visualisation and analysis of high dimensional data and complex ...

BioLayout Express 3D
visualisation and analysis of high dimensional
data and complex systems
Prof Tom Freeman
Systems Immunology Group,
The Roslin Institute,
University of Edinburgh

functional genomics
and systems biology
paradigm
model
manipulate
HCS
mine
measure

PLoS Comp Biol. 3:2032-42 (2007)
Nature Protocols, 4:1535-50 (2009)
BioLayout Express 3D Team
Tom Freeman, Thanasis Theocharidis,
Tim Angus, Derek Wright
The Roslin Institute
www.biolayout.org
Anton Enright, Stijn van Dongen, +1
EMBL-EBI

current status
• open source program, now on 12 th version
• development funded by BBSRC until Oct. 2013, further funding obtained
(until Oct 2014) with EMBL-EBI for use in analysis of NGS data
• program used approx. 4,000 time per month by about 500 users worldwide,
web site accesses ~3,000 per month
applications in analysis of
• expression data
• proteomics data
• multi-parameter FACs data
• DNA sequence analysis
• pathway modelling and simulation

current capabilities
• OS-independent, multi-platform compatibility (Windows, Mac, Linux).
• available as installer version, webstart or platform independent (.jar) file
• supports input of multiple file types:
‣ text (.txt)
‣ simple interaction (.sif, tgf)
‣ layout files (.layout)
‣ expression files (.expression)
‣ matrix (.matrix)
‣ yEd GraphML files (.graphml)
• interactive rendering of very large network graphs (60,000 nodes, millions
of edges) in 2/3D
• built in clustering and exploration capabilities
• graph animation
• full support for mEPN pathway notation scheme
• implementation of modified Signaling Petri Net (SPN) algorithm

high dimensional data
100’s columns, thousands rows
BioLayout Express 3D
analysis pipeline for high
dimensional data
calculate
correlation matrix
Layout, dynamic visualisation and
clustering of graphs (10’s thousands
nodes, millions of edges) in 2D or 3D
minutes
data integration and exploration

http://www.macrophages.com/pig-atlas
http://biogps.org/

Modelling of Pathway Knowledge
Molecular Interaction Map of a Macrophage
Oda K, Kimura T, Matsuoka Y, Funahashi A, Muramatsu M, Kitano 2004

Sources of ‘Pathway Data’
the literature (reviews)
pathway databases
• KEGG
• NetPath
• Upstate
• Panther
• Reactome
• WikiPathways
• GenMapp…….
for comprehensive list see:
http://www.pathguide.org/
interaction databases
• BIND
• String
• Ingenuity……
gene lists
• pathway membership – databases/array designs/reviews

4 views of apoptosis
need for a standardised approach for presenting and modelling biological
pathways

http://www.sbgn.org

network modelling of biological pathways and systems
aim
to construct models of the current consensus view of the biological pathways
underpinning the biological systems, particularly the macrophage and its role in
the innate immune response
pathway models should:
• logically depict molecular interactions using standardised notation
• unambiguously depict the identity of components
• depict state transitions of components
• capture the semantics of relationships between components of the pathway as
described in the literature
• be useful in the analysis of genomics data and hypothesis generation
• provide a resource for the computational modelling of pathways
• easily understood and constructed by biologists

IFNB Pathway Diagram
Interferon B (IFNB1) is a cytokine released from many cell
types in response to immune stimulation. It homodimerises
and binds to its cell surface receptor complex composed of
the receptor proteins IFNAR1 and IFNAR2 and the
intracellular kinases TYK2 and JAK1. The complex is
composed of 2 of each of these proteins. Binding causes a
conformation change in the complex resulting in the
autophosphorylation of JAK1. Once activated the complex
catalyses the phosphorylation of STAT2 which forms a
heterodimer with STAT1. This complex then binds
interferon regulatory factor 9 (IRF9) forming the complex
often referred to as ISGF3 and translocates to the nucleus.
Here it binds to the IRF sequence in the promotor of a
number of genes including IRF2, IL12B, STAT1, IL15,
TAP1, GBP1, PSMB9, SOCS3. In turn SOCS3 inhibits the
atuophosphorylation of the receptor thereby inhibiting
further activation.

Freeman et al., BMC Systems Biology 4:65, 2010

Framework Map of Macrophage
Signalling Pathways
140 proteins, 99 complexes, 44 genes
285 interactions
Raza et al., BMC Systems Biology. 2:36-51, 2008

non-TLR pathogen sensing
antigen presentation, proteosome, Ub
oxidative
phosphorylation
NF-kB
cell cycle
TLR, kinase cascades
glucorticoid
system
cholesterol metabolism

Integrated View of
Macrophage Activation and
Effector Pathways
Raza et al., BMC Systems Biology 4:63 2010
current activities
• mapping of new pathway systems based on
literature and transcriptional signatures
• better integration of transcription factor
networks/gene expression networks
• mapping to SBGN/SBML
2,031
Nodes
2,494
Edges

analysis and visualization of rule-based stochastic flow
through biological pathways
objectives
• to explore the possibility of
using the pathway diagrams
constructed by us as a resource
for pathway simulation
modelling
• to model pathways activity using
a method that is scalable and
does not require knowledge of
kinetic parameters

Petri nets
• Petri nets were first proposed by Carl Petri in 1938
• they are a formal, graphical, executable technique for
the specification and analysis of concurrent, discreteevent
dynamic systems
• they bipartite graphs composed of places, transitions
and edges (bipartite graphs - place-transition-place)
• a place generally represents an entity or in our case a
pathway component
• a transition represents a process between places and
can be used to model dependencies between places
(AND/OR)
• an edge depicts the relationships between on place and
another via a transition. All are the same with the
exception of inhibition edges

Signaling Petri Net: stochastic flow simulation
• the Signaling Petri Net (SPN) is a new algorithm
proposed for use in simulations of biological pathways
see: Ruths et al. PLoS Comp Biol. 4:76 (2008)
• the algorithm models the stochastic flow of a variable
number of tokens
• it doesn't need kinetic parameters of
reactions/transitions
• fast computational times

Conversion of mEPN concepts to Petri net

graphical and computation pathway notation system (mEPN v2.0, 2012)

starting assumptions
•the ‘rate’ of any given reaction in vivo is not known and can not be
determined. Even if this was not the case, individually these factors would
have little affect on the overall dynamics of the system (see below).
•activity ‘flow’ through biological pathways is primarily determined by the
connections between components i.e. the network topology and the amount of
any given component
•the pathway network forms the basis of the model and the amount of any
given component can be measured or inferred
•biological systems are stochastic i.e. is one whose behaviour is nondeterministic
and based on random events
•the model may be incorrect and these assumptions may only be
approximations of the truth

Screenshot 1

Screenshot 2

modelling and visualization of stochastic flow through
large network systems
1. pathway models drawn in yEd graph
editor, parameterized and saved as
.graphml files
2. models imported into
BioLayout and SPN
algorithm used to calculate
time-dependent stochastic
flow through network
3. the results of flow simulations can
be visualised as graphs (mouse-over
function) or viewed as real-time
animations where the size and
colour of nodes is used to represent
their activity

BioLayout Express 3D Team
Roslin Institute
Tom Freeman
Thansis Theocharidis
Ben Boyer
Tim Angus
Derek Wright
www.biolayout.org
EMBL-EBI
Anton Enright
Stijn van Dongen