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DIAMONDS<br />
Dedicated Integration and Modelling<br />
of Novel Data and Prior Knowledge<br />
to Enable Systems Biology<br />
Summary<br />
We will demonstrate the power of a systems biology<br />
approach to study the regulatory network structure of the<br />
most fundamental biological process in eukaryotes: the cell<br />
cycle. An integrative approach will be applied to build<br />
a basic model of the cell cycle in four diff erent species<br />
including S. cerevisiae (budding yeast), S. pombe (fi ssion<br />
yeast), A. thaliana (weed, model plant) and human cells. To<br />
do this, a consortium has been assembled of leaders in the<br />
fi elds of cell cycle biology, functional genomic technologies,<br />
database design and development, data analysis and<br />
integration technology, as well as modelling and simulation<br />
approaches. The project combines a number of complementary<br />
data sets toward an advanced mining and modelling<br />
environment, designed to assist the biologist in building<br />
and amending hypotheses, and to help the investigator<br />
when designing new experiments to challenge these<br />
hypotheses. By doing these simultaneously in widely diff erent<br />
organisms, we will ensure that the tools are generally<br />
applicable across species. By bringing together biologists,<br />
bioinformaticians, biomathematicians and (commercial)<br />
software developers in the design phase, we will ensure<br />
that a user-friendly, intuitive data analysis environment is<br />
created. The main data streams generated de novo within<br />
the project concern transcript profi ling and proteomics<br />
data (Y2H and TAP). These data will be complemented with<br />
information extracted through comparative genomics, and<br />
prior knowledge coming from literature mining (text mining<br />
tools). The project will bring together a number of existing<br />
technologies to build a knowledge warehouse in a relational<br />
database designed to contain cell cycle regulatory network<br />
information, accessible through an intuitive user platform<br />
(GUI) with embedded modelling tools. This platform will<br />
enable both top-down and bottom-up hypothesis-driven<br />
research, and will serve as a basis to develop more rigorous<br />
dynamical models for cell cycle variants.<br />
Problem<br />
Cell division is regulated by highly conserved genetic networks.<br />
Occasionally the cell division machinery becomes<br />
unstable, resulting in an uncontrolled proliferation of cells. In<br />
humans, the uncontrolled division and growth of cells can<br />
lead to cancer. Cell division is also at the core of biomass<br />
production and agricultural yield. A better understanding of<br />
26<br />
Keywords | Functional genomics | systems biology | regulatory networks | dynamical modelling |<br />
the processes that regulate cell division is therefore of prime<br />
importance for human health, welfare and sustainable<br />
development. The approach taken by DIAMONDS constitutes<br />
a pioneering step toward the application of a systems<br />
biology approach for genetic network analysis, and as such<br />
will contribute to the maturation of systems biology into<br />
a general approach, complementary to the traditional geneby-gene<br />
approach. With the rapid increase in genome<br />
sequences, published literature and databases on proteomic<br />
and transcriptomic data, it is obvious that integrative analysis,<br />
bringing together various complementary data types<br />
for the identifi cation of network motifs, should be tested<br />
on well-defi ned biological models to assess the potential of<br />
a systems biology approach.<br />
Aim<br />
The overarching objective of this multidisciplinary project is<br />
to demonstrate the power of a systems biology approach to<br />
study fundamental biological processes. We focus on eukaryotic<br />
cell cycle regulation, and will develop and implement<br />
a computational model that will function as a hypothesisgenerating<br />
engine in a systems biology ‘wet-lab’ environment.<br />
The work will be done in a number of wet-labs and dry labs,<br />
on yeast, plant and human cells, to make sure that the<br />
approach is validated across widely diff erent organisms. The<br />
main target of the project consists of two parts:<br />
• a cell cycle knowledge base and an integrated platform<br />
of data mining, modelling and simulation tools that will<br />
allow the integrated analysis of that data in a systems<br />
biology approach;<br />
• the development of a basic model, the use of this model<br />
to design new experiments, the production and analysis<br />
of novel data, and the integration of these into a refi ned<br />
model.<br />
The knowledge base and tools will be made available and<br />
introduced to the European research community.<br />
The principle method to reach this target is to harvest and/<br />
or produce a large body of cell cycle-related biological<br />
knowledge. This will function as the central resource for the<br />
modelling and simulation environment that will be developed.<br />
As mentioned above, the knowledge warehouse will<br />
constitute one of the major deliverables of the project, enabling<br />
future hypothesis-driven research. The project will<br />
showcase the fact that a systems biology approach towards<br />
analysis of a fundamental biological process can in fact<br />
become mature today, and hinges on an integrated data<br />
analysis pipeline, extended with modelling and simulation<br />
tools. The essential elements of such a pipeline will be:<br />
functional genomics data production (transcriptome and<br />
proteome); literature mining; comparative analysis of genes<br />
and networks; a visualisation, modelling and simulation environment,<br />
and a web service-based data integration layer.<br />
CANCER RESEARCH PROJECTS FUNDED UNDER THE SIXTH FRAMEWORK PROGRAMME