
Email: katja.lange@wur.nl; Tel:<br />
Chris van der Lans<br />
Bioinformatica, Cluster Techniek, Hogeschool Leiden<br />
Postbus 382, 2300AJ Leiden, The Netherlands<br />
Email: s1049018@student.hsleiden.nl; Tel:<br />
Dr Aat M. Ledeboer<br />
<strong>NCSB</strong>-TIFN project, Top Institute Food and Nutrition (TIFN)<br />
Burgemeester Le Fèvre de Montignyplein 8, 3055NL Rotterdam, The Netherlands<br />
Email: aat.ledeboer@hetnet.nl; Tel:<br />
Ir Sylvia E. Ledevedec, PhD<br />
Toxicology, Gorlaeus Laboratory, Leiden Amsterdam Centre for Drug Research (LACDR), Faculty of Science,<br />
Leiden University (UL)<br />
Einsteinweg 55, 2333CC Leiden, The Netherlands<br />
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<strong>Abstract</strong> book<br />
Email: s.e.ledevedec@lacdr.leidenuniv.nl; Tel: +31-71-527 6226<br />
Michael Liem<br />
Bioinformatica, Cluster Techniek, Hogeschool Leiden<br />
Postbus 382, 2300AJ Leiden, The Netherlands<br />
Email: s1047457@student.hsleiden.nl; Tel:<br />
Ya-fen Lin<br />
Laboratory of Genetics, Wageningen University & Research Centre (WUR)<br />
Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands<br />
Email: ya-fen.lin@wur.nl; Tel: +31-317-484 682<br />
Dr Paul van der Logt<br />
Bioscience, Nutrition and Health, Unilever Discover, Unilever<br />
Olivier van Noortlaan 120, 3133AT Vlaardingen, The Netherlands<br />
Email: paul-van-der-logt@unilever.com; Tel:<br />
Sven Menschel<br />
Microbiology, Department of Agrotechnology and Food Science, Wageningen University & Research Centre<br />
(WUR)<br />
Dreijenplein 10, 6703 Wageningen, The Netherlands<br />
Email: svenmenschel@gmail.com; Tel:<br />
Dr Roeland M.H. Merks<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group, Centrum Wiskunde &<br />
Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: roeland.merks@sysbio.nl; Tel: +31-20-592 4117<br />
Tom Mokveld<br />
Bioinformatica, Cluster Techniek, Hogeschool Leiden<br />
Postbus 382, 2300AJ Leiden, The Netherlands<br />
Email: s1051228@student.hsleiden.nl; Tel:<br />
Prof. Dr Jaap Molenaar<br />
Director Biometris, Mathematics and Statistical Methods, Department of Plant Sciences, Wageningen<br />
University & Research Centre (WUR)<br />
Postbus 100, 6700AC Wageningen, The Netherlands<br />
Email: jaap.molenaar@wur.nl; Tel: +31-317-486 042<br />
Dr Martijn Moné<br />
Dept of Molecular Cell Physiology, Faculty of Earth and Life Sciences (FALW), VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: m.j.mone@vu.nl; Tel: +31-20-598 7194<br />
Dr Joris M. Mooij<br />
Intelligent Systems, Department of Computer Science, Faculty of Science (FWN) / Institute for Computing<br />
and Information Sciences (ICIS), Radboud University Nijmegen (RU)<br />
Postbus 9010, 6500GL Nijmegen, The Netherlands<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Email: j.mooij@cs.ru.nl; Tel: +31-24-365 2354<br />
Dr Ir Simon van Mourik<br />
Biometris, Mathematics and Statistical Methods, Department of Plant Sciences, Wageningen University &<br />
Research Centre (WUR)<br />
Postbus 100, 6700AC Wageningen, The Netherlands<br />
Email: simon.vanmourik@wur.nl; Tel:<br />
Prof. Dr Bela Mulder<br />
Group Leader, Theory of Biomolecular Matter, FOM Institute for Atomic and Molecular Physics (AMOLF)<br />
Postbus 41883, 1009DB Amsterdam, The Netherlands<br />
Email: mulder@amolf.nl; Tel: +31-20-754 7100<br />
Dr Ir Jan-Peter H. Nap<br />
Applied Bioinformatics (AB), Bioscience, Plant Research International (PRI), Wageningen University &<br />
Research Centre (WUR)<br />
Postbus 619, 6700AP Wageningen, The Netherlands<br />
Email: janpeter.nap@wur.nl; Tel: +31-317-480 984<br />
Prof. Jeremy K. Nicholson<br />
Biological Chemistry, Department of Surgery and Cancer, Imperial College London<br />
South Kensington Campus, SW7 2AZ London, United Kingdom<br />
Email: j.nicholson@imperial.ac.uk; Tel: +44-20-7594 3195<br />
Drs Harm Nijveen, Ing.<br />
Laboratory of Bioinformatics, Wageningen University & Research Centre (WUR)<br />
Postbus 8128, 6700ET Wageningen, The Netherlands<br />
Email: harm.nijveen@wur.nl; Tel: +31-317-484 706<br />
Dr Brett G. Olivier<br />
Systems Bioinformatics, Faculty of Earth and Life Sciences (FALW), VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: b.g.olivier@vu.nl; Tel:<br />
Ir Margriet M. Palm, MSc<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group, Centrum Wiskunde &<br />
Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: m.m.palm@cwi.nl; Tel: +31-20-592 4167<br />
Dr Claire E. Price<br />
Department of Molecular Genetics, Faculty of Mathematics and Natural Sciences (FWN) & Groningen<br />
Institute of Biomolecular Sciences and Biotechnology (GBB), University of Groningen (RUG)<br />
Nijenborgh 7, 9747AG Groningen, The Netherlands<br />
Email: c.e.price@rug.nl; Tel: +31-50-363 8052<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Dr Jeanine J. Prompers<br />
Section of Biomedical NMR, Department of Biomedical Engineering, Faculty of Biomedical Technologie<br />
(BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: j.j.prompers@tue.nl; Tel: +31-40-247 3128<br />
Dr Shodhan Rao<br />
Medical Systems Biology, Department of Pediatrics, Centre for Liver, Digestive and Metabolic Diseases,<br />
University Medical Centre Groningen (UMCG)<br />
PO Box 30.001, 9700RB Groningen, The Netherlands<br />
Email: s.rao@umcg.nl; Tel: +31-50-361 9349<br />
Dr Xiang-Yu Rao<br />
R&D Bioinformatics, Rijk Zwaan<br />
Eerste Kruisweg 9, 4793RS Fijnaart, The Netherlands<br />
Email: catfish873@hotmail.com; Tel: +31-168-468 600<br />
Prof. Dr Dirk-Jan Reijngoud<br />
Department of Laboratory Medicine, Laboratory Metabolic Diseases, University Medical Centre Groningen<br />
(UMCG)<br />
(Huispost EA60) Postbus 30.001, 9700RB Groningen, The Netherlands<br />
Email: d.j.reijngoud@med.umcg.nl; Tel: +31-50-361 1253<br />
Prof. Dr Ir Marcel J.T. Reinders<br />
Pattern Recognition and Bioinformatics, Faculty of Electrical Engineering, Mathematics and Computer<br />
Science (EWI), Delft University of Technology (DUT)<br />
Mekelweg 4, 2628CD Delft, The Netherlands<br />
Email: m.j.t.reinders@tudelft.nl; Tel: +31-15-278 6424<br />
Dr Fahrad Rezaee<br />
Medical Proteomics, Cell Biology, University Medical Centre Groningen (UMCG)<br />
A. Deusinglaan 1, 9713AV Groningen, The Netherlands<br />
Email: f.rezaee@med.umcg.nl; Tel: +31-50-363 8147<br />
Dr Ir Natal A.W. van Riel, MSc PhD<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: n.a.w.v.riel@tue.nl; Tel: +31-40-247 5506<br />
Rienk A. Rienksma, MSc<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: rienk.rienksma@wur.nl; Tel: +31-317-484 454<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Ton Rijnders, PhD<br />
Scientific Director, Top Institute Pharma (TIP)<br />
Postbus 142, 2300AC Leiden, The Netherlands<br />
Email: ton.rijnders@tipharma.com; Tel: +31-71-332 2035<br />
Luc Rijsdam<br />
Bioinformatica, Cluster Techniek, Hogeschool Leiden<br />
Postbus 382, 2300AJ Leiden, The Netherlands<br />
Email: blanckenburg.b@hsleiden.nl; Tel:<br />
Prof. Dr Ir Jo M.M. Ritzen<br />
Empower European Universities<br />
Kloosterweg 54, 6241GB Bunde, The Netherlands<br />
Email: jo.ritzen@empowereu.org; Tel: +31-43-388 3155<br />
Jop A. van Rooij, MSc<br />
Theoretical Biology & Bioinformatics, Department of Biology, Faculty of Science, Utrecht University (UU)<br />
Postbus 80.056, 3508TB Utrecht, The Netherlands<br />
Email: j.a.vanrooij@uu.nl; Tel: +31-30-253 ????<br />
Sergio Rossell, PhD<br />
Centre for Systems Biology and Bioenergetics (CSBB), University Medical Centre St Radboud (UMCN)<br />
Geert Grooteplein 26-28, 6525GA Nijmegen, The Netherlands<br />
Email: s.rossell@cmbi.ru.nl; Tel: +31-24-361 9693<br />
Dr Diman van Rossum<br />
<strong>NCSB</strong> Programme Manager, c/o <strong>NCSB</strong> Bureau, University of Amsterdam (UvA), Netherlands Consortium for<br />
Systems Biology (<strong>NCSB</strong>)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: diman.vanrossum@ncsb.nl; Tel: +31-20-525 5150<br />
Susanne Roth<br />
Molecular Cell Physiology, Faculty of Earth and Life Sciences (FALW), VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: s.roth@vu.nl; Tel: +31-20-598 6966<br />
Dr Peter J. Schaap<br />
Laboratory of Systems and Synthetic Biology, Microbiology, Wageningen University & Research Centre<br />
(WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: peter.schaap@wur.nl; Tel: +31-317-485 142<br />
Joep P.J. Schmitz, MSc<br />
Systems Bioinformatics, Faculty of Earth and Life Sciences (FALW) / IBIVU, VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: j.p.j.schmitz@vu.nl; Tel:<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Anne Schwabe, MSc<br />
NISB Junior Group, Scientific Computing for Systems Biology, Multiscale Modelling and Nonlinear Dynamics,<br />
Centrum Wiskunde & Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: a.schwabe@cwi.nl; Tel: +31-20-592<br />
Ir Fianne L.P. Sips<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: f.l.p.sips@tue.nl; Tel:<br />
Dr Gertien J. Smits<br />
Molecular Biology and Microbial Food Safety, Swammerdam Institute for Life Sciences (SILS), Faculty of<br />
Science (FNWI), University of Amsterdam (UvA)<br />
Science Park 904, 1098XH Amsterdam, The Netherlands<br />
Email: g.j.smits@uva.nl; Tel: +31-20-525 5143<br />
Dr Ir Marvin N. Steijaert<br />
Microbiology and Systems Biology, Department of Biosciences, TNO Quality of Life, Netherlands<br />
Organisation for Applied Scientific Research (TNO)<br />
Postbus 360, 3700AJ Zeist, The Netherlands<br />
Email: marvin.steijaert@tno.nl; Tel: +31-30-694 4734<br />
Andrew Su, PhD<br />
Department of Molecular and Experimental Medicine (MEM), The Scripps Research Institute<br />
(MEM-216) North Torrey Pines Road 10550, CA92037 La Jolla, California, United States of America<br />
Email: asu@scripps.edu; Tel:<br />
Dr Maria Suarez Diez<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: maria.suarezdiez@wur.nl; Tel: +31-317-484 454<br />
Georg Summer, MSc<br />
Cardiology, Faculty of Health, Medicine and Life Sciences, Maastricht University (UM)<br />
Universiteitssingel 50, 6229ER Maastricht, The Netherlands<br />
Email: g.summer@maastrichtuniversity.nl; Tel: +31-43-388 2959<br />
Farahaniza Supandi, MSc<br />
Section Medical Genomics, Department of Clinical Genetics, VU University Medical Centre (VUmc)<br />
Van der Boechorststraat 7, 1081BT Amsterdam, The Netherlands<br />
Email: f.supandi@vumc.nl; Tel: +31-20-598 2832<br />
Dr András Szabó<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group (MAC4), Centrum<br />
Wiskunde & Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Email: a.szabo@cwi.nl; Tel: +31-20-592 4077<br />
Dr Radek Szklarczyk<br />
Centre for Molecular and Biomolecular Informatics (CMBI), Nijmegen Centre for Molecular Life Sciences<br />
(NCMLS) / Centre for Systems Biology and Bioenergetics (CSBB), University Medical Centre St Radboud<br />
(UMCN)<br />
Postbus 9010, 6500GL Nijmegen, The Netherlands<br />
Email: radek@cmbi.ru.nl; Tel:<br />
Prof. Dr Bas Teusink<br />
Systems Bioinformatics, Faculty of Earth and Life Sciences (FALW) / IBIVU, VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: b.teusink@vu.nl; Tel: +31-20-598 9435<br />
Bram Thijssen, BSc<br />
Institute for Computing and Information Sciences (ICIS), Radboud University Nijmegen (RU)<br />
Postbus 9010, 6500GL Nijmegen, The Netherlands<br />
Email: bramsemail@gmail.com; Tel:<br />
Dr Peter Tindemans<br />
Director, Global Knowledge Strategies & Partnerships<br />
Jozef Israelslaan 41, 2596AN 's-Gravenhage, The Netherlands<br />
Email: peter@tindemans.demon.nl; Tel: +31-6-2044 1945<br />
Hieng-Ming Ting<br />
Laboratory of Plant Physiology, Department of Plant Sciences, Wageningen University & Research Centre<br />
(WUR)<br />
Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands<br />
Email: jimmytinghm@gmail.com; Tel:<br />
Dr Evgeni Tsivtsivadze<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: evgeni@science.ru.nl; Tel:<br />
Dr Séverine S. Urdy<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group, Centrum Wiskunde &<br />
Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: urdy@cwi.nl; Tel: +31-20-592 9333<br />
Remco A. Ursem<br />
R&D Bioinformatics, Rijk Zwaan<br />
Eerste Kruisweg 9, 4793RS Fijnaart, The Netherlands<br />
Email: r.ursem@rijkzwaan.nl; Tel: +31-168-468 600<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Ir Joep Vanlier, MSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
(WH 3.2a) PO Box 513, 5600MB Eindhoven, The Netherlands<br />
Email: j.vanlier@tue.nl; Tel: +31-40-247 5573<br />
Dr Koen Venema<br />
Pharmacokinetics & Human Studies, Department of Biosciences, Cluster Earth, Environment and Life<br />
Sciences, Netherlands Organisation for Applied Scientific Research (TNO)<br />
Postbus 360, 3700AJ Zeist, The Netherlands<br />
Email: koen.venema@tno.nl; Tel: +31-888-661 886<br />
Dr Paul Verbruggen<br />
Synthetic Systems Biology and Nuclear Organisation Group (NOG), Swammerdam Institute for Life Sciences<br />
(SILS), Faculty of Science (FNWI), University of Amsterdam (UvA)<br />
Postbus 94215, 1090GE Amsterdam, The Netherlands<br />
Email: p.verbruggen@uva.nl; Tel: +31-20-525 5136<br />
Remi S. Verhoeven, BSc<br />
Biomodelling and Biosytems Analysis Group (<strong>NCSB</strong>-NISB-project), Life Sciences Group, Centrum Wiskunde &<br />
Informatica (CWI)<br />
Postbus 94079, 1090GB Amsterdam, The Netherlands<br />
Email: verhoeve@cwi.nl; Tel: +31-20-592 9333<br />
Jack W. Vink, BSc<br />
Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics (KEBB),<br />
Academic Medical Centre (AMC)<br />
Postbus 22700, 1100DE Amsterdam, The Netherlands<br />
Email: j.w.vink@amc.uva.nl; Tel: +31-20-566 ????<br />
Prof. Dr Roel J. Vonk<br />
Medical Biomics, Cell Biology, University Medical Centre Groningen (UMCG)<br />
Postbus 30.001, 9700RB Groningen, The Netherlands<br />
Email: r.j.vonk@med.umcg.nl; Tel:<br />
Andra Waagmeester<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
(UNS50 box 19) Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: andra.waagmeester@maastrichtuniversity.nl; Tel: +31-43-388 1187<br />
Dr Johan A. Westerhuis<br />
BioSystems Data Analysis (BDA), Swammerdam Institute for Life Sciences (SILS), Faculty of Science (FNWI),<br />
University of Amsterdam (UvA)<br />
Science Park 904, 1098XH Amsterdam, The Netherlands<br />
Email: j.a.westerhuis@uva.nl; Tel: +31-20-525 6546<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 23 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Prof. Dr Ir Ko A.P. Willems van Dijk, PhD<br />
Department of Human Genetics (S4-P) & General Internal Medicine, Leiden University Medical Centre<br />
(LUMC)<br />
Postbus 9600, 2300RC Leiden, The Netherlands<br />
Email: k.willems_van_dijk@lumc.nl; Tel: +31-71-526 9470<br />
Dr Egon L. Willighagen<br />
BiGCaT group, Department of Bioinformatics, Faculty of Health, Medicine and Life Sciences, Maastricht<br />
University (UM)<br />
(UNS50 box 19) Postbus 616, 6200MD Maastricht, The Netherlands<br />
Email: egon.willighagen@maastrichtuniversity.nl; Tel: +31-43-388 1187<br />
Drs Nilgun Yilmaz<br />
Molecular Cell Physiology, Faculty of Earth and Life Sciences (FALW), VU University Amsterdam (VU)<br />
De Boelelaan 1085, 1081HV Amsterdam, The Netherlands<br />
Email: nilguenyilmaz@gmail.com; Tel:<br />
Hui Li Yuan, MSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
(WH 2.104) Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: h.yuan@tue.nl; Tel:<br />
Niels A. Zondervan, BSc<br />
Laboratory for Systems & Synthetic Biology, Department of Agrotechnology and Food Science, Wageningen<br />
University & Research Centre (WUR)<br />
Dreijenplein 10, 6703HB Wageningen, The Netherlands<br />
Email: nazondervan@hotmail.com; Tel:<br />
Sjanneke M. Zwaan, BSc<br />
Computational Biology Group, Division of Biomedical Imaging & Modelling, Faculty of Biomedical<br />
Technologie (BMT), Eindhoven University of Technology (TUE)<br />
Postbus 513, 5600MB Eindhoven, The Netherlands<br />
Email: j.m.zwaan@student.tue.nl; Tel:<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 24 / 83
ABSTRACTS<br />
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
<strong>Abstract</strong>s<br />
<strong>Abstract</strong>s are sorted by surname of the presenting author.<br />
The presenting author is listed first in the abstract’s authors list.<br />
Coding at top of each abstract page:<br />
Surname – <strong>NCSB</strong> partner – type of abstract (keynote, invited lecture, oral, poster-flash, poster)<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 25 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Akhtar - CGC - Poster Flash<br />
A TRIP through the genome: a high-throughput method to study the influence of chromatin<br />
context on gene regulation<br />
Waseem Akhtar 1 , Johann de Jong 3 , Alexey Pindyurin 2 , Ludo Pagie 2 , Lodewyk Wessels 3 , Maarten van<br />
Lohuizen 1 , Bas van Steensel 2<br />
1 Division of Molecular Genetics, 2 Division of Gene Regulation, 3 Division of Molecular Biology, Netherlands<br />
Cancer Institute, Amsterdam, The Netherlands<br />
The genetic information in the cell comprises of coding and non-coding genes and regulatory elements<br />
arranged on the DNA, which is bound by histones and other proteins to form chromatin. This genetic<br />
information is under epigenetic control mediated by DNA methylation and post-translational modifications<br />
of histones. It is however unclear how local chromatin context affects the function of regulatory elements.<br />
In addition to transcription, chromatin is a substrate for many other biological processes such as<br />
replication, splicing and DNA damage repair. Furthermore, recent studies have pointed towards an<br />
intriguing, although poorly explored link between chromatin and distantly related processes such as<br />
translation and mRNA stability. Here we present a system, named Thousands of Reporters Integrated in<br />
Parallel (TRIP) that can be used to study the influence of local chromatin architecture on these processes<br />
systematically. As a prototype, we have developed a reporter system to study the effect of chromatin<br />
microenvironment on gene expression in a high throughput manner. To this end we have generated<br />
libraries of mouse embryonic stem cells with random insertions of reporter transgenes using PiggyBac<br />
transposons. Each of these transgenes (harbouring identical promoter and reporter sequences) contains a<br />
unique nucleotide barcode at the end of its transcription unit. Genomic locations of individual transgenes<br />
can be determined by mapping their respective barcodes. Deep sequencing analysis of barcode<br />
representations in cDNA preparations from the same population of cells allows us to quantify the<br />
expression of all transgenes simultaneously. We have generated a data set of >17,000 transgenes with the<br />
murine housekeeping PGK promoter integrated throughout the genome. These transgenes, depending on<br />
their locations, show nearly 1,000-fold variability in their expression. Integration with dozens of genomewide<br />
epigenome maps revealed candidate regulatory mechanisms. In particular, we found that transgenes<br />
landing in lamina-associated domains show very low expression suggesting that these domains are<br />
inherently less permissible for transcription. In addition, by using an inducible promoter we have begun to<br />
study the influence of chromatin microenvironment on the dynamics of transcriptional activation. Taken<br />
together, our novel high-throughput approach offers a new ‘systems level’ tool for the understanding of<br />
gene regulation by chromatin context.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 26 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Angelopoulos – CGC (AddOn) - Poster<br />
Development of in silico models for in vitro tumour metastasis<br />
Nicos Angelopoulos 1 , Sylvia Le Devedec 2 , Emma Spanjaard 3 , Karin Legerstee 4 , Kuan Yan 5 , Vasiliki-Maria<br />
Rogkotis 2 , Fons J. Verbeek 5 , Adriaan Houtsmuller 4 , Bob van de Water 2 , Johan de Rooij 3 , Lodewyk Wessels 1<br />
1 NKI/AVL, Amsterdam; 2 Div. of Toxicology Leiden University; 3 Hubrecht Institute, Utrecht; 4 Josephine<br />
Nefkens Institute, Erasmus MC, Rotterdam, 5 LIACS, Leiden University<br />
We present the conceptual underpinnings and a number of practical stepping stones of a multi disciplinary<br />
project that aims to produce models which are useful to the systematic study of cancer and the<br />
development of anti-metastasis drugs. Although we do not present generated models as yet, we identify<br />
the biological system of interest along with the necessary technologies and key techniques we are starting<br />
to utilise in the project. Tumour metastasis is a highly complex process that includes the dissipation of cells<br />
from their original site into the distant organs of the patients. The initial steps in this process are welldescribed<br />
in in vitro tumour cell-based models in which the transition from static to a motile cell behaviour<br />
is induced by specific extracellular stimuli, like transforming growth factor β (TGFβ) and hepatocyte growth<br />
factor (HGF). Cellular components that are critical for the execution of the transition are clustered in large<br />
and highly dynamic protein networks and complexes. Focal adhesions are one of these complexes that<br />
regulate the linkage between (tumour) cells and the extracellular matrix. Due to the availability of<br />
sophisticated fluorescence-based microscopic imaging technologies we are now able to determine the<br />
dynamics of these networks and complexes in unprecedented detail under conditions that mimic crucial<br />
early steps in metastasis. The overall objective of this project is to develop quantitative and predictive in<br />
silico models of the relation between focal adhesion dynamics, the signalling networks that communicate<br />
external stimuli to focal adhesions and the metastatic potential of tumour cells.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 27 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Astola - CBSG - Oral<br />
A missing link in the network, can we ever find it?<br />
Laura Astola, Simon van Mourik, Jaap Molenaar<br />
Biometris, Wageningen University & Research Centre<br />
Flavonoids are plant secondary metabolites and as such an integral part of the human diet. There is<br />
increasing evidence that these polyphenols may explain the beneficial effects of a diet rich in fruits and<br />
vegetables on the prevention of several chronic and age-related diseases. To control the amount that these<br />
micronutrients are present in our daily food, we need to understand the molecular pathways that lead to<br />
the accumulation of these compounds. In our earlier work on flavonoid pathway inference [1-3], we have<br />
found ourselves in what seems to be a typical situation in systems biology, where the molecular<br />
interactions are fairly well known, the kinetic parameters are completely unknown and where the network<br />
topology is almost known [4]. This means that we have only a few missing links in our network structure.<br />
There is an abundant literature that describe methods for finding such missing links accompanied with<br />
results, when these methods are a pplied to real data. Some of them are top-down and very abstract [5],<br />
others are more context-dependent relying on the existing knowledge on the features of the specific<br />
system dynamics [6]. Here we want to turn the problem setting upside down and ask how dramatic can the<br />
effect of a missing link be to the inference results. When the network is robust against structural changes in<br />
topology, the effect of such missing links on the network performance can be limited [7]. We focus here on<br />
dynamic networks and consider three different ordinary differential equation-(ODE) based models: a linear<br />
model, a non-linear Michaelis-Menten-type model and a complex non-linear model for gene regulatory<br />
networks as in the DREAM<strong>2012</strong> challenge [8]. We investigated the discrepancy between the true and<br />
inferred network as a function of number of edges removed and their average connectivity.<br />
To measure the degree of failure, we look at the percentage of lost/red undant edges as well as the errors<br />
in the inferred parameters. We believe that our findings can be instructive and useful for the biological<br />
network inference practitioners.<br />
References:<br />
1. Astola et al. "Metabolic pathway inference from time series data: a non iterative approach.", In: 6th<br />
IAPR International Conference, Pattern Recognition in Bioinformatics, Lecture Notes in Bioinformatics.<br />
Volume 7036., Springer<br />
2. Astola et al. "Inferring the genes underlying flavonoid production in tomato." , In: 9th International<br />
Workshop on Computational Systems Biology, Ulm, Germany<br />
3. Astola et al. "Tree graphs and identifiable parameter estimation\\ in metabolic networks.", Submitted<br />
to BMC Systems Biology (<strong>2012</strong>)<br />
4. Esse et al. "A mathematical model for brassinosteroid insensitive1-mediated signaling in root growth<br />
and hypocotyl elongation.", Plant Physiology <strong>2012</strong><br />
5. Leskovec et al. "Kronecker graphs: An approach to modeling netwo rks", Journal of Machine Learning<br />
Research 2010<br />
6. Lee et al. "Computational methods for discovering gene networks from expression data", Briefings in<br />
bioinformatics 2009<br />
7. Dijk et al. "Mutational robustness of gene regulatory networks", Plos One <strong>2012</strong><br />
8. "Dialogue for Reverse Engineering Assessments and Methods", http://www.the-dream-project.org<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 28 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Bakker - TIFN - Poster<br />
Systems biology of trypanothione synthetase: a complex enzyme catalysing the multistep<br />
synthesis of trypanothione<br />
Barbara M. Bakker 1 , Jurgen Haanstra 1 , Alejandro Leroux 2 , Silicotryp Consortium, R. Luise Krauth-Siegel 2<br />
1 University of Groningen, University Medical Centre, Groningen, The Netherlands; 2 Biochemie Zentrum der<br />
Universität Heidelberg (BZH), Heidelberg, Germany<br />
Trypanosoma brucei, the causative agent of African sleeping sickness, relies on the trypanothione<br />
metabolism for redox homeostasis, a pathway essential for parasite survival. The use of trypanothione<br />
makes the T. brucei redox system distinct from the mammalian glutathione system and trypanothione<br />
biosynthesis is therefore considered a potential target for antitrypanosomal drugs. The Silicon<br />
Trypanosome Project aims to construct a comprehensible dynamic model of T. brucei which includes redox<br />
metabolism. To this end, a previously constructed kinetic model of T. brucei glycolysis will be extended with<br />
the trypanothione system. The model extension will be based on kinetic characterisation of recombinant<br />
enzymes in an in vivo like buffer and intracellular enzyme concentrations measured by quantitative<br />
Western blot analyses.<br />
For many enzymes in the model a standard enzyme-kinetic equation can be used. Trypanothione<br />
synthetase (TryS), however, has a rather complex mechanism, which is currently only partly understood.<br />
TryS catalyses the production of reduced trypanothione (T(SH)2) in a two-step reaction from two molecules<br />
of glutathione (GSH) and one molecule of spermidine (Spd) using two ATP. In the first step, GSH is<br />
conjugated to spermidine to create glutathionylspermidine (Gsp) with the consumption of ATP. The second<br />
step is similar to the first one, but then the glutathione is conjugated with the Gsp with the use of another<br />
ATP. The substrate GSH and the product T(SH)2 inhibit the reaction. The reaction itself is virtually<br />
irreversible but in amidase reactions, which formally are the reversal of the synthetase reactions, the<br />
intermediate Gsp and the end-product T(SH)2 are hydrolysed to GSH and Spd or GSH and Gsp, respectively.<br />
Furthermore, the enzyme displays a low ATPase activity in the absence of other substrates.<br />
To obtain insight in the exact mechanism, we have formulated a kinetic model describing the catalytic cycle<br />
o f TryS. The elementary reaction steps are represented by linear kinetic equations, yielding a model with<br />
26 rate constants. In an iterative process of experiments and modelling we compare model variants and fit<br />
the parameters to the experimental data. Ultimately, we plan to reduce the most plausible model variant<br />
to a kinetic rate equation capturing all the experimentally observed dynamics.<br />
Our approach thus applies systems biology not only at the pathway level, but also at the level of single, yet<br />
complex, enzymes. We anticipate that our extended models will provide tools for increasingly detailed<br />
network-based selection of potential drug targets against T. brucei.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 29 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Besten - TIFN - Oral<br />
In vivo fluxes rather than concentrations of short-chain fatty acids distinguish the physiological<br />
effect of nutritional fibers<br />
Gijs den Besten 1,2 , Shodhan Rao 1,3 , Karen van Eunen 1,2 , Albert Gerding 1 , Bert K. Groen 1,2,3 , Barbara M.<br />
Bakker 1,2,3 and Dirk-Jan Reijngoud 1,2,3<br />
1 Center for Liver Digestive and Metabolic Diseases, University of Groningen, University Medical Center<br />
Groningen, Groningen, The Netherlands; 2 Netherlands Consortium for Systems Biology, Amsterdam, The<br />
Netherlands; 3 Systems Biology Centre for Energy Metabolism and Ageing, University of Groningen,<br />
Groningen, The Netherlands<br />
The main end products of bacterial fermentation of fibres in the large intestine - acetate (C2), propionate<br />
(C3), and butyrate (C4) - are found to be of high importance for colonic health maintenance and regulation<br />
of host metabolism. Multiple studies showed that increased fibre intake results in elevated cecal<br />
concentrations of short-chain fatty acids (SCFA). Concentrations, however, are the result of the balance of<br />
production and uptake of SCFA and do not give information about the fluxes per se. The latter requires<br />
stable isotope methodology in vivo.<br />
To get insight into the in vivo steady-state SCFA fluxes, we continuously infused 13C-labelled SCFA into the<br />
cecum of mice and measured the isotopic enrichment of SCFA after six hours. To calculate all relevant<br />
fluxes, we developed a new mathematical model of SCFA metabolism, which accounts for all production<br />
and consumption fluxes of the different SCFA and their interconversions by the cecal microbiome. We<br />
applied the method to mice fed a high-fat diet and investigated how the steady state SCFA fluxes were<br />
affected when 10% of cellulose in the diet was replaced by the fibres fructooligosaccharide (FOS) or Guar<br />
Gum (GG).<br />
After six weeks the mice in the GG group showed significantly reduced body weight compared to the<br />
control group, whereas FOS had no effect on body weight (Control 30.4±0.5, FOS 29.8±0.5, GG 26.4±0.4<br />
gram). Only GG decreased the glucose and insulin levels, resulting in a lower HOMA-IR compared to the<br />
control group (Control 15.7±2.0, FOS 13.2±1.8, GG 6.9±1.0). This beneficial effect of GG on insulin<br />
sensitivity of the host could not be explained by the cecal SCFA concentrations, which increased three times<br />
in either fibre group as compared to the control group (Control 43±2, FOS 126±7, GG 123±5 mM). Also the<br />
in vivo SCFA production rates were increased in both fibre groups, but significantly more pronounced in the<br />
GG group than in the FOS group (Control: 60 ±0.9, 18±1.0, 0.3±1.2 FOS: 205±11, 67±16, 0.0±1.2 GG:<br />
270±12, 117±17, 0.0±1.3 µmol/h/mouse for acetate, propionate and butyrate, respectively). The same<br />
effect was found in the in vivo SCFA uptake rates by the host (Control: 54±1.3, 18±0.4, 5.5±0.3 FOS: 198±14,<br />
49±5, 24±1.4 GG: 240±15, 120±6, 27±1.5 µmol/h/mouse for acetate, propionate and butyrate,<br />
respectively). The production flux of butyrate was zero, indicating that butyrate is not directly formed from<br />
the fibre nutrients. The butyrate produced was solely formed from acetate.<br />
To our knowledge we are the first to determine production and uptake fluxes of SCFA in vivo in mice.<br />
Although in both fibre diets the cecal SCFA concentration was increased evenly, the production and uptake<br />
fluxes were higher in the GG group. We hypothesise that the difference in the fluxes is at least partially<br />
responsible for the difference in body weight and glucose handling in the two fibre groups.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Boas - NISB - Poster<br />
An unifying hypothesis for lumen formation during angiogenesis<br />
Sonja E.M. Boas, Roeland M.H. Merks<br />
Dept of Life Sciences, CWI, Amsterdam; <strong>NCSB</strong>-NISB, Amsterdam<br />
Blood vessel development (angiogenesis) is important during embryogenesis and for many processes<br />
throughout our entire lifespan, such as wound healing and also tumour growth. New blood vessels hollow<br />
(lumen formation) to allow blood perfusion. Despite decades of experimental research, two alternative<br />
lumen formation hypotheses are still debated. The vacuolation hypothesis originally suggests that a lumen<br />
forms intracellularly by fusion of vacuoles and this view was extended to extracellular lumen formation by<br />
exocytosis of vacuoles. The cell-cell repulsion hypothesis assumes that lumens initiate by active repulsion of<br />
adjacent cells and are expanded by cell shape changes. We present a plausible mechanism to unify the two<br />
alternative hypotheses: we use a computational model to show that they can arise in the same vessel from<br />
a single mechanistic framework.<br />
Our model represents endothelial cells in a branched vessel that eventually forms a lumen. It is based on<br />
the Cellular Potts Model, which allows explicit modelling of cell shape. This model reproduces both lumen<br />
formation hypotheses, based on two underlying, molecular mechanisms: membrane polarisation and<br />
vesicle cycling. These mechanisms are deduced from literature studies. Membrane polarisation is triggered<br />
by the extracellular matrix and results in a basolateral membrane and an apical membrane. The lumen will<br />
form at the apical membrane. During vacuolation, pinocytotic vesicles fuse into vacuoles, which are<br />
preferentially exported at the apical membrane through exocytosis. During cell-cell repulsion, vesicles<br />
containing negatively charged CD34-sialomucins (e.g. PODXL) are targeted to the apical membrane. We use<br />
the computational model to test if this mechanistic framework can explain the conflicting experimental<br />
observations. Each of the mechanisms in the model can be examined to gain new insights in their function<br />
and relative importance in lumen formation during angiogenesis.<br />
Acknowledgments:<br />
We thank the Indiana University and the Biocomplexity Institute for providing the CC3D modelling<br />
environment.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 31 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Boer – External (VU, Amsterdam) - Poster<br />
Programme Curfit: two-dimensional alignment of GC-GC chromatograms<br />
W.P.H. de Boer 1 , G. Vivo-Truyols 2 , J. Lankelma 1<br />
1 Dept. of Molecular Cell Physiology, VU, Amsterdam; 2 Van‘t Hoff Institute for Molecular Sciences, UVA,<br />
Amsterdam<br />
The computer programme Curfit 1 has originally been developed to align LC-MS chromatograms with the<br />
profile of a reference chromatogram by iterative computation of a two-dimensional warping function.<br />
However the underlying algorithm Curfit2D appears to be better suited to align GC-GC or LC-LC<br />
chromatograms. The algorithm assumes both chromatogram axes to increase monotonically in value, which<br />
does not hold for mass spectra. Here we demonstrate the potential of the algorithm for the alignment of<br />
GC-GC diesel oil samples.<br />
We had three batches of three diesel oil samples, each batch from a different oil company. The samples<br />
turned out to require baseline drift correction and some degree of data smoothing to broaden sharp peaks.<br />
Curfit therefore has been extended by adding two additional algorithms, both based on a running window,<br />
to allow execution of these pre-processing steps. In addition it proved to be necessary to introduce a form<br />
of data normalisation to reduce the dominant influence of large peaks on the alignment calculation since<br />
the data comprise a very (106) large “single” peak and a series of medium (103) peaks in the remainder of<br />
the domain. One option is to give the area around the single peak a low weight in the alignment<br />
calculation, but a better approach is normalisation of each column (row) of the chromatogram intensity<br />
matrix to its unit average. The normalisation can then be undone after computation of the warping<br />
function.<br />
The accuracy of the alignment can be judged from a number of output criteria. First, the relative rootmean-square<br />
(RMS) error between retention times of reference and sample chromatograms after<br />
alignment, relative to the RMS before alignment. Since the relative RMS error is also used to decide on<br />
convergence of the Curfit2D algorithm, an independent criterion is the matrix correlation coefficient 2 which<br />
is the matrix correlation of the reference chromatogram and the aligned sample chromatogram, in terms of<br />
matrix inner products. In addition the cross-correlation of the total intensity curve (TIC) of reference and<br />
sample chromatograms is informative in value and lag (global shift). The TICs are obtained by summing up<br />
the intensities of the columns (rows) of the chromatograms, normalized to the number of columns (rows).<br />
The diesel oil samples have been pairwise aligned, to a total of 72 alignments. The remaining nine pairings,<br />
of a sample with itself, requires only a single iteration of the Curfit2D algorithm and yields the obvious 100<br />
percent alignment. All pairs involved baseline correction and data smoothing, with a running window of 5 x<br />
5 retention time measuring points. The alignments were all of high quality, and virtually insensitive to pair<br />
switching. The matrix correlations are presented in the table below. The warping function essentially shows<br />
a linear drift, up to -7 seconds along the rows and up to - 9.7 10 3 seconds along the columns of the sample<br />
chromatogram. All runs were done on a 64-bit workstation under Windows 7.<br />
Once accepted for publication, the programme will be made freely available on the VU and/or The<br />
Netherlands Bioinformatics Centre (NBIC) website in the interest of scientific progress.<br />
References<br />
1. de Boer, W.P.H., Lankelma, J., Bischoff, R., Horvatovich, P., “Program Curfit: two-dimensional alignment<br />
of LC-MS chromatograms”, Poster <strong>Abstract</strong> NBIC conference 2011, Lunteren, The Netherlands.<br />
2. Ramsey, J.O., et al, “Matrix correlation”, Psychometrika 49 (1984) 403-423.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 32 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
1a 2a 3a 1b 2b 3b 1c 2c 3c<br />
1a 1.00 1.00 0.99 1.00 0.96 0.99 1.00 0.98 1.00<br />
2a 0.99 1.00 1.00 1.00 1.00 1.00* 1.00* 0.99 0.99*<br />
3a 0.99 1.00 1.00 1.00 1.00 1.00 1.00* 0.99 1.00*<br />
1b 1.00 1.00 1.00* 1.00 1.00 1.00 1.00 0.99 1.00*<br />
2b 0.96 1.00 1.00 1.00 1.00 1.00 1.00 1.00* 0.99<br />
3b 0.99 1.00* 1.00 1.00 1.00 1.00 1.00* 0.99 0.99*<br />
1c 0.99 1.00* 1.00* 1.00 1.00 1.00* 1.00 1.00 0.99<br />
2c 0.97 0.99 0.99 0.99 1.00* 0.99 1.00 1.00 0.98<br />
3c 1.00 0.99* 1.00* 1.00* 0.99 0.99* 1.00 1.00 1.00<br />
* No further alignment required after baseline correction and data smoothing.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 33 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Bosdriesz - NISB - Oral<br />
Escherichia coli implements a robust regulatory network motif that maximises growth rate<br />
Evert Bosdriesz 1,2 , Douwe Molenaar 1 , Bas Teusink 1,2,3 , Frank Bruggeman 1,3,4<br />
1 Systems Bioinformatics, IBIVU, VU University, Amsterdam; 2 The Netherlands Bioinformatics Centre (NBIC);<br />
3 Netherlands Institute for Systems Biology (NISB); 4 Life Sciences, Centre for Mathematics and Computer<br />
Science (CWI)<br />
The regulation of ribosomal gene expression has been studied for half a century and the focus has been<br />
either on its role in growth rate and fitness maximisation [1-4] or on its molecular mechanisms [5-7].<br />
Attempts to reconcile both angles of research have not led to clear hypotheses for further research. It has<br />
become clear that, since the protein synthesis machinery comprises a large part of total protein in fast<br />
growing organisms, the ribosomal content has to be carefully tuned with respect to precursor producing<br />
biosynthetic pathways to achieve a maximal growth rate. Furthermore, this regulation should function in a<br />
robust manner, independent of particular catabolic and anabolic pathways and environmental conditions.<br />
Here we investigate whether knowledge about the molecular mechanism of ribosomal RNA and protein<br />
gene transcription leads to optimal growth-rate dependent control. First, we identify the general<br />
requirements for a regulatory network capable of optimally tuning ribosomal gene expression to attain the<br />
maximum growth rate of the organism under any environmental condition. We show that these<br />
requirements are met by a surprisingly simple feedback mechanism, that exploits so called ultra-sensitivity<br />
[8, 9]. This motif functions nearly perfectly over a wide range of external conditions and growth rates, and<br />
is insensitive to its own kinetic details. We show that Escherichia coli implements this optimal strategy in<br />
the ppGpp-mediated regulation of amino-acid and protein synthesis. A mathematical model of this motif<br />
behaves in agreement with several experimental observations made on the dynamics of the stringent<br />
response and the growth rate dependent regulation of ribosomal content. We propose that, despite<br />
apparent differences in molecular detail of mechanisms compared to E. coli [10], other organisms<br />
implement a similar feedback design to control ribosomal synthesis.<br />
References<br />
1. Maaloe O, Kjeldgaard NO (1966) Control of macromolecular synthesis. Microbial and Molecular Biology series.<br />
New York: W.A. Benjamin, Inc.<br />
2. Ingraham JL, Maaloe O, Neidhardt FC (1983) Growth of the bacterial cell. Sunderland, MA, USA: Sinauer<br />
Associates, Inc.<br />
3. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene<br />
expression: Origins and consequences. Science 330: 1099-1102.<br />
4. Keener J, Nomura M (1996) Regulation of ribosome synthesis. In: Neidhardt FC, editor, Es- cherichia coli and<br />
Salmonella: Cellular and Molecular Biology, Washington: ASM Press.<br />
5. Paul BJ, Ross W, Gaal T, Gourse RL (2004) rRNA Transcription in Escherichia coli. Annu Rev Genet 38: 749-70.<br />
6. Cashel M, Gentry D, Hernandez V, Vinella D (1996) The stringent response. In: Neidhardt FC, editor, Escherichia<br />
coli and Salmonella: Cellular and Molecular Biology, Washington: ASM Press, volume 264.<br />
doi:10.1007/s004380000381.<br />
7. Lemke JJ , Sanchez-Vazquez P, Burgos HL, Hedberg G, Ross W, et al. (2011) Direct regulation of Escherichia coli<br />
ribosomal protein promoters by the transcription factors ppGpp and DksA. Proc Natl Acad Sci U S A 108: 5712-<br />
5717.<br />
8. Koshland DE, Goldbeter a, Stock JB (1982) Amplification and adaptation in regulatory and sensory systems.<br />
Science (New York, NY) 217: 220-5.<br />
9. Elf J, Ehrenberg M (2005) Near-critical behavior of aminoacyl-tRNA pools in E. coli at rate- limiting supply of amino<br />
acids. Biophys J 88: 132-46.<br />
10. Kr Ì•asny Ì• L, Gourse RL (2004) An alternative strategy for bacterial ribosome synthesis: Bacillus subtilis rRNA<br />
transcription regulation. EMBO J 23: 4473-4483.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Chen – External (VU, Amsterdam) - Poster<br />
Development and Validation of the invertebrate soil quality (iSQ) Chip for Ecological Effects<br />
Assessment of Chemicals<br />
Guang-Quan Chen, Dick Roelofs, Nico van Straalen<br />
Be-Basic organisation<br />
In the last 10 years the Triad approach has shown to be effective in ecological risk assessment of polluted<br />
sites. However, some bottlenecks still remain. For example, in current ecological risk assessment it is not<br />
always clear which pollutant or other stress-inducing compound is causing negative effects, especially when<br />
a site is polluted with a cocktail of pollutants. Although the strength of the Triad is that it takes all<br />
pollutants and other stressors present at a site into account. In the case of remediation or redevelopment<br />
of contaminated land it is important to identify toxicant classes so that remediation efforts can be more<br />
effectively tailored towards biologically active compounds. Also, traditional tests can be expensive and<br />
time-consuming, although some biomarkers can resolve parts of those drawbacks. In this project we want<br />
to validate and valorise expression profiles as a biomarker for soil quality management. Transcriptomic<br />
tools are much more flexible and more comprehensive compared to classical biomarkers. To study<br />
ecological responses to soil conditions, we developed and applied an invertebrate Soil Quality (iSQ) Chip.<br />
The iSQ chip based on the Agilent platform and validated its use for soil quality testing by setting up a<br />
standardised exposure procedure for a collembolan model Folsomia candida to soil (Nota et al. 2008; Nota<br />
et al. 2009). The test will be introduced in the toxicological part of the Triad approach. It is expected that<br />
the iSQ test will play an important role in resolving some of the bottlenecks and add an enormous value to<br />
the current risk assessments by being fast and pollution-type specific. We will closely collaborate with the<br />
Bioclear, specialised in conducting the Triad approach.<br />
References<br />
Nota, B., M. Timmermans, C. Franken, K. Montagne-Wajer, J. Marien, M. E. De Boer, T. E. De Boer,<br />
B.Ylstra, N. M. Van Straalen, and D. Roelofs. Gene Expression Analysis of Collembola in Cadmium<br />
Containing Soil. Environmental Science & Technology, 42, 8152-8157. 2008.<br />
Nota, B., M. Bosse, B. Ylstra, N. M. van Straalen, and D. Roelofs. Transcriptomics reveals extensive<br />
inducible biotransformation in the soil-dwelling invertebrate Folsomia candida exposed to<br />
phenanthrene. BMC Genomics 10.2009.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Dijk - CBSG - Poster Flash<br />
Modelling the floral transition: Linking changes in gene expression with changes in floweringtime<br />
phenotype<br />
Aalt D.J. van Dijk 1,2 , F. Leal Valentim 1 , Richard G.H. Immink 1 , Simon van Mourik 2 , Roeland C.H.J. van Ham 3 ,<br />
Markus Schmid 4 , Gerco C. Angenent 1<br />
1 Wageningen UR, Plant Research International, Bioscience, Wageningen; 2 Wageningen UR, Biometris,<br />
Wageningen; 3 Keygene N.V., Wageningen; 4 Max Planck Institute for Developmental Biology, Molecular<br />
Biology, Tübingen, Germany<br />
An important characteristic of plants is their ability to control flowering-time and to flower under the<br />
optimal conditions. Arabidopsis thaliana responds to external stimuli, such as day-length, temperature and<br />
vernalisation, by changing the expression level of the so-called flowering-time genes. The transcriptional<br />
profile of these genes is controlled by a complex regulatory network that includes feedback loops and the<br />
transport of proteins from the leaves to the meristem.<br />
Here we propose a model to describe the expression of the genes in the core of the flowering-time<br />
regulatory network. The model includes genes whose expression promotes flowering, such as FT, FD, SOC1,<br />
AGL24, LFY and AP1; and also whose represses flowering, like SVP and FLC. The parameters of the model<br />
are adjusted by fitting the equations to time course of mRNA expression from different tissues: leaf and<br />
meristem.<br />
In a first analysis, the model is interrogated to predict changes in the expression level of the components in<br />
the network given knock-out mutation in the genes. The mutant simulations were validated with timecourses<br />
of mRNA expression from three mutant backgrounds: soc1, agl24 and fd. Then, by correlating the<br />
gene expression with the molecular switch to flowering, we use the model for flowering-time predictions.<br />
Flowering-time for 13 mutants was measured to assess the accuracy of the predictions. Finally, we used the<br />
model to assess the flowering-time for different protein concentrations, shedding light on the molecular<br />
mechanism underlying the adaptive response to external stimuli.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Driel - <strong>NCSB</strong> - INVITED LECTURE<br />
The MetSyn initiative: can the life sciences live up to the expectations?<br />
Roel van Driel, Bert Groen, Vitor Martins dos Santos, Natal van Riel, Ko Willems van Dijk<br />
Netherlands Consortium for Systems Biology (<strong>NCSB</strong>)<br />
Given its huge potential, life sciences should move to a next level by effectively and visibly contribute to<br />
pressing societal issues in health and economy. To be effective, research programmes should be scaled to<br />
the complexity of the system studied. The alternative is adding up information from many smaller studies,<br />
which for a number of reasons is not very successful.<br />
Back-of-the-envelope calculations indicate that a credible and effective programme, aiming at for instance<br />
comprehensively tackling metabolic syndrome, should have a volume between 100 and 1000 M€ in 10-15<br />
years, involving many research institutes and hospitals and hundreds of PIs in many countries. A simple<br />
cost-benefit analysis show that - if successful - such investment in metabolic syndrome is highly costeffective.<br />
Large-scale research programmes should be credible to society, in particular to funders and<br />
politicians, by making progress measurable and accountable and by professional management. Presently,<br />
we do not have the tools to manage such programme scientifically and therefore these must be developed.<br />
Systems biology and bioinformatics are the integrator of the highly diverse data sets into quantitative and<br />
predictive models, which are tools to make life science research goal-oriented and efficient.<br />
The <strong>NCSB</strong> Mid-term Evaluation Committee has advised us to develop an ambitious flagship project that<br />
unites Dutch expertise in an international context. The MetSyn initiative intends to develop the contours of<br />
a concrete and credible large-scale demonstration programme in the life sciences, that (i) addresses<br />
societally and economically important issues, (ii) builds on explicit and abundant Dutch expertise, and (iii)<br />
can be carried out at an international level.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Dutta - NBIC - Oral<br />
Visualise your models: pathway based visualisation for integrative systems biology<br />
Anwesha Dutta 1,2 , Martina Kutmon 1,2 , Martijn P van Iersel 3 , Chris T Evelo 1,2<br />
1 Department of Bioinformatics - BiGCaT, Maastricht University; 2 Netherlands Consortium for Systems<br />
Biology (<strong>NCSB</strong>); 3 General Bioinformatics, Reading, UK<br />
Different omics platforms—genomics, transcriptomics, proteomics, metabolomics and fluxomics—are<br />
generating new insights into how biological systems work at a molecular level. Although each individual<br />
omics approach provides a global view of a specific cellular process, this view is limited to only one aspect.<br />
In order to gain a comprehensive understanding of the system as a whole, researchers are faced with the<br />
challenge of merging these different types of results.<br />
PathVisio is an open source tool for drawing and editing biological pathways and visualising and analysing<br />
data. In order to make multi omics data visualisation more intuitive we developed new add-ons for the<br />
software to enable visualisation of multiple data sets that can be about data of different types. This also<br />
allows visualisation of data on the lines that symbolise interactions and reactions in the pathways,<br />
essentially adding edge visualisation for network biology. In this way we can for instance show<br />
results of fluxomics studies or from dynamic system biology models.<br />
For illustration of the potential of the proposed visualisation strategy and of its value for data assessment<br />
and mining we visualised data of diverse kind on fatty acid beta oxidation pathway. This example would<br />
provide a pedagogic example to researches who hope to apply the method to their own research.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Dutta - NBIC - Poster<br />
PathVisioRPC: connecting PathVisio to the world<br />
Anwesha Dutta 1,2 , Martijn P. van Iersel 3 , Martina Kutmon 1,2 , Lars Eijssen 1 , Magali Jaillard 1 , Egon L.<br />
Willighagen 1 , Chris T. Evelo 1,2<br />
1 Department of Bioinformatics - BiGCaT, Maastricht University; 2 Netherlands Consortium for Systems<br />
Biology (<strong>NCSB</strong>); 3 General Bioinformatics, Reading, UK<br />
PathVisioRPC is an XML-RPC interface of PathVisio [1] which allows users to access PathVisio functionality<br />
without leaving the programming environment they are familiar with. The application allows users to call<br />
functions of PathVisio to create new pathways, modify existing pathways, visualise their data on the<br />
pathways and perform pathway statistics, all from within their scripting language of choice (such as C,<br />
Python, Perl, PHP, Ruby, R etc.).<br />
The function calls are made using an XML-RPC server therefore the programming language chosen for<br />
making the function calls is irrelevant as long as it has an XML-RPC client implementation. XML-RPC is a<br />
remote procedure call (RPC) protocol which uses XML to encode its calls and HTTP as a transport<br />
mechanism[2].Most major programming languages (Java, C, Python, Perl, PHP, Ruby, R etc.) have such an<br />
implementation.<br />
Implementation<br />
The application consists of the following components:<br />
• PathVisio and its libraries.<br />
• Handler classes: classes which provide abstract functions to PathVisio. Their functionality consists of<br />
translating basic data types such as numbers and strings that can be handled by XML-RPC into PathVisio<br />
objects.<br />
• CallHandler class: class which provides all the functions that are made available through the external<br />
interface. All calls are redirected to the appropriate functions in the handler classes.<br />
• XML-RPC: the Apache library providing the XML-RPC server.<br />
• External clients: client code which directly calls or provides abstract methods for working with PathVisio.<br />
Example use case<br />
Data from high-throughput experiments such as microarrays can be combined with pathways to achieve<br />
new insights. Using pathways one can view data in its biological context rather than in an arbitrarily<br />
ordered table. Researchers in the life sciences often use scripting languages such as R to process their data,<br />
perform statistical analysis on the gene/protein expression datasets and then visualise the differentially<br />
expressed genes/proteins on pathways using PathVisio. PathVisioRPC can provide an easy solution there by<br />
allowing PathVisio to be incorporated into the analysis pipeline resulting in saving time and effort which<br />
would have been otherwise lost in solving data compatibility issues.<br />
References<br />
1. Presenting and exploring biological pathways with PathVisio. van Iersel MP, Kelder T, Pico AR, Hanspers<br />
K, Coort S, Conklin BR, Evelo C. BMC Bioinformatics 2008, 9:399 (25 Sep 2008).<br />
2. Programming Web Services with XML-RPC. Simon St. Laurent, Joe Johnston, Edd Dumbill. (June 2001)<br />
O'Reilly. First Edition.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Ercan - TIFN - Poster<br />
Quantitative Physiology of Lactococcus lactis at Zero-Growth Conditions<br />
O. Ercan 1,2,3,6 , E.J. Smid 1,5 , M. Kleerebezem 1,3,4<br />
1 Top Institute Food and Nutrition (TIFN), P.O. Box 557, 6709 PA Wageningen; 2 Laboratory of Microbiology,<br />
Wageningen University, Wageningen; 3 NIZO Food Research BV, P.O. Box 20, 6710 BA Ede; 4 Host-Microbe<br />
Interactomics, Wageningen University, Wageningen; 5 Laboratory of Food Microbiology, Wageningen<br />
University, Wageningen; 6 Kluyver Centre for Genomics of Industrial Fermentation, P.O. Box 5057, 2600 GA<br />
Delft, The Netherlands.<br />
In natural environments, due to variable-nutrients availability, micro-organisms live in feast and famine<br />
existence, with famine as the prevalent state. When conditions are favourable, carbon and energy sources<br />
are first consumed for growth-associated processes. Under conditions with limited nutrient supply, most<br />
metabolic energy is diverted to maintenance instead of growth. Also under industrial fermentation<br />
conditions, micro-organisms may experience long periods of extremely low nutrient availability. For<br />
example, lactic acid bacteria have strongly restricted access to nutrients during the cheese ripening<br />
process. Survival under these conditions requires adaptations of cellular metabolism, and coincides with<br />
extremely slow or no-growth of the micro-organisms. Zero-growth is defined as a metabolically active, nongrowing<br />
state of a micro-organism in which product-formation capability is maintained and thereby is<br />
principally different from starvation.<br />
Retentostat cultivation system has been designed to simulate zero-growth conditions. Retentostat<br />
cultivation is a modification of chemostat cultivation in which the growth limiting carbon source is fed at a<br />
constant rate, while biomass is retained in the bioreactor by a retention filter-probe in the effluent line.<br />
Prolonged retentostat cultivation leads to growth rate that approximate zero while the rate of energy<br />
transduction equals the maintenance energy requirement. The aim of this project is to quantitatively<br />
investigate zero-growth physiology of the plant-derived lactic acid bacterium, Lactococcus lactis.<br />
After cultivating of L. lactis at extremely low growth rates in glucose-limited retentostat conditions, cell<br />
physiology, metabolic profile, and robustness of the strain were investigated. Moreover, energetic<br />
parameters, substrate- and energy-related maintenance coefficients and biomass yields were calculated<br />
from the retentostat cultures. Specific growth rates decreased to 0.0001 h -1 after 42 days, while doubling<br />
times increased to over 260 days for two independent cultures. In addition to this, viability of the overall<br />
culture was above 95%, as assayed with the LIVE/DEAD BacLight kit using FACS. While both fermentations<br />
displayed very similar end-product profiles, there were two metabolic shifts between homo-lactic and mixacid<br />
fermentation patterns during the retentostat cultivation. The biomass concentrations were accurately<br />
predicted by a maintenance coefficient of 1.1 mmol of carbon g -1 of biomass h -1 calculated from retentostat<br />
cultivations.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Eunen - TIFN - Poster<br />
Revealing the mechanism by which microbial metabolites protect against obesity<br />
Karen van Eunen 1,3,4,5 , Gijs den Besten 1,3,4 , Christian A. Tiemann 2,4 , Dirk-Jan Reijngoud 1,3,4 , Natal A. van<br />
Riel 2,4 , Bert K. Groen 1,3,4,5 , Barbara M. Bakker 1,3,4,5<br />
1 Department of Pediatric, Center for Liver, Digestive and Metabolic Diseases, University of Groningen,<br />
University Medical Centre Groningen, Groningen; 2 Department of BioMedical Engineering, Eindhoven<br />
University of Technology, Eindhoven; 3 Systems Biology Center for Energy Metabolism and Aging, University<br />
of Groningen, Groningen; 4 Netherlands Consortium for Systems Biology, Amsterdam; 5 Top Institute for Food<br />
and Nutrition, Wageningen, The Netherlands<br />
The major end products of bacterial metabolism in the mammalian large intestine are the short-chain fatty<br />
acids (SCFAs). The dominant SCFAs involved in the physiology are the straight-chain fatty acids butyrate<br />
(C4), propionate (C3) and acetate (C2). It is believed that there is a relation between the microbial activities,<br />
the SCFAs and the development of obesity (Schwiertz et al., Obesity, 2010). In mice, we have seen that the<br />
addition of either one of the short-chain fatty acids resulted in retaining a lower body weight on a high-fat<br />
diet. Furthermore the SCFA mice perform better in insulin and glucose tolerance tests and show lower liver<br />
triglyceride levels.<br />
In order to reveal the underlying mechanisms of these observed changes, we have applied a mathematical<br />
modelling methodology developed by Tiemann et al. (BMC Syst Biol, 2011). Based on measured fluxes and<br />
metabolite concentrations the methodology identifies the essential parameter adaptations to describe the<br />
transition to the ‘new’ phenotype. Such adaptations reflect the changes in the molecular processes in the<br />
underlying biological network. Since the observed changes in the experimental data were very similar for<br />
the different short-chain fatty acids, we focused so far on the comparison between the high-fat diet with<br />
and without butyrate.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Fleck – External (WCSB, Wageningen) - Poster<br />
Design of light perception<br />
Christian Fleck<br />
Laboratory for Systems and Synthetic Biology, Microbiology, Wageningen University & Research Centre<br />
In order to detect light, humans and animals have light-sensitive proteins in the sensory cells of the retina.<br />
Analogously, plants too have light-sensitive proteins known as photoreceptors for detecting changes in<br />
their light environment. Phytochromes are photoreceptors that are activated by red light and are therefore<br />
optimally able to detect the red part of light. But plants also use phytochromes to detect far-red light,<br />
although their photophysical properties make them ill-suited to do so. By combining experimental<br />
approaches with mathematical modelling we found an explanation for this paradox of which scientists have<br />
long been aware. We demonstrate that light perception is a systems property rather than being solely<br />
defined by the molecular property of the photoreceptor. Moreover, we show how the unique features of<br />
the phytochromes can be exploited to synthetically construct light perception networks, which are tailored<br />
for a specific wavelength.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Girolami - External (London, UK) - KEYNOTE<br />
Thomas Bayes FRS: the 17 th century English parson that assists with modern day systems biology<br />
Mark A. Girolami<br />
Department of Statistical Science, University College London (UCL), UK<br />
Hypothesis driven research is the corner stone of all the sciences. The complexity of living systems and the<br />
dynamical processes that govern their development, regulation and evolution is simply staggering and yet<br />
great strides are being made in peeking into the mysteries of cellular control. The formalisation of<br />
hypothesis driven investigation in the field of systems biology is based upon the development of abstracted<br />
mathematical models and this has a long history dating back to the likes of Hodgkin, Fisher, Huxley, and<br />
Katz. However reasoning under the levels of uncertainty inherent in the biological sciences must be<br />
addressed in as formal a manner as the development of the mathematical models. The Bayesian approach<br />
to statistical inference and evidence based reasoning is a formal codification and probability calculus for the<br />
scientific method. In this talk I will describe how this framework conceptually should be an integral part of<br />
the scientific reasoning process within systems biology and demonstrate the application of the approach to<br />
an ongoing systems-based study of cell-signalling.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Heemskerk - CMSB - Poster<br />
Niacin induces insulin resistance and enhances beta2-adrenergic sensitivity in ApoE*3-<br />
Leiden/CETP mice<br />
M van Heemskerk, S van den Berg, V van Harmelen, J van Klinken, M Boon, B Guigas, A Pronk, H Dharuri, P<br />
Rensen, K Willems van Dijk<br />
Dept. of Human Genetics, Molecular Cell biology, Endocrinology and Metabolic Diseases, General Internal<br />
Medicine, Leiden University Medical Centre, Leiden, The Netherlands<br />
Introduction<br />
Niacin (nicotinic acid) potently improves circulating lipid profiles and reduces atherosclerosis. However,<br />
niacin also induces insulin resistance in liver, muscle and adipose tissue.<br />
Aim<br />
Here, we studied the mechanism of niacin induced changes in whole body and adipocyte-specific insulin<br />
sensitivity.<br />
Methods<br />
Female ApoE*3-Leiden/CETP mice received niacin (0.3% w/w) in the food. Intraperitoneal insulin and<br />
terbutaline tolerance tests were performed. Subsequently, adipocytes were isolated and tested for changes<br />
in morphometry, and their lipolytic responsiveness to a variety of stimuli/repressors was assessed.<br />
Results<br />
Treatment with niacin resulted in impaired insulin tolerance, characterised by elevated fasting plasma<br />
glucose and insulin levels in vivo. In addition, niacin treatment resulted in enhanced beta2-adrenergic<br />
stimulation by terbutaline of glucose production independent of changes in circulating insulin levels. Ex vivo<br />
isolated adipocytes showed no changes in morphometry. Adipocytes isolated from control animals<br />
subjected acutely to niacin were found to be less responsive to lipolytic repression by insulin, which was<br />
further aggravated in adipocytes from chronic niacin treated animals. In accordance with whole body data,<br />
adipocytes chronically subjected to niacin were more sensitive to lipolytic stimulation by terbutaline. qPCR<br />
analysis of adipose tissue indicated very significant down regulation of both the insulin (IR, IRS1, PDE3B)<br />
and beta-adrenergic pathways (Beta1,2,3 AR, Beta-arrestin1 and 2), indicating vast changes in lipolysis<br />
regulation due to niacin. Adipocytes from niacin treated animals also showed a decreased response to<br />
PDE3B (phosphodiesterase 3B) inhibition, in line with its decreased gene expression. As beta2-adrenergic<br />
signalling also relies on PDE3B for negative feedback, the finding that PDE3B expression correlates very well<br />
with both adipocyte insulin resistance and beta2-adrenergi c sensitivity, decreased PDE3B could explain<br />
both phenomena.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Hendrickx – External (UvA, Amsterdam) - Poster Flash<br />
Exploring cellular decision-making principles by time-resolved metabolomics<br />
Diana M. Hendrickx a,b,c , Huub C.J. Hoefsloot a,c , Margriet M.W.B. Hendriks c,d , Andre B. Canelas e,f , Age K.<br />
Smilde a,c<br />
a Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam; b Department<br />
of Metabolic Diseases, University Medical Centre Utrecht; c Netherlands Metabolomics Centre, Leiden;<br />
d Analytical Biosciences, Leiden/Amsterdam Center for Drug Research, Faculty of Science, Leiden University;<br />
e Kluyver Centre for Genomics of Industrial Fermentation, Biotechnology Department, Delft University of<br />
Technology; f Systems Bioinformatics, Centre for Integrative Bioinformatics, VU University Amsterdam<br />
In living organisms, cells adapt to changes in their environment by means of cellular decision-making<br />
systems. While adapting to the new conditions, cells have to be robust to maintain certain essential<br />
functions for survival. Elucidating the underlying principles of robustness and cellular decision-making is<br />
one of the major challenges in systems biology, because it can contribute to different disciplines, such as<br />
microbiology, plant biology and medicine, by discovering unknown principles of cellular functioning.<br />
Cells are known to be evolved to be optimal against perturbations by changing their reaction rate<br />
distribution. Exploring what the system has optimised can elucidate information about the principles<br />
underlying cellular decision making and robustness.<br />
One of the approaches to calculate reaction rate profiles is dynamic flux balance analysis (DFBA). DFBA is<br />
based on only stoichiometry and reaction rate constraints and can be applied without detailed knowledge<br />
of kinetics.<br />
In this study, the outcome of different optimisation principles is studied by combining DFBA with timeresolved<br />
metabolomics data. In standard DFBA, both reaction and concentration profiles are unknown.<br />
Incorporating quantitative profiles of metabolite concentrations reduces the solution space of the DFBA.<br />
The results of the DFBA are compared with literature to make conclusions which optimisation principles<br />
result in biologically meaningful reaction profiles.<br />
The method was illustrated with experimental data, namely quantitative metabolite profiles on 5 external<br />
and 28 internal metabolites of the central carbon metabolism of S.cerevisiae under a glucose pulse. The<br />
results show that DFBA combined with time-resolved data is an efficient method to test hypotheses about<br />
what the cell optimizes and, as a consequence, about principles of cellular decision-making and robustness.<br />
Acknowledgement<br />
This project was financed by the Netherlands Metabolomics Centre (NMC) which is part of the Netherlands<br />
Genomics Initiative / Netherlands Organization for Scientific Research.<br />
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Hettling - NBIC - Poster<br />
Estimating Confidence Regions of Tricarboxylic Acid Cycle Fluxes in Heart Tissue from Noisy NMR<br />
Data<br />
Hannes Hettling 1 , David Alders 2 , Johan Groeneveld 3 , Jaap Heringa 1 , Thomas Binsl 4 , and Johannes van Beek 1,5<br />
1 Centre for Integrative Bioinformatics, VU University, Amsterdam; 2 Leiden University Medical Centre, Leiden;<br />
3 Erasmus Medical Centre, Rotterdam; 4 Crosslinks BV, Rotterdam; 5 VU University Medical Centre, Amsterdam<br />
Measuring central carbon metabolism is essential for understanding cardiac energetics in vivo. Here we<br />
present a method for the analysis of noisy NMR measurements in single tissue biopsies from porcine hearts<br />
with a mathematical model of the TCA cycle. Reaction fluxes are estimated by parameter optimisation in<br />
combination with extensive Markov chain Monte Carlo sampling of feasible combinations of metabolic flux<br />
values that can describe the measured NMR data.<br />
Isotopically enriched intermediates were measured in small tissue biopsies taken at a single time point<br />
after the timed infusion of 13C labelled substrates for the TCA cycle. The carbon labelling patterns in the<br />
metabolic intermediates are then determined by NMR and analysed with a mathematical model which<br />
describes the transitions of the carbon atoms in the reactions of the TCA cycle [1]. The dynamic behaviour<br />
of the model depends on the values of four flux parameters, which are optimised to describe the<br />
isotopomer distributions derived from the NMR measurements. For model simulation and parameter<br />
estimation we use the R-package FluxEs, which was developed in our group [1]. A major challenge in the<br />
accurate quantification of metabolic fluxes is the relatively high noise level in the data, since (i) NMR<br />
spectra for small tissue samples at one single point in time are intrinsically very noisy and (ii) samples were<br />
taken from different regions in the heart. To account for the noise and for the spatial heterogeneity in the<br />
data, we do not merely rely on a single best fit of the flux parameters of the model to the data points.<br />
Instead, we sample ensembles of different parameter configurations using the Metropolis-Hastings<br />
algorithm. This procedure allows us to define confidence bounds on the estimated flux values for the TCA<br />
cycle, taking the nonlinearity of parameter interdependencies into account. Also, we can incorporate prior<br />
knowledge on single flux parameter values derived from the literature into the analysis.<br />
To validate our method, we calculate myocardial oxygen consumption from the sampled ensembles of TCA<br />
cycle flux parameters and compare these values to in vivo blood gas measurements for six different<br />
experimental conditions including basal state, cardiac stress induced by stenosis of the coronary vessels<br />
and administration of dobutamine or adenosine. Despite the high noise level in the NMR data, the average<br />
oxygen consumption in the tissue samples agrees with oxygen uptake measurements in the whole heart.<br />
With our method, we correctly predict a lower TCA cycle flux and a higher anaplerotic flux for ischemic<br />
hearts in comparison to the control condition whereas cardiac stress induced by dobutamine leads to a<br />
higher TCA cycle flux. In conclusion, the TCA cycle flux and its 95% confidence interval can be estimated in<br />
single cardiac biopsies.<br />
Reference<br />
1. Binsl et al, 2010, Bioinformatics 26(5), 653-660<br />
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Horst - NBIC - Poster<br />
SKOtch: a SKOS vocabulary editor<br />
Andrew P. Gibson, Eelke van der Horst, Jack W. Vink, Gerbert A. Jansen, Serge Barth, Koen W. A. van<br />
Grinsven, Albert A. de Graaf, Varshna Goelela, Jildau Bouwman, Suzan Wopereis, Marijana Radonjic, Ben<br />
van Ommen, Antoine H. C. van Kampen<br />
Bioinformatics Laboratory, Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center,<br />
1105AZ Meibergdreef 9, Amsterdam; Systems Bioinformatics/Molecular Cell Physiology, VU University<br />
Amsterdam, De Boelelaan 1085, 1081HV, Amsterdam; TNO Quality of Life, Business Unit Biosciences, TNO,<br />
Zeist; Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Science<br />
Park 904, 1098XH Amsterdam; Netherlands Consortium for Systems Biology, University of Amsterdam, PO<br />
Box 94215, 1090 GE, Amsterdam; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA,<br />
Nijmegen, the Netherlands.<br />
Systems biology aims to understand complex biological systems by studying a large number of components,<br />
such as (bio)molecules, cells, and organisms, and their interactions. This, together with the data intensive<br />
experiments that are conducted, requires a new infrastructure for handling the large amount of knowledge<br />
that is involved. Knowledge bases are management systems for knowledge that facilitate storage,<br />
organisation, querying, and reasoning of (scientific) knowledge. For the construction of a knowledge base, a<br />
vocabulary is needed that controls the terms, or concepts, that are mentioned in the knowledge base. SKOS<br />
(Simple Knowledge Organisation System) is a system that facilitates creation of controlled vocabularies<br />
within the framework of the Semantic Web. It is centered around the idea of 'Concepts' that are organised<br />
under one or more 'Concept Schemes'. Concepts can be related to other concepts through semantic<br />
relations. In this work, an editor was developed that facilitates the creation and maintenance of online<br />
SKOS vocabularies. Concepts and schemes are easily created and made available on the Semantic web,<br />
without exposing the user to the underlying details of the SKOS specification and repository management.<br />
It allows knowledge engineers to attach several kinds of labels, definitions, and notes to concepts that aid<br />
the editorial and documentation process. In addition, users are able to create both hierarchical<br />
(broader/narrower) and symmetric semantic relations between concepts, and to create links to external<br />
resources, such as other vocabularies or webpages. This editor is now being used as part of several<br />
collaborative projects focusing on construction of knowledge bases.<br />
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Hugenholtz - TIFN – Poster<br />
Fiber screening study: how do fibers change gut microbiota and their short chain fatty acid<br />
production?<br />
F. Hugenholtz, K. Lange, G. Hooiveld, M. Kleerebezem, H. Smidt<br />
Top Institute Food and Nutrition, PO Box 557, 6700 AN, Wageningen; Laboratory of Microbiology,<br />
Agrotechnology and Food Sciences, Wageningen University, Dreijenplein 10, 6703 HB, Wageningen;<br />
Netherlands Consortium for Systems Biology, c/o NISB Bureau, University of Amsterdam, Science Park 904,<br />
1098 XH, Amsterdam; Nutrition, metabolism, and Genomics Group, Division of Human nutrition,<br />
Wageningen University; Host-Microbe Interactomics, Wageningen University, de Elst 1, 6708 WD<br />
Wageningen, the Netherlands<br />
The microbiota of the gastrointestinal tract plays a key role in the digestion of our food. Complex metabolic<br />
networks of interacting microbes in the gastrointestinal tract of humans and other mammals yield a wide<br />
range of metabolites of which the short chain fatty acids, in particular butyrate, acetate, and propionate<br />
are the most abundant products of carbohydrate fermentation. In addition to complex carbohydrates,<br />
amino acids derived from dietary proteins can also serve as substrates for short chain fatty acid formation,<br />
leading to expansion of the fermentation end-product palet that includes branched-chain fatty acids. So far<br />
metabolic networks were documented in in vitro models. In this project interactions between diet,<br />
microbiota and host will be quantitatively studied and subsequently modelled using a Systems Biology<br />
approach. In initial experiments the cecum and colon of conventionally raised mice on different fibre diets<br />
are analysed. Determinations of the microbiota composition using phylogenetic microarray technology will<br />
be complemented with metatranscriptome and metabolome analyses to obtain microbiota metadata that<br />
can be modelled in relation to the dietary regimes. The metadata obtained will be employed for integrated<br />
modelling that will also include host derived transcriptome data, which is expected to result in refinement<br />
of our understanding of the interactions between diet, microbiota and host.<br />
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Janssens - CMSB - Poster<br />
In vivo magnetic resonance spectroscopy of lipid handling in steatotic rat liver<br />
Sharon Janssens 1 , Richard A. Jonkers 1 , Mattijs Heemskerk 2 , Sjoerd A. van den Berg 2 , Ko Willems van Dijk 2 ,<br />
Natal A. van Riel 1 , Klaas Nicolay 1 , Jeanine J. Prompers 1<br />
1 Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven; 2 Department of<br />
Human Genetics, Leiden University Medical Centre, Leiden<br />
Background<br />
Hepatic steatosis is the abnormal and excessive accumulation of triglycerides in the liver and a histological<br />
hallmark of non-alcoholic fatty liver disease (NAFLD). It is the most common liver disorder in Western<br />
countries and it is linked to obesity and type 2 diabetes. The aim of this study was to determine differences<br />
in lipid metabolism in steatotic livers of rats on different high-fat diets using 1H-[13C] magnetic resonance<br />
spectroscopy (MRS) together with the oral administration of 13C-labeled lipids.<br />
Methods<br />
27 male Wistar rats (11 weeks old) were divided into 3 diet groups: low-fat (10% of calories from fat, CON),<br />
high-fat palm oil (45% fat, HFP), and high-fat lard (45% fat, HFL). They received the diet for 10 weeks after<br />
which MRS experiments were performed at baseline (BL), and 4h and 24h after oral administration of 1.5g<br />
[U-13C] Algal lipid mixture per kg body weight.<br />
Results<br />
At baseline, intracellular hepatic lipid (IHCL) content was higher in the two high-fat diet groups (HFP 5.58 ±<br />
0.43%; HFL 4.50 ± 0.32%) compared to the control group (1.33 ± 0.08%). Four hours after administration of<br />
13C-labeled lipids, 13C enrichment of IHCL was lower in the HFP group (0.026 ± 0.005%) compared to CON<br />
(0.065 ± 0.010%) while there was no difference between CON and HFL (0.052 ± 0.012%). At 24h, 13C<br />
enrichment of IHCL was increased in the HFP group compared to 4h (0.095 ± 0.015%), while there was no<br />
change in the HFL group (0.066 ± 0.013%) and a decrease in the CON group (0.023 ± 0.004%).<br />
Conclusion<br />
The animals on the high-fat diets developed steatotic livers. However, this was not accompanied by<br />
increased lipid uptake in the early postprandial phase. Instead, postprandial lipid uptake seemed to be<br />
prolonged and/or lipid turnover was decreased in livers of high-fat diet fed animals.<br />
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Khandelwal - NISB – Poster Flash<br />
Predicting fluxes in microbial communities through physiological properties of participating<br />
microbes<br />
Ruchir Khandelwal, Brett G. Olivier, Wilfred F.M. Röling, Bas Teusink, Frank J. Bruggeman<br />
VU University Amsterdam<br />
Functioning of any microbial community generally results from the interactions between various functional<br />
groups of micro-organisms. We aim to establish a generalised mathematical modelling method for<br />
analytically predicting the properties of microbial communities on basis of the physiological properties of<br />
the individual member organisms. In this post genome era, metabolism of micro-organisms can be<br />
modelled on basis of detailed metabolic networks generated from the whole genome data available.<br />
However, we describe the microbial metabolism using only measurable fluxes of growth, product<br />
formation, oxygen uptake, and substrate consumption etc. Such Reduced Stoichiometric Metabolic<br />
descriptions of the member organisms are computationally less cumbersome to handle and help us<br />
determine the system-wide flux distribution of shared compounds in their environment.<br />
A Stoichiometric Network Analysis method has been established for two organisms co-existing under<br />
obligatory mutualistic relationship where one organism uses the product of the other organism and vice<br />
versa. Reduced Stoichiometric Metabolic Descriptions were used towards predicting growth rates,<br />
substrate uptake and product formation fluxes of these organisms in co-existence. These solutions were<br />
confirmed through traditional Linear Programming methods such as Flux Balance Analysis.<br />
The virtue of being less cumbersome in modelling of such reduced but confined descriptions of the<br />
metabolism of organisms leads a new way for taking modelling to the ecological level, where one does not<br />
only model and predict the behaviour of single organism in the context of its complex environment, but<br />
wants to model a whole ecosystem, consisting of up to thousands of species with variable coarse graining<br />
of the organismal metabolism.<br />
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Klinken - CMSB - Oral<br />
A systems biology approach for enriching genetic association studies of metabolite profiles with<br />
prior pathway knowledge<br />
J.B. van Klinken 1 , H.K. Dharuri 1 , P. Henneman 1 , C.M. van Duijn 2 , P.A.C. 't Hoen 1 , K. Willems van Dijk 1,3<br />
1 Center for Human and Clinical Genetics, Leiden University Medical Centre, Leiden; 2 Department of<br />
Epidemiology, Erasmus Medical Centre, Rotterdam; 3 Department of Internal Medicine, Leiden University<br />
Medical Centre, Leiden, The Netherlands<br />
Genome-Wide Association (GWA) studies have recently focused on intermediate phenotypes such as blood<br />
metabolite levels in order to dissect complex traits. These studies have led to the discovery of several novel<br />
loci, but they have thus far been relatively unsuccessful in providing insights in the etiology of complex<br />
metabolic disorders. To this end, pathway-based methods have been proposed as a promising approach for<br />
the functional analysis and interpretation of GWA study results. Typically, these methods are based on<br />
gene set enrichment analysis (GSEA), where gene sets are defined by annotated pathways from<br />
biochemical databases such as KEGG and BioCyc or by Gene Ontology terms. The main weakness of this<br />
approach, however, is that it is biased towards traditional pathway definitions and does not take into<br />
account the complex interactions that are present in metabolic networks. Here, we propose an alternative<br />
approach for defining gene sets of metabolic pathways that i s based on the topology of metabolic<br />
networks, which is essentially independent of existing pathway annotations and literature bias.<br />
We propose to define gene sets based on the concept Elementary Flux Modes (EFMs) and evaluate their<br />
enrichment in a GWA study of serum metabolite concentrations. EFMs correspond to the minimal sets of<br />
reactions through which a non-zero flux can occur at steady state, and therefore provide a natural and<br />
unbiased definition of a metabolic pathway. Our approach consists of calculating the EFMs in the genomescale<br />
stoichiometric model of the human hepatocyte from Gille et al. (Mol. Sys. Biol. 2010), that can either<br />
produce or degrade each of the measured metabolites. Next, GSEA is performed based on the GWA study<br />
results, where the gene sets are defined as the sets of enzymatic reactions that participate in the EFMs.<br />
Significantly enriched EFMs then represent pathways that carry a steady state flux that has a relatively large<br />
influence on the given blood metabolite concentration. Subsequently, these EFMs are exported for<br />
visualisation in Cytoscape. In order to contain the combinatorial explosion that occurs when calculating the<br />
EFMs in genome-scale networks, we do not enforce the steady state constraint for a list of common hub<br />
and currency metabolites (ATP, NADH, acetyl-CoA, etc.). A fully automated approach for identifying these<br />
metabolites is being investigated. We are currently validating our approach on a set of fasting serum<br />
metabolite concentrations, amongst which several sugars, amino acids, lipids and ketone bodies, that have<br />
been determined in individuals from the Erasmus Rucphen Family (ERF) cohort on two metabolomic<br />
platforms (LC-MS and 1H-NMR). In addition, we are investigating the applicability of our method to the<br />
analysis of gene expression data.<br />
In conclusion, we present a novel strategy to analyse metabolomics data in a genome-wide setting using a<br />
Systems Biology approach, which has the potential to identify novel loci and to provide new etiological<br />
insights in GWA studies of metabolomic phenotypic traits.<br />
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Kuipers - KC - KEYNOTE<br />
Highly modified peptides by synthetic biology approaches<br />
Oscar P. Kuipers, Auke van Heel, Dongdong Mu, Liang Zhou, Andrius Buyvidas, Manuel Montalban-Lopez<br />
Department of Molecular Genetics, University of Groningen<br />
Antibiotic resistance in human pathogens is on the rise and this rise is not met with new approved<br />
antibiotics to combat these resistant bacteria. To solve this growing problem of resistance, alternative<br />
sources of antibiotics should be explored. One of these sources could be the class of ribosomally<br />
synthesised, post-translationally modified peptides called lantibiotics. Lantibiotics are well studied peptides<br />
which are stabilised by their characteristic rings formed with lanthionine or methyllanthione residues out of<br />
dehydrated Ser- and Thr-residues. We’ll describe four different approaches to develop novel antimicrobials:<br />
1. Developing a synthetic biology approach to efficiently use the large amount of sequenced genomes<br />
(>3000) as a resource for novel lantibiotics. These putative lantibiotics can be used in an L. lactis plug-andplay<br />
production- and modification system containing the modification enzymes NisBC of the model<br />
lantibiotic nisin which have a relaxed substrate specificity. Here we present the results of the first 25<br />
candidates.<br />
2. Use of heterologously expressed post-translational modification enzymes to hyper modify lantibiotics.<br />
Various documented posttranslational modifications are believed to be important for activity. For instance,<br />
L-Serine can be converted to D-Alanine with 2,3-didehydroalanine as an intermediate. Moreover, an S-<br />
aminovinyl-D-cysteine (AviCys) ring can be formed when a C-terminal Cysteine is present in lantibiotics like<br />
gallidermin. We try to extend the available number of lantibiotic modification enzymes using the system<br />
containing NisB, NisC and NisT by introducing the modification enzyme LtnJ, responsible for D-alanine<br />
formation in lacticin 3147, using nisinleader-peptide fusions as substrate, to convert the available<br />
dehydroalanines into D-alanines. Moreover GdmD, a modification enzyme demonstrated to create an S-<br />
aminovinyl-D-cysteine (AviCys) in gallidermin, is also selected to modify pregallidermin and other<br />
lantibiotics. By mass spectrometry, it is shown that these lantibiotics can be modified by GdmD in vivo. This<br />
result broadens the array of lantibiotics which can be generated in Lactococcus lactis.<br />
3. Design and production of novel lantibiotics by ring module- and hinge-variation. We have analysed the<br />
structure, stability, and potency of diverse known lantibiotics in order to select appropriate modules. These<br />
modules will be randomly fitted in a defined architecture, that of nisin, and the nisin induction and<br />
modification machinery will be exploited for the required modifications. The screening of more than 10,000<br />
chimeric molecules for biological activity is expected to render more active antimicrobials, which will be<br />
submitted to a second engineering step to improve their activity, stability and spectrum. Active molecules<br />
will be subjected to a second round of selection after random and directed mutagenesis. This second<br />
library, even larger than the first one, can generate improved molecules. We expect not only to obtain<br />
novel potent molecules, but also to gain insight on the modularity of lantibiotics and their structure-activity<br />
relationship.<br />
4. Biomodules for introducing various types of circular and heterocyclic modifications in lantibiotics.<br />
Purpose is to design three unique biomodules that introduce heterocyclic, cyclic and circular (head-to-tail)<br />
modifications in lantibiotics (already containing lanthionine rings). For this purpose we will functionally<br />
combine posttranslational modification systems of the E. coli microcins B17 (MccB17) and J25 (MccJ25) and<br />
the Clostridial circular bacteriocin circularin A, respectively, with the NisBTC machinery and express them in<br />
the heterologous host Lactococcus lactis. Model peptides harbouring chimeric leaders will be designed for<br />
each of the biomodules. Implementing a system to make additional unique modifications and increasing<br />
the repertoire of unusual amino ac ids used in biosynthetic peptides, will give unprecedented possibilities<br />
to create peptides with improved bioactivities and chemico-physical properties also outside the<br />
antimicrobial field.<br />
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Kutmon - NBIC - Poster<br />
From biological pathways to regulatory interaction networks<br />
M. Kutmon 1 , T. Kelder 2 , P. Mandaviya 1 , N. Douaillin 1 , C.T. Evelo 1 , S.L. Coort 1<br />
1 Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht; 2 TNO, Research Group<br />
Microbiology & Systems Biology, Zeist, The Netherlands<br />
Pathway and network analysis can help to interpret large omics datasets, by providing insight in how<br />
biological processes are affected. However, often not all possible regulatory interactions, e.g. microRNAtarget<br />
interactions, are included in biological pathways to keep them clear, readable, and focused on a<br />
specific context. On one hand, adding all possible regulatory interactions to a pathway would reduce<br />
readability. On the other hand, leaving out this information could limit the ability to discover novel findings.<br />
To allow switching between these two options, we developed a Cytoscape plug-in, CyTargetLinker, that<br />
dynamically adds regulatory interactions to biological networks to allow their inclusion into pathway and<br />
network analysis. CyTargetLinker can add several different types of regulatory interactions from different<br />
resources, such as miRTarBase for miRNA-target interactions or TF Encyclopedia for transcription factorgene<br />
interactions.<br />
The plug-in is generic and can be used with any type of regulatory interactions. It is possible to extend a<br />
biological network with miRNA and transcription factors from multiple sources in one step. Researchers can<br />
then visualise their expression data for mRNA and miRNA together in the network. We also use the<br />
interaction data from the STITCH database to show interactions between chemicals and proteins. That will<br />
assist researchers in the identification of drug targets in a specific pathway. Another application of the plugin<br />
could be to show the disease, ontological or pathway associations of the genes in a biological network.<br />
The plug-in can use this information and enrich a biological network with disease associations.<br />
The integration of regulatory interactions in pathway and network analysis is crucial to gain better insights<br />
in the regulation of biological processes. The CyTargetLinker plug-in in Cytoscape helps researchers to build<br />
enriched networks to better understand their experimental results and to get a better overview of the<br />
regulation of biological processes.<br />
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Lam - External (WCSB, Wageningen) - Poster Flash<br />
A systems biology approach to decipher the interactions in a microbial community during<br />
conversion of toxic 4-chlorosalicylate into potentially useful metabolites<br />
Lam, C.M.C. 1,3 , Gabdoulline, R. 2 , Bobadilla Fazzini, R.A. 3 , Martins dos Santos, V.A.P. 1<br />
1 Systems and Synthetic Biology, Dreijenplein 10, Building 316, 6703 HB Wageningen, The Netherlands;<br />
2 BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbuettel, Germany; 3 Systems and Synthetic<br />
Biology, Helmholtz Centre for Infection Research, Inhoffentrasse 7, D-38124 Braunschweig, Germany.<br />
Chlorinated aromatic compounds from industrial wastes are harmful to the environment, but some<br />
bacteria are capable of converting them into useful metabolites and biomass. Pseudomonas reinekei and<br />
Achromobacter spanius form a synergistic community which is able to utilise chlorinated aromatics. In this<br />
study, we adopted a systems biology approach to understand the interactions between the two species<br />
through an integration of proteomic analysis and modelling of the central metabolic fluxes and dynamic<br />
enzymatic and growth kinetics when the bacteria were grown on 4-chlorisalicylate in batch and chemostat.<br />
Among those roughly a hundred differentially expressed proteins, about 20% are estimated using a<br />
constraint-based stoichiometric metabolic model of P. reinekei to be essential for cell growth, however only<br />
a few enzymes showed a direct correlation with growth rate. The dynamic model of growth of P. reinekei<br />
and A. spanius with 4-chlorosalicylate as the single carbon source indicated influence from a metabolic byproduct<br />
which affects the optimal levels of an upstream enzyme in the degradation pathway. The insights<br />
gained from this work have provided a foundation for engineering the microbial community to enhance its<br />
detoxification capacity, and potentially to produce valuable metabolic intermediates from waste toxic<br />
aromatics.<br />
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Lange - TIFN - Poster<br />
Dietary fibre-induced changes on short chain fatty acids and transcriptome response in C57Bl/6J<br />
mice<br />
K. Lange 1,3 , F. Hugenholtz 2,3 , M. Müller 1,3 , G. Hooiveld 1,3<br />
1 Nutrition, Metabolism & Genomics group, Division of Human Nutrition, Wageningen University;<br />
2 Laboratory of Microbiology, Wageningen University; 3 Netherlands Consortium for Systems Biology, the<br />
Netherlands<br />
Introduction<br />
A diet rich in dietary fibre has a beneficial health effect by promoting gastrointestinal homeostasis and<br />
decreasing risk for obesity and metabolic disorders. Dietary fibre is fermented by the gut microbiota, which<br />
yields the production of mainly short chain fatty acids (SCFA). These SCFA are taken up by the host<br />
organism, where some of the SCFA have been reported to affect inflammation and energy metabolism. The<br />
underlying mechanism and influence of microbiota is not known.<br />
Aim<br />
To assess interactions between diet, microbiota and host transcriptional changes in colon.<br />
Methods<br />
Five dietary fibre diets, containing either Inulin (IN), Fructooligosaccharide (FOS), Arabinoxylan (NAXUS),<br />
Guar Gum (GG) or Resistant starch (RS), and a control diet (no fermentable fibre added) were fed to<br />
C57Bl/6J mice for 10 days. Colonic tissue-derived RNA was used for Microarray and subsequent Gene Set<br />
Enrichment Analysis. Additionally, SCFA were measured in luminal intestinal content using gas<br />
chromatography.<br />
Results<br />
IN, FOS, NAXUS and GG showed increased intestinal SCFA concentration whereas RS was comparable to the<br />
control diet. Gene Set Enrichment Analysis revealed an enhancement of among others TCA cycle, electron<br />
transport chain, fatty acid metabolism, mitochondrial biosynthesis and Nrf2 target genes for IN, FOS,<br />
NAXUS and GG, but not for RS, in comparison to the control diet.<br />
Conclusion<br />
Dietary fibre increasing colonic luminal SCFA concentrations are also associated with enhanced energy<br />
metabolism and antioxidative response on transcriptional level. Microbiota composition data will be used<br />
for integration with the host transcriptome data to assess interactions.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Le Dévédec – CMSB (AddOn) - Poster<br />
Automated analysis of matrix adhesion dynamics in migrating tumour cells<br />
S.E. Le Dévédec 2 *, K. Yan 1 *, H. de Bont 2 , E. Spanjaard 3 , J. de Rooij 3 , F.J. Verbeek 1 *, B. van de Water 2 *<br />
1 Imaging and Bioinformatics, Leiden Institute of Advanced Computer Science, Leiden University; 2 Toxicology<br />
Department, Leiden/Amsterdam Centre of Drug Research, Leiden University; 3 Hubrecht Institute, The<br />
Netherlands<br />
*contributed equally<br />
Cell migration is essential in a number of processes and quantification can now be accomplished using<br />
modern integrative imaging approaches. Cell migration includes processes such as wound healing,<br />
angiogenesis, and cancer metastasis. Especially, invasion of cancer cells in the surrounding tissue is a crucial<br />
step that requires increased cell motility. Matrix adhesions are the closest contact between the cell and the<br />
extra-cellular matrix and they are very diverse in shape and lifetime. Tumour cell migration is controlled by<br />
the assembly and disassembly of those adhesions which are thus essential for cancer metastasis. However,<br />
little is known about the molecular mechanisms that regulate adhesion dynamics during tumour cell<br />
migration. In order to gain understanding of the relationship between cell migration and the dynamic of<br />
matrix adhesions (MA), quantification is required. We have developed an integrative imaging consisting of<br />
acquisition of both cellular and structural levels followed by an automated image analysis procedure of<br />
time-lapse image sequences. For the acquisition, epi-fluorescence and TIRF microscopy are combined at<br />
the same time to allow the visualization of respectively cell body, cell nucleus and matrix adhesions (TIRF).<br />
This results in high-resolution multi-channel time-lapse image sequences. The sequences are used for a<br />
quantification of migratory cell behaviour and the dynamics of matrix adhesions. The image analysis<br />
method consists of three consecutive steps, i.e. annotation, tracking, and phenotypic feature extraction.<br />
Phenotypic features are derived from both object masks and motion trajectories; typically morphology and<br />
motility features such as size, extension, elongation, orientation, velocity and motion linearity. In addition,<br />
features for relative position were introduced. Together these features represent a spatio-temporal<br />
quantification of the matrix adhesion dynamics in the migrating cell. In summary, we are presenting a set of<br />
tools and methodologies for advancing our understanding of how matrix adhesions are dynamically<br />
regulated in cells; paving the way for mathematical modelling.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Mooij – External (RUN, Nijmegen) - Poster<br />
Inferring cyclic causal models from equilibrium data: a case study<br />
Joris M. Mooij<br />
Institute of Computing and Information Sciences, Radboud University Nijmegen<br />
Causal feedback loops play important roles in many biological systems. In the absence of time series data,<br />
inferring the structure of such cyclic causal systems can be extremely challenging. I present a case study of<br />
a causal analysis of protein concentration data from a well-known cellular signalling network that plays an<br />
important role in human immune system cells [Sac05]. This flow cytometry data has been analysed in the<br />
past by different researchers in order to evaluate various causal inference methods. Most of these methods<br />
only consider acyclic causal structures (with the notable exception of [Ita10]), even though the data shows<br />
strong evidence that feedback loops are present.<br />
I have developed a novel method to infer cyclic causal models from equilibrium data using locally linear<br />
models. I have applied the method on the protein concentration data provided by Sachs et al. The method<br />
successfully identified feedback loops on small subsystems that have also been reported in the literature.<br />
The estimated causal model gives a significantly better quantitative description of the complete data set<br />
due to the fact that feedback loops are properly taken into account. This shows that a sophisticated<br />
quantitative modelling approach is able to extract more information out of available data, in this case giving<br />
a more complete representation of the signalling network.<br />
References<br />
[Sac05] K. Sachs, O. Perez, D. Pe'er, D. A. Lauffenburger, G. P. Nolan. Causal Protein-Signalling Networks<br />
Derived from Multiparameter Single-Cell Data. Science 308(5721):523-529, 2005<br />
[Ita10] S. Itani, M. Ohannessian, K. Sachs, G. P. Nolan, M. A. Dahleh. Structure Learning in Causal Cyclic<br />
Networks. JMLR Workshop and Conference Proceedings 6:165-176, 2010<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Nicholson - External (London, UK) - KEYNOTE<br />
Phenotyping the patient journey: systems medicine in the real world<br />
Jeremy K. Nicholson<br />
Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, United Kingdom<br />
Systems biology tools are now being applied at individual and population levels to understand integrated<br />
biochemical function of complex organisms including man. Metabolic phenotyping offers an important<br />
window on systemic activity and both NMR and mass spectrometric methods have been successfully<br />
applied to characterise and quantify a wide range of metabolites in multiple biological compartments to<br />
explore the biochemical sequelae of human disease (1,2). There is also extensive cross-talk between the<br />
host and the gut microbiome at the metabolic control and signalling level that is modulated in exquisitely<br />
complex ways by genes and environment and link to disease risk factors (3,4). These symbiotic supraorganismal<br />
interactions greatly increase the degrees of freedom of the metabolic system that poses a<br />
significant challenges to fundamental notions on the nature of the human diseased state, the<br />
aetiopathogenesis of common diseases and current systems modelling requirements for personalised<br />
medicine (5). We have developed scalable and translatable strategies for “phenotyping the hospital patient<br />
journey” (6) using top-down systems biology tools that capitalise on the use of both metabolic modelling<br />
and pharmaco-metabonomics (7,8) for diagnostic and prognostic biomarker generation to aid clinical<br />
decision-making at point-of-care. Such diagnostics (including those for near real-time applications as in<br />
surgery and critical-care) can be extremely sensitive for the detection of diagnostic and prognostic<br />
biomarkers in a variety conditions and are a powerful adjunct to conventional procedures for disease<br />
assessment that are required for future developments in “precision medicine” including understanding of<br />
the symbiotic influences on patient state (9). Many biomarkers also have deeper mechanistic significance<br />
and may also generate new therapeutic leads or metrics of efficacy for clinical trial deployment.<br />
Furthermore the complex and subtle gene-environment interactions that generate disease risks in the<br />
general human population also express themselves in the metabolic phenotype and as such the<br />
“Metabolome Wide Association Study” approach (10) gives us a powerful new tool to generate disease risk<br />
biomarkers from epidemiological sample collections and for assessing the health of whole populations.<br />
Such population risk models and biomarkers also feedback to individual patient healthcare models thus<br />
closing the personal and public healthcare modelling triangle.<br />
References<br />
1. Nicholson JK et al (2002) Nature, Rev. Drug Disc. 1 (2) 153-161.<br />
2. Nicholson JK et al (2004) Nature, Biotech. 22 1268-1274.<br />
3. Nicholson J.K. and Lindon, J.C. (2008) Nature 455 1054-1056.<br />
4. Swann, J.R. et al (2011) PNAS 108 4523-4530.<br />
5. Nicholson, J.K. et al (<strong>2012</strong>) Science 336 1262- 1267.<br />
6. Kinross, J. et al (2011) Lancet 377 (9780) 1817-1819.<br />
7. Clayton, T.A. et al (2006) Nature 440 1073-1077.<br />
8. Clayton, T.A. et al (2009) PNAS 106 14728-14733.<br />
9. Mirnezami, R. Nicholson, J.K. and Darzi, A. (<strong>2012</strong>) New Eng. J. Med. 366 (6) 489-491.<br />
10. Holmes, E. et al (2008) Nature 453 396-400.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Nijveen - NBIC - Poster<br />
LineUp: a Cytoscape plug-in for comparing gene expression in a network context<br />
Harm Nijveen, Job Geerligs, Jack Leunissen(Ϯ)<br />
Laboratory of Bioinformatics, Wageningen University, Wageningen; Netherlands Bioinformatics Centre<br />
(NBIC), Nijmegen.<br />
Comparing the expression levels of sets of genes between different conditions or a series of time points is a<br />
powerful approach to identify the important factors that play a role in a biological process of interest. The<br />
heat map is an often-used visualisation method to analyse these expression levels, by using a matrix where<br />
each cell represents the expression of a gene under a certain condition or time point and the colour of the<br />
cell shows the corresponding expression level. A shortcoming of heat maps is the lack of additional<br />
information on the displayed entities: the matrix structure of a heat map dictates a simple one dimensional<br />
organisation of the genes, whereas a two dimensional network structure would provide more information<br />
about the relationship of the different genes.<br />
We are developing a plug-in ‘LineUp’ for the popular network visualisation tool Cytoscape that helps the<br />
biologist to visualise the expressions levels of genes or abundance of metabolites in a network context<br />
(gene regulation network, metabolic pathway, etc.) and compare these levels between different conditions<br />
or time points. The same network is repeated in a grid of images, one for each individual condition, with<br />
the expression levels for the different genes mapped onto the network per condition using a heat map-like<br />
colouring for the nodes. This visualisation strategy that was popularised by Edward Tufte is known as ‘Small<br />
Multiples’. The additional information of the network structure can help to better interpret the results of<br />
an experiment or it could be used to validate and perhaps improve the used network. Analysis of large<br />
networks or many different conditions is greatly enhanced by using a dual- or multi-display setup.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Palm - NISB – Poster Flash<br />
Cell-based modelling of angiogenesis suggests that tip cell selection via lateral inhibition enables<br />
vessel stabilisation by repressing tip cell fate in branches<br />
Margriet M. Palm 1,2,3 , Roeland M.H. Merks 1,2,3,4<br />
1 Centrum Wiskunde & Informatica, Life Science Group, Amsterdam; 2 Netherlands Institute for Systems<br />
Biology, Amsterdam; 3 Netherlands Consortium for Systems Biology, Amsterdam; 4 Mathematisch Instituut,<br />
Universiteit Leiden, Leiden<br />
During angiogenesis, the formation of new blood vessels from existing ones, two different cell types are<br />
recognised: tip cells that lead sprouts and stalk cells that form the sprouts. Tip cells are distinguished from<br />
stalk cells by the extension of long filopodia that allow tip cells to sense and actively respond to their<br />
environment. During angiogenesis stalk cells are selected to become a tip cell via the Delta-Notch pathway,<br />
and subsequently take over the leading position. This implies that 1) tip cells are dynamically selected and<br />
2) the specific behaviour of tip cells enables them to lead a sprout. What is still unclear is how the dynamic<br />
selection of tip cells during angiogenesis benefits angiogenesis.<br />
To study the role of tip cell selection in angiogenesis we extend a cell-based angiogenesis model which<br />
describes how the collective behaviour of single cells can result in network formation. In this model<br />
sprouting is induced by chemotaxis towards a morphogen that is secreted by the cells. First, we define two<br />
separate cell types: tip and stalk, with type specific properties. To identify these properties we use a<br />
population with a fixed number of tip and stalk cells and search for parameter values that enable tip cells to<br />
migrate to sprout ends. Second, we add tip cell selection to the model which is based on lateral inhibition<br />
via the Delta-Notch pathway and tip cell activation by the ECM.<br />
We identified decreased chemotaxis as a tip-cell specific property. This difference between tip and stalk<br />
cells allows tip cells to explore their environment and to form and connect sprouts. Predefined tip cells can<br />
group together on a single branch and collectively pull on the branch, resulting in long and thin branches.<br />
When tip cell selection is added to the model, this behaviour disappears. Tip cells are still selected in<br />
branches, but only when such a tip cell becomes a leading cell, tip cell fate is maintained. Thus, tip cells<br />
election enables network stabilisation.<br />
Our work suggests that tip cell selection not only benefits angiogenesis by regulating the number of tip<br />
cells, but also by controlling the positioning of tip cells. The combination of lateral inhibition via Delta-<br />
Notch signalling and tip cell induction by the ECM maintains tip cell fate for cells at the sprout end, and<br />
represses long term presence of tip cells within branches.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Price - KC – Oral<br />
Global regulators play a pivotal role in the evolution of Lactococcus lactis under constant growth<br />
conditions<br />
Claire E. Price , Filipe Santos, Douwe Molenaar, Jan Kok, Bas Teusink, Bert Poolman, Oscar Kuipers<br />
Department of Molecular Genetics, University of Groningen, Nijenborgh 7, 9747AG, Groningen; Department<br />
of Biochemistry, University of Groningen, Nijenborgh 4, 9747AG, Groningen; Kluyver Centre for Genomics of<br />
Industrial Fermentation, Delft; Netherlands Consortium for Systems Biology, Amsterdam; Systems<br />
Bioinformatics IBIVU, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HV Amsterdam<br />
The industrially important lactic acid bacteria Lactococcus lactis has a well-characterised metabolism in<br />
which sugars are rapidly converted to mainly lactic acid. While L. lactis produces lactate under anaerobic<br />
conditions with high concentrations of glucose, the bacteria switches to more efficient mixed acid<br />
fermentation in the presence of other sugars and low glucose concentrations glucose. Inefficient and<br />
incomplete metabolism when a more efficient alternative appears available is a widespread phenomenon.<br />
Numerous metabolic models have been proposed to understand this phenomenon but do not accurately<br />
predict when homolactic or mixed acid fermentation pathways are used by L. lactis. By considering<br />
processes other than metabolic ones, for example protein synthesis, a more accurate prediction of the<br />
substrate-dependent switch from mixed-acid fermentation to homolactic fermentation can be predicted. In<br />
order to understand the evolutionary advantage of the inefficient use of substrates as well as adaptation to<br />
cultivation under constant conditions, L. lactis MG1363 has been evolved in 4 parallel small-scale<br />
chemostats in a new chemically defined medium developed for long term cultivation. Extensive<br />
characterisation of the adapted strains was performed including whole genome sequencing,<br />
transcriptomics and proteomics studies. Adaptation to growth in high dilution chemostats resulted in a<br />
gradual switch from homolactic to mixed acid fermentation. The evolved strains were more efficient in<br />
glucose uptake and had an altered carbohydrate utilisation pattern. Re-sequencing of the strains revealed<br />
that the gene encoding the catabolite control protein, ccpA, was mutated in all of the strains. The<br />
mutations resulted in amino acid substitutions in the DNA-binding region of CcpA. We therefore suggest<br />
that this affects the affinity of CcpA for DNA and results in a heterogenous change in the expression of Ccpregulated<br />
genes. The hypothesis has been tested using both in vitro DNA binding assays as well as in silico<br />
molecular dynamics techniques. While other mutations were identified in the evolved strains, only ccpA<br />
was mutated in all of the strains indicating that CcpA plays a pivotal role in evolution under constant<br />
growth conditions. The emergence of the mutations in ccpA were tracked during the evolution experiments<br />
giving insight into the path of evolution.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Rao - External (SBC-EMA, Groningen) - Poster<br />
A model reduction method for reversible biochemical reaction networks<br />
Shodhan Rao, Barbara Bakker, Arjan van der Schaft, Bayu Jayawardhana<br />
Systems Biology Centre for Energy Metabolism and Ageing (SBC-EMA), University of Groningen.<br />
For many purposes one may wish to reduce the number of dynamical equations of a chemical reaction<br />
network in such a way that the behaviour of a number of key metabolites is approximated in a satisfactory<br />
way. We propose a novel method for model reduction of biochemical reaction networks governed by<br />
reversible enzyme kinetics. A similar technique is also employed in the Kron reduction method for model<br />
reduction of resistive electrical networks.<br />
We obtain a reduced model from the original model by imposing the condition that certain metabolites<br />
remain at constant concentration. These metabolites are usually the ones that are not measurable or the<br />
ones that we consider insignificant from a modelling point of view. Consequently the reduced model has<br />
fewer number of variables as compared to the original model, and yet the behaviour of a number of<br />
significant metabolites in the reduced network is approximately the same as in the original network. Our<br />
model reduction method is useful from a computational point of view, especially when we need to deal<br />
with models of huge biochemical reaction networks.<br />
We have applied our model reduction technique in order to reduce the yeast glycolysis model described in<br />
[1]. We have simulated the transient behaviour of the metabolites that are not eliminated during the model<br />
reduction procedure. It is found that there is a good agreement between the transient behaviour of the<br />
concentration of most of such metabolites when comparing the full network to the reduced network.<br />
Reference<br />
1. Karen van Eunen, José A.L. Kiewiet, Hans V. Westerhoff and Barbara M. Bakker (<strong>2012</strong>), Testing<br />
Biochemistry Revisited: How In Vivo Metabolism Can Be Understood from In Vitro Enzyme Kinetics,<br />
PLoS Computational Biology, 8(4): e1002483.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Rienksma - External (WCSB, Wageningen) - Oral<br />
A method to classify metabolic enzyme regulation from gene and protein expression data<br />
R.A. Rienksma, P.J. Schaap, V.A.P. Martins dos Santos<br />
Laboratory for Systems and Synthetic Biology, Wageningen University & Research Centre<br />
Genome-scale metabolic models (GSMM) are available for Mycobacterium tuberculosis (Beste et al., 2007;<br />
Jamshidi and Palsson, 2007; Fang et al., 2010) that can serve as a tool to define the region of feasible<br />
metabolic flux distributions under various conditions. It has been shown for S. cerevisiae (Bordel et al.,<br />
2010) that these fluxes can be combined with gene-expression data to classify enzymes in three categories:<br />
1) enzymes showing transcriptional regulation, 2) enzymes showing post-transcriptional regulation, 3)<br />
enzymes showing metabolic regulation. Semi-quantitative proteomics data can also be used to distinguish<br />
between these categories.<br />
Changes in gene expression from one condition to another are usually blindly linearly extrapolated to<br />
changes in enzyme concentrations and flux. The consequences are that post-transcriptional regulation of<br />
enzymes is ignored and potential drug targets are overlooked.<br />
It is explained how different types of data from growth experiments in quasi-steady state conditions can be<br />
used to classify enzymes: gene expression data (microarrays), semi-quantitative proteomics data, and<br />
quantification of exchange fluxes (substrate uptake rates, secretion rates). The specific growth rate and the<br />
exchange fluxes are used to constrain the solution spaces of the different conditions. By comparing the<br />
means of all metabolic fluxes with the semi-quantitative proteomic and gene expression data from at least<br />
two different conditions, the metabolic enzymes can be classified in the three categories mentioned above.<br />
References<br />
1. Beste DJV, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek<br />
AM, McFadden J: GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis<br />
metabolism. Genome Biology 2007, 8:R89.<br />
2. Bordel S, Agren R, Nielsen J: Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals<br />
Transcriptional Regulation in Key Enzymes. PLoS computational biology 2010, 6:7<br />
3. Fang X, Wallqvist A, Reifman J: Development and analysis of an in vivo-compatible network of<br />
Mycobacterium tuberculosis. BMC Systems Biology 2010, 4:160.<br />
4. Jamshidi N, Palsson B: Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv<br />
using the in silico strain iNJ661 and proposing alternative drug targets. BMC Systems Biology 2007,<br />
1:26.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Rooij - CBSG - Poster<br />
Capturing complex cell shape in the Cellular Potts Model<br />
Jop van Rooij, Ramiro Magno, Yara Sanchez-Corrales, Veronica Grieneisen, Stan Marée<br />
Theoretical Biology & Bioinformatics, Utrecht University The Netherlands; Computational and Systems<br />
Biology, John Innes Centre, United Kingdom<br />
The Cellular Potts Model (CPM) is able to simulate cells and their behaviour within tissues on the basis of<br />
cell size, surface tension and adhesion. Behaviour which can be modelled using the CPM includes cell<br />
aggregation, cell sorting and tissue rounding. However, although cell size, surface tension and adhesion<br />
arguably play a important role in determining the behaviour of biological cells, it is clear that cells also<br />
actively regulate their shape and movement. In its basic formulation, the CPM does not allow for these<br />
active cell deformations and therefore the set of cell and tissue behaviour it can model is limited.<br />
We present extensions to the CPM for modelling actively regulated cell deformations. This allows us to<br />
create realistic spatial models of complex unicellular organisms such as yeast and amoebas. On a higher<br />
level, it makes it possible to study the relation between the specified cell shape of a single cell and the<br />
resultant shape of that cell when it interacts within a tissue context. This can give insight in the manner in<br />
which conflicts between cells are resolved, both spatially and temporary.<br />
Our extended Cellular Potts Model allowed us to study the development of the epidermal leaf cells in<br />
Arabidopsis thaliana in a theoretical framework. During the development of the leaf, these cells, called<br />
pavement cells, develop from simple (elongated) cells into complex cells which extend into each other to<br />
form a puzzle like pattern. We study this process by simulating the development of pavement cells and<br />
analysing the relation between cell and tissue characteristics.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Rossell – External (CSBB, Nijmegen) – Poster Flash<br />
Inferring metabolic states in uncharacterised environments using gene-expression<br />
measurements<br />
Sergio Rossell, Martijn A. Huynen, Richard A. Notebaart<br />
Centre for Systems Biology and Bioenergetics, Nijmegen<br />
The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux<br />
distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the<br />
frequent situation where the nutrients available to the cells are unknown. These two factors: network size<br />
and ignorance of the nutrient availability, challenge the identification of the actual metabolic state of living<br />
cells among the myriad possibilities. Here we develop a method to address this challenge by integrating<br />
gene-expression measurements with genome-scale models of metabolism. Our method builds<br />
environment-specific models by exploring the space of alternative flux distributions that maximise the<br />
agreement between gene expression and metabolic fluxes. We applied our method to model the metabolic<br />
states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as<br />
main energy source. The resulting models comprise drastically reduced spaces of alternative flux<br />
distributions, as is evidenced from the fact that they include only 70% and 50% of the reactions in the<br />
original model, respectively. These environment-specific models were successful in i) predicting genes that<br />
are specifically essential for growth on the media tested, and ii) identifying the main energy source<br />
available to the cells in each medium. Our method is of immediate practical relevance for medical and<br />
industrial applications, such as the identification of novel drug targets, and the development of<br />
biotechnological processes that use complex, largely uncharacterised media, such as biofuel production.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Schmitz - NISB - Oral<br />
Towards a consensus model of yeast glycolysis<br />
J. Schmitz 1 , N. Swainston 2 , M. Heinemann 3 , B. Teusink 1 (and many colleagues)<br />
1 VU University Amsterdam; 2 The University of Manchester; 3 University of Groningen<br />
The importance of metabolism and in particular glycolysis for health and disease is currently being rediscovered.<br />
As a blueprint for glycolysis in eukaryotic organisms, we aim to develop a dynamic model of<br />
yeast glycolysis - unprecedented in scope and quality. Past attempts to describe this process by applying<br />
bottom-up mechanistic modelling approaches were rather limited in scope and for their development only<br />
a fraction of the data buried in literature was used. However, developing a detailed model of such a central<br />
pathway with full exploitation of measurement capabilities as well as already available literature data<br />
represents a formidable challenge. We are convinced that such attempt can only succeed by the combined<br />
effort of multiple groups in a community-driven approach. In this ‘consensus yeast glycolysis’ consortium<br />
the following workflow is being applied: The initial model is developed by integration of in vitro determined<br />
enzyme kinetics. It provides a basis for estimation of in vivo kinetics from data of steady state metabolite<br />
concentrations for a wide range of pathway fluxes. The derived in vivo kinetics are subsequently tested and<br />
refined against dynamic data recorded during e.g. glucose pulse perturbation experiments. This workflow<br />
will be repeated iteratively to increase the mechanistic information included in the model, improve its<br />
predictive strength, provide novel biological insight and eventually yield the consensus model. On this<br />
poster we will present the recent progress in combining the inputs provided by different groups and<br />
experimental platforms according to this workflow. Specifically, the observed differences between in vitro<br />
and in vivo kinetics, their implications for predicted dynamical behaviour of the pathway as well as key<br />
challenges faced while applying this workflow will be presented.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Sips - CMSB - Poster Flash<br />
A computational framework to analyse heterogeneous plasma lipoprotein metabolism<br />
Fianne Sips, Christian Tiemann, Peter Hilbers, Natal van Riel<br />
Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands<br />
Introduction<br />
The size and lipid content distributions of plasma lipoproteins co-determine metabolic and cardiovascular<br />
disease risks. Understanding of the mechanisms which control lipoprotein metabolism therefore has high<br />
clinical relevance. While much of the physiology of lipoprotein metabolism is known, the interplay of the<br />
mechanisms leading to increased cardiovascular risk is not fully understood.<br />
To aid further quantitative understanding of the mechanisms underlying phenotype changes in mouse<br />
models a novel computational model of plasma lipoprotein metabolism in mice was developed. The model<br />
was successfully applied to wild-type mouse phenotypes as well as to altered phenotypes resulting from<br />
common genetic deficiency models and data sets in which the effects of an intervention were observed in<br />
time.<br />
Methods<br />
The computational model entails detailed descriptions of heterogeneous lipoproteins, coupled with a<br />
phenomenological kinetic model describing lipoprotein metabolism. Both HDL and VLDL metabolism and a<br />
framework of compositional calculations based on multiple data sets of C57Bl/6J mice were incorporated in<br />
the model. The model was developed and parameterised to describe the cholesterol and triglyceride<br />
profiles [1] obtained from fast protein liquid chromatography (FPLC) separation.<br />
Results<br />
The novel computational model was able to reproduce experimentally observed plasma lipoprotein profiles<br />
of wild-type mice. Model simulations of the steady state profiles provided predictions of the unobserved<br />
underlying lipid and lipoprotein distributions and fluxes.<br />
By applying parameter perturbations qualitative predictions of the lipoprotein profile of common<br />
genetically modified mouse models were successfully made. The model therefore described not only wildtype<br />
metabolism, but also protein deficiency models such as the SR-B1 or PLTP knock-out models. The<br />
model was also applied to FPLC profiles obtained following an intervention entailing LXR activation by<br />
TO901317.<br />
Conclusion<br />
A novel computational framework is presented which is able to describe the metabolism and FPLC profiles<br />
of wild type mice. The model provides opportunities to investigate a variety of complex phenotypes in<br />
which lipoprotein metabolism is disturbed resulting in changes in particle composition and size.<br />
References<br />
1. Aldo Grefhorst, Maaike H. Oosterveer, Gemma Brufau, Marije Boesjes, Folkert Kuipers, Albert K. Groen,<br />
Pharmacological LXR activation reduces presence of SR-B1 in liver membranes contributing to LXRmediated<br />
induction of HDL-cholesterol, Atherosclerosis, Volume 222, Issue 2, June <strong>2012</strong>, Pages 382-389<br />
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Smits - External (UvA, Amsterdam) - Poster<br />
Intracellular pH control of yeast growth and development<br />
Gertien J. Smits, Rick Orij, Azmat Ullah, Stanley Brul<br />
Dept of Molecular Biology and Microbial Food Safety, Swammerdam Institute for Life Sciences/Netherlands<br />
Institute for Systems Biology, University of Amsterdam<br />
Because protonation affects the properties of almost all molecules in cells, pHc is usually assumed to be<br />
constant. Interestingly, pHc is emerging as an important determinant of tumourigenic transformation and<br />
metastasis. In the model organism yeast, pHc changes in response to the presence of nutrients and varies<br />
during growth. Since small changes in pHc can already lead to major changes in metabolism, signal<br />
transduction, and phenotype, we decided to analyse the genetic basis of pHc control. Introducing a pHsensitive<br />
GFP into the yeast deletion collection allowed quantitative genome-wide analysis of pHc in live,<br />
growing yeast cultures. pHc is robust towards gene deletion, and deletion of no single gene resulted in a<br />
pHc of more than 0.3 units lower then wild-type. Correct pHc control required not only vacuolar proton<br />
pumps, but also mitochondrial function. Additionally, we identified a striking relationship between pHc and<br />
growth rate. Careful dissection of cause and c onsequence revealed that pHc is in quantitative control of<br />
cell division rate. Single deletion of only 19 genes released this control, revealing that pHc is a true signal<br />
that controls cell division rate. Additional evidence that pHi may also be an important factor in yeast’s<br />
developmental decisions will also be discussed.<br />
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Steijaert - TIFN - Oral<br />
Integrative analysis of bacterial short chain fatty acid production<br />
M.N. Steijaert 1,2,3 , P. Kovatcheva-Datchary 2,5 , A. Maathuis 1,2 , J.H.G.M. van Beek 4 , M. Egert 2,5 , W. de Vos 2,5 , K.<br />
Venema 1,2 , A.A. de Graaf 1,2 , H. Smidt 2,5<br />
1 TNO, Zeist; 2 TIFN, Wageningen; 3 <strong>NCSB</strong>, Amsterdam; 4 Vrije Universiteit, Amsterdam; 5 Wageningen<br />
University, The Netherlands<br />
An important beneficial role of gastrointestinal microbiota is the production of short chain fatty acids<br />
(SCFAs) such as acetate, propionate and butyrate from non-digestible dietary carbohydrates in the colon. In<br />
the host, SCFAs play important roles in the maintenance of gut health and overall metabolic health. As a<br />
result, SCFA production is considered an important target for prebiotic modulation. In this study we used<br />
the TNO in vitro model of the human proximal colon (TIM-2) to investigate how carbohydrate substrates<br />
influence the production of SCFAs. In particular, we aimed to relate the activities of predominant bacterial<br />
groups to the accompanying activities of SCFA production pathways.<br />
Uniformly 13C-labeled starch, inulin and lactose were fermented by a microbial community derived from<br />
human faecal samples in three TIM-2 experiments. Concentrations and 13C-labeling of major fermentation<br />
products and intermediates were determined at various time points up to 8 hours after substrate<br />
administration. A computational modelling technique, 13C metabolic flux analysis, was used to estimate<br />
the activity of main metabolic pathways during the fermentation of the labelled substrates. 16S ribosomal<br />
RNA was isolated at various time points, and RNA-based stable isotope probing (RNA-SIP) was used to<br />
separate the 16S rRNA into fractions according to the amount of substrate-derived 13C incorporated. These<br />
16S rRNA fractions were further analysed using phylogenetic microarray (HITChip) analysis.<br />
The metabolic flux analysis yielded distinct profiles for each of the analysed substrates. The three<br />
fermentation experiments show considerable differences in substrate uptake rates, the relative activity of<br />
product pathways and the transient accumulation of intermediate metabolites (e.g., transient lactate<br />
production during the fermentation of lactose and inulin but not during starch fermentation). In addition,<br />
the combination of RNA-SIP with HITChip analysis lead to the identification of bacterial phylotypes that<br />
were most actively involved in the fermentations. Particularly, it allowed to distinguish between bacterial<br />
phylotypes that act as primary substrate degraders and bacteria that play a role further down the trophic<br />
chain. To this end, Ruminococcus bromii-related populations were most actively involved in initial starch<br />
degradation, whereas populations related to Dorea longicatena and Bifidobacterium adolescentis, and<br />
Bifidobacterium spp. and Collinsella spp. constituted most important inulin and lactose degraders,<br />
respectively.<br />
13C metabolic flux analysis and 16S rRNA-SIP provide complementary information about these complex<br />
fermentation processes. Therefore, we will further integrate both approaches to better understand how<br />
substrates influence SCFA production. A better understanding of these complex interactions will guide the<br />
design of novel nutritional products that influence bacterial SCFA production and improve human health.<br />
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Su - External (La Jolla, US) - KEYNOTE<br />
Crowdsourcing biology: the Gene Wiki, BioGPS and biological games<br />
Andrew Su<br />
Department of Molecular and Experimental Medicine (MEM), The Scripps Research Institute, La Jolla,<br />
California, United States of America<br />
Comprehensively annotating the function of human genes is a formidable challenge for the biomedical<br />
research community. Current efforts to organise biological knowledge are driven by a few centralised<br />
teams of curators and developers. Here, we describe several efforts to engage the entire biomedical<br />
research community in addressing this challenge. The Gene Wiki focuses on building a gene-specific review<br />
article for every human gene. BioGPS aims to build a community-maintained gene annotation portal. And a<br />
suite of games at GeneGames.org addresses a variety of biomedical research goals using biological games.<br />
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Suarez-Diez - External (WCSB, Wageningen) - Poster<br />
DIVA a multi-scale network visualisation and analysis tool<br />
Maria Suarez Diez, Jesse van Dam, Peter J Schaap and Vitor AP Martins dos Santos<br />
Laboratory of Systems and Synthetic Biology, Dreijenplein 10, 6703 HB, Wageningen, The Netherlands<br />
A number of methods have been developed to infer genome wide regulatory networks using omics data.<br />
Direct network inference methods such as CLR, ARACNE and MRNET result in a single network, which<br />
interconnects the genes according to their co-expression pattern. In addition, there are other data-centric<br />
methods, such as biclustering techniques, that aim at identifying the response of the network to external<br />
perturbation, even when the actual topology of the network is still unknown. These methods also allow the<br />
introduction of additional biological information. For visualisation purposes the direct network inference<br />
methods have the advantage that their outcome can be displayed as one network and provides an<br />
overview of the regulation in the organism, whereas module identification methods lead to the discovery of<br />
functionally related sets of co-expressed genes.<br />
We have developed an integrative software platform for mining, co-visualisation and analysis of these and<br />
other types of biological networks such as protein interaction networks. The main feature of this<br />
application is the direct inspection and analysis of these different types of networks. When a selection is<br />
made in one network the selection is automatically highlighted in the other networks and a list with of<br />
details of the selected nodes is shown. Every node in the network has a unique id, which is linked to the<br />
locus tag. A selection can be stored and retrieved from a list of previously stored selections or imported<br />
sets of genes. The following analysis can be performed on a selection, (1) motif identification and testing,<br />
(2) GO enrichment analysis and (3) expression profiling. Each of the performed analysis is stored with a<br />
unique id, so that they can be accessed on later times.<br />
By applying DIVAT to Mycobacterium tuberculosis we were able to discover new regulatory elements and<br />
functional modules and improve some known modules. A number of these cases will be presented.<br />
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Supandi - NBIC - Oral<br />
Computational prediction of changes in cerebral metabolic fluxes from mRNA expression data<br />
Farahaniza Supandi 1,4 , Johannes van Beek 1-3<br />
1 Section Medical Genomics, Department of Clinical Genetics, VU University medical centre, Amsterdam;<br />
2 Netherlands Consortium for Systems Biology, Amsterdam; 3 Centre for Integrative Bioinformatics, VU<br />
University, Amsterdam; 4 Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala<br />
Lumpur, Malaysia<br />
Parkinson’s disease (PD) is the second most common type of neurodegenerative disease affecting elderly<br />
people. Although metabolic changes have been associated with PD, it is unknown how metabolic fluxes in<br />
different brain regions are redistributed in early and late stages of the disease. We used a network model<br />
of brain central carbon and energy metabolism to predict changes in the small regions that are most<br />
affected by PD. The model analysis makes use of measured changes in mRNA expression in the substantia<br />
nigra (SN) and several other brain regions of PD patients relative to healthy controls, taking flux balance<br />
and reaction reversibility constraints into account. Our analysis predicts that during PD<br />
(1) metabolic fluxes in glycolysis, TCA cycle and oxidative phosphorylation are decreased in most brain<br />
regions, including the substantia nigra and dopaminergic neurons,<br />
(2) to compensate for decreases in alpha-ketoglutarate dehydrogenase activity, the glutamate-GABA<br />
shuttling pathway is activated,<br />
(3) metabolic fluxes and ATP synthesis are increased in the globus pallidus internus (GPi) region of the<br />
brain.<br />
The pattern of altered gene expression suggests that metabolic re-routing partially compensates for<br />
decreased enzyme expression to maintain the capacity for ATP synthesis during PD. Because the GPi is<br />
inhibited by the substantia nigra via neural circuits which control movement, the GPi is known to show<br />
increased physiological activity during PD, which apparently is reflected by increased metabolism.<br />
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Szabo - NISB - Poster<br />
Modelling heart valve formation<br />
András Szabó 2,3 , Anne K. Lagendijk 1 , Roeland M.H. Merks 2,3,4 , Jeroen Bakkers 1<br />
1 Hubrecht Institute, KNAW and University Medical Centre Utrecht, Utrecht; 2 <strong>NCSB</strong>; 3 CWI, Amsterdam;<br />
4 Mathematical Institute, Leiden University, Leiden<br />
In the developing embryo heart valves emerge from the endocardium, the inner lining of the heart tube,<br />
due to an increased extracellular matrix (ECM) production at well defined positions. One of the main ECM<br />
components in this process is glycosaminoglycan hyaluronan (HA), secreted by the endocardial cells. After<br />
undergoing a so-called endothelial-mesenchymal transition (endo-MT), endocardial cells colonise the<br />
excess ECM scaffold, called endocardial cushion, forming the heart valves. This process requires the<br />
regulated secretion of HA. Expression of the main HA synthase, Has2, is regulated both negatively through<br />
micro-RNAs, and positively, through HA itself.<br />
We present a computational model of the positive and negative regulators of Has2, and show that the<br />
known mechanisms are indeed able to explain homeostatic balance of HA levels. Furthermore, we point<br />
out that the activating and inhibitory interactions between the micro-RNA miR-23, Has2 and HA are<br />
reminiscent of a reaction-diffusion system. Using a spatially extended mathematical model, we show that<br />
the system can produce a confined expression of HA, but only if the inhibitory signal is transferred to<br />
adjacent cells. These results suggest that miR-23 is exported from the endocardial cells in exosomes.<br />
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Thijssen – External (CSBB, Nijmegen) – Poster Flash<br />
Bayesian data integration of time-series omics data through dynamical models of the yeast cell<br />
cycle<br />
Bram Thijssen, Tjeerd Dijkstra<br />
Institute of Computing and Information Sciences, Radboud University Nijmegen<br />
We used dynamical models of the yeast cell cycle to integrate gene expression time-series from<br />
microarrays, transcript abundance from SAGE (serial analysis of gene expression), protein abundance from<br />
quantitative western blot and information on rate constants. We used a Bayesian framework for data<br />
integration and Bayes factors to compare alternative model hypotheses (Vishemirsky and Girolami 2008).<br />
We evaluated many different sampling techniques including several variants of sequential Monte Carlo and<br />
parallel tempering.<br />
We found that many sampling techniques did not adequately deal with the observed multimodality of the<br />
model parameters and that the best technique of parallel tempering imposes a practical limit of ~30 state<br />
variables and ~60 parameters. As practical limit we set a few days of running time on a 64 core compute<br />
cluster. Clearly, there is ample room for improvement.<br />
Second, we showed that it is feasible to integrate mRNA and protein data without any extensive preprocessing<br />
(like in Chen et al Cell <strong>2012</strong>), leading to testable hypotheses in the natural units of each of the<br />
parameters. For example by combing the time series of two-colour microarrays (which only measures<br />
relative levels) with absolute SAGE data we could infer the unknown initial concentrations of all mRNA<br />
species in the model. Thus, data integration allows the variables to be expressed in their natural units that<br />
are often not available from high-throughput assays.<br />
References<br />
<br />
Chen, Mias, Li-Pook-Than et al, Personal Omics Profiling Reveals Dynamic Molecular and Medical<br />
Phenotypes Cell 148, 1293-1307, <strong>2012</strong>.<br />
Vishemirsky and Girolami, Bayesian Ranking of Biochemical System Models, Bioinformatics 24, 833-839,<br />
2008.<br />
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Tsivtsivadze - External (WCSB, Wageningen) - Poster<br />
Sparse co-regularisation for multi-view clustering<br />
Evgeni Tsivtsivadze, Peter Schaap, Vítor Martins dos Santos<br />
Laboratory for Systems and Synthetic Biology, Wageningen University & Research Centre<br />
In many unsupervised learning problems, the data can be available in different representations/views, e.g.<br />
sequences, graphs, images, etc. By leveraging information from multiple views we can obtain clustering<br />
that is more robust and accurate compared to the one obtained via the individual views. We propose a<br />
novel framework for merging information from individual views into a single clustering model. Our<br />
approach is based on sparse co-regularisation of the clustering hypotheses (via L1-norm penalty function)<br />
that searches for the solution which is consistent across different views. In our empirical evaluation on<br />
three real-world biomedical datasets proposed method notably outperforms number of baseline<br />
algorithms such as several variants of spectral clustering, hierarchical, DBSCAN, and k-means clustering.<br />
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Unen - NISB<br />
Dynamics of GPCR activation: measuring Gaq-mediated signalling in living cells<br />
J. van Unen, N. Reinhard, T.W.J. Gadella Jr, J. Goedhart<br />
Swammerdam Institute for Life Sciences, Section of Molecular Cytology, van Leeuwenhoek Centre for<br />
Advanced Microscopy, University of Amsterdam, Amsterdam, The Netherlands<br />
The Gaq signalling pathway has been implicated in many important cellular processes, including<br />
proliferation, cell migration and cytoskeletal organisation. The classical downstream pathway through PLCβ<br />
and calcium mobilisation has been extensively studied to date. More recently, another effector of Gaq is<br />
identified, p63RhoGEF, which has been proposed to activate the RhoA signalling cascade. Activation of this<br />
novel, competing pathway leads to actin polymerisation and translocation of the transcription factor MKL2<br />
to the nucleus. Using advanced microscopy techniques like Farster resonance energy transfer (FRET), we<br />
measured different downstream effectors with different temporal activation kinetics. Starting at the<br />
plasma membrane on the millisecond time-scale with receptor G-protein interaction and G-protein<br />
activation, followed by seconds time-scale dynamics of calcium fluxes and finally nuclear translocation of<br />
MKL2 on the minute time-scale. Our preliminary results hint on the importance of the different temporal<br />
dynamics in this important signalling pathway.<br />
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Vanlier - CMSB - Oral<br />
Designing optimal experiments to identify progressive adaptations in biological systems<br />
Joep Vanlier, Christian A Tiemann, Peter AJ Hilbers, Natal AW van Riel<br />
Department of Biomedical Engineering, Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5612<br />
AZ, The Netherlands<br />
Unravelling long term adaptations in biological systems is complicated by the multilevel nature of such<br />
system and the time-scale on which they occur. Such complications are further exacerbated by the fact that<br />
insufficient information on the network structure and interaction mechanisms is available to explicitly<br />
formulate the involved processes. For this reason classical methods for optimal experiment design are not<br />
applicable. We propose and demonstrate a new method for experimental design which we apply on a<br />
model of hepatic lipid and plasma lipoprotein metabolism under pharmacological activation of the liver X<br />
receptor (LXR). We perform experiment design in order to more accurately predict whole-body excretion of<br />
cholesterol. At present, the adaptation of the associated fluxes could not be predicted accurately.<br />
We proposed a method for designing experiments in order to identify progressive changes in biological<br />
systems. Our method captures the modulating effects on the metabolic level using time-dependent<br />
descriptions (or trajectories) of the model parameters. By generating bootstrap replicates of the data a<br />
distribution of parameter trajectories is generated. The non-linear relations in this distribution are<br />
subsequently probed to determine which experiments would lead to more constrained predictions at the<br />
output of interest.<br />
The proposed method enabled us to rank different experiments according to their efficacy to constrain our<br />
prediction of interest. In this case a non-invasive experiment based on faecal matter appeared to be highly<br />
effective. Data corresponding to the proposed experiment was acquired and subsequently included. This<br />
lead to a reduction in the uncertainty associated with the identified adaptations in cholesterol excretion.<br />
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Venema - TIFN - Poster<br />
Use of 13C labeled substrates to trace microbial metabolism in the colon; light in the tunnel<br />
Koen Venema, Annet Maathuis, Albert de Graaf<br />
TNO, Zeist<br />
It is important to know which species in the colon are responsible for microbial activities, to elucidate<br />
dominant microbial functionalities in the human GI-tract, cooperation between different members of the<br />
microbiota to break down substrates, and ultimately their effect on host health. Stable-isotopes can play an<br />
important role in answering these questions. To couple the microbial composition to metabolic activity in<br />
the colon, in situ nucleic acid-based Stable-Isotope Probing (SIP) has been shown to be very promising.<br />
Typically, 13C-labeled carbohydrates that act as substrates in the food chain are used for this. So far we<br />
have used 13C-labeled lactose, inulin, starch, galacto-oligosaccharides, and 6’-sialyl-lactose (a breast-milk<br />
component).<br />
Isotopic labelling in metabolites can be specifically detected by mass spectrometry and/or NMR-based<br />
analytical techniques. Recently, we have coupled SIP to LC-MS and NMR measurements to create the link<br />
between i) substrate, ii) microbe that is involved in fermentation of the substrate, and iii) metabolites that<br />
are produced. This has for instance allowed the elucidation of cross-feeding between different members of<br />
the microbiota in fermentation of starch. These experiments were performed in a validated, computercontrolled<br />
dynamic in vitro model of the colon (TIM-2) that accurately simulates the conditions in the<br />
human large intestine. The main 13C-labeled microbial metabolites that were detected in these studies<br />
were SCFA, lactate, formate, ethanol and glycerol. They together accounted for a 13C recovery rate of 90-<br />
95%. Since the exact amount and nature of microbial metabolites produced on certain 13C-labeled<br />
substrates can be determined, the exact amount of energy harvested by the microbiota can be calculated,<br />
allowing the possibility to link composition and/or activity of certain members of the microbiota to obesity.<br />
The combination of technologies described above have also been used in clinical trials. The results have<br />
greatly advanced our understanding of the processes occurring in the colon.<br />
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Verbruggen - NISB - Oral<br />
Dynamics of an in vivo chromatin associated system: nucleotide excision DNA repair<br />
Paul Verbruggen 1,2 , Tim Heinemann 3,4 , Erik Manders 1 , Thomas Höfer 3,4 , Roel van Driel 1,2<br />
1 Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam; 2 Netherlands<br />
Institute for Systems Biology, 1090GE Amsterdam; 3 Research Group Modelling of Biological Systems,<br />
German Cancer Research Centre, 69120 Heidelberg, Germany; 4 Bioquant Centre, 69120, Heidelberg,<br />
Germany<br />
Many chromatin-associated multi-protein complexes display highly dynamic behaviour individual protein<br />
components rapidly associate and dissociate at a time scale of seconds, whereas the processes that are<br />
catalysed occur at the minutes-hours scale. Examples of such systems are transcription initiation, DNA<br />
replication and DNA repair in which the assembly of large protein complexes and process progression must<br />
be coordinated to ensure fidelity and specificity within a reasonable time frame. How such highly dynamic<br />
protein complexes achieve this is poorly understood. To address this fundamental issue we analyse the<br />
dynamic in vivo behaviour of proteins that cooperate in Nucleotide Excision DNA Repair (NER). We<br />
integrate experimental data obtained from quantitative live-cell imaging experiments into a mathematical<br />
model that reproduces all experimental data at the two time scales and gives deep insight into the<br />
functional principles of such dynamic systems. The model was only reconcilable with the data if stochastic<br />
protein binding and dissociation was assumed. Currently, we are testing model predictions to explore the<br />
robustness, control and cell-to-cell variability of the in vivo NER process. For instance, model predictions<br />
indicate that individual repair proteins have remarkably low control over the NER process. Exploiting the<br />
natural cell-to-cell variation of the concentration of the DNA damage sensing protein XPC, single cell<br />
analyses show that the NER process may be indeed highly robust against variations in protein<br />
concentration. Our in-depth understanding of the in vivo NER dynamics will be extended to other dynamic<br />
chromatin-associated processes, in particular transcription initiation, using NER as a paradigm.<br />
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Waagmeester - NBIC – Poster Flash<br />
Pathway curation: from isolated pathway knowledge to search-click-and-grow<br />
Andra Waagmeester, Egon Willighagen, Chris Evelo<br />
Department of Bioinformatics, BiGCaT, Maastricht University<br />
We here present an approach to address the problem of aggregating all knowledge supporting or<br />
contradicting biological pathways. With the increasing accuracy and high-throughput aspects of nowadays<br />
biological experimental research, the amount of data for known pathways is exploding. However, it is hard<br />
to match past knowledge on pathways against these new data and insights.<br />
Pathways, like signalling, metabolic, and transcription pathways, are abstractions of biological interactions.<br />
They provide a way to present large quantities of knowledge in a concise manner. The curation of pathways<br />
involves the integration of biological data from a pluriform set of different knowledge sources, such as<br />
databases, the literature or crowd mined expert knowledge. Current pathway approaches require large<br />
curation teams browsing through many websites. Whether it is PubMed, Uniprot, or any other relevant<br />
resource. Because most of the data is already in electronic form, data aggregation should be feasible. The<br />
different website layouts are not helping, but many resources do come with a so-called application<br />
programme interface (API), which allow access to the data.<br />
We demonstrate how existing pathway knowledge can be complemented with those further resources.<br />
For this, we use PathVisio (www.pathvisio.org), which is an extensive desktop pathway editor and pathway<br />
analysis tool. As an analysis tool it allows the projection of experimental data on pathways. PathVisio shares<br />
an open source codebase and a native format (GPML) with WikiPathways (www.wikipathways.org).<br />
WikiPathways is an open resource for biological signalling, pathway, and regulation pathways. It is based on<br />
the same principles as Wikipedia where the community as a whole provides the knowledge. WikiPathways<br />
contains more than 2000 pathways covering 26 species. Where WikiPathways requires a lean code base for<br />
optimised online performance, PathVisio being run as a desktop application, allows more complex<br />
algorithms and libraries to be included. This also enables the development of various plug-ins.<br />
We have developed a plug-in called PathVisio Loom, which provides a framework for knowledge<br />
aggregation through menu guided additions. It allows the integration of biological knowledge available in<br />
the different formats (e.g. text mining data, interaction data available through web services, Semantic Web<br />
Data or structure interaction data from local files). Starting from a single pathway object, after clicking the<br />
curator is given a set of known interactions aggregated from different online resources to select those<br />
relevant to the pathway under scrutiny.<br />
With PathVisio Loom, pathway knowledge integration transforms from a mainly manually editing process<br />
to a more click-and-grow process.<br />
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<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Yuan – External (TUE, Eindhoven) - Poster<br />
How to incorporate the effect of different light conditions in a genome‐scale model of<br />
Arabidopsis<br />
Yuan, H.L. 1 , Wijnen, Bram 1 , Ren, Y. 2 , Zhou, G.F. 2 , Yu, J.Q. 3 , Hilbers, P.A.J. 1 , van Riel, N.A.W. 1<br />
1 Eindhoven University of Technology, Eindhoven, Netherlands; 2 Philips Research Asia‐Shanghai, Shanghai,<br />
China; 3 Zhejiang University, Hangzhou, China<br />
Light affects many aspects of plant growth and developmental processes as well as disease resistance in<br />
plants. Arabidopsis thaliana is a small flowering plant that serves as a model organism for understanding<br />
the complex processes required for plant growth and development. Computational models can be used to<br />
provide key insights into the way that light regulates biological processes in the life cycles of a plant and the<br />
mechanisms underlying these processes. In this study we present the concept how to integrate the effect<br />
of different light conditions into a genome‐scale model of Arabidopsis. Flux Balance Analysis (FBA) is chosen<br />
as a suitable approach for studying genome‐scale metabolic network reconstructions of Arabidopsis. FBA<br />
has been successfully implemented and generated some preliminary results. For instance, different effects<br />
and even opposite effects of catalase activity on biomass accumulation were found for catalase located in<br />
different compartments. FBA performed on metabolic network reconstructions also brings new challenges<br />
primarily because constrained‐based reconstruction and analysis (COBRA) methods, including FBA, are only<br />
able to model steady state reaction fluxes. Efforts have already been made to overcome these limitations,<br />
for example, by performing a loopless flux variability analysis (FVA). In conclusion, constraints‐based<br />
genome‐scale modelling provides a suitable framework to assess the influence of light conditions on<br />
Arabidopsis.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 81 / 83
ACKNOWLEDGEMENTS<br />
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
Acknowledgements<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 82 / 83
<strong>NCSB</strong> <strong>2012</strong> symposium<br />
<strong>Abstract</strong> book<br />
The <strong>NCSB</strong> research programme (known under number 050-060-620) is financially supported by the<br />
Netherlands Genomics Initiative (NGI) / Netherlands Organisation for Scientific Research (NWO)<br />
The <strong>NCSB</strong>-AddOn research projects (known under number 050-060-621), which are integrated in the <strong>NCSB</strong><br />
research programme, are financially supported by the Technology Foundation STW.<br />
<strong>NCSB</strong> implements systems biology within the following NGI genomics centres & technological top<br />
institutes: CGC, CBSG, CMSB, KC, NBIC, NISB, and TIFN.<br />
<strong>NCSB</strong><strong>2012</strong>_<strong>Abstract</strong><strong>Book</strong>_<strong>2012</strong>1027.docx page 83 / 83
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