Data Integration Aand Modelling: On a New Design to enable Systems biology (DIAMONDS)

We will demonstrate the power of a Systems Biology approach to study the regulatory network structure of the most fundamental biological process in eukaryotes: the cell cycle. An integrative approach will be applied to build a basic model of the cell cycl e, in four different species including S. cerevisiae (budding yeast), S. pombe (fission yeast), A. thaliana (weed, model plant) and human cells. To do this, a Consortium is assembled of leaders in the fields of cell cycle biology, functional genomics tech nologies, database design and development, data analysis and integration technology, in addition to modelling and simulation approaches. The project combines a number of complementary data sets, toward an advanced mining and modelling environment designed to assist the biologist in building and amending hypotheses, and to help the investigator when designing new experiments to challenge these hypotheses. By doing this simultaneously in widely different organisms we will ensure that the tools are generally applicable across species. By bringing together in the design phase biologists, bioinformaticians, bio-mathematicians and (commercial) software developers we will ensure that a user-friendly, intuitive data analysis environment is created. The main data streams generated de novo within the project concern transcript profiling and proteomics data (Y2H and TAP). These data will be complemented with information extracted through comparative genomics, and prior knowledge coming from literature mining (text m ining tools). The project will bring together a number of existing technologies to build a knowledge warehouse in a relational database designed to contain cell cycle regulatory network information, accessible through an intuitive user platform (GUI) with embedded modelling tools. This platform will enable both top-down and bottom-up hypothesis-driven research, and it will serve as a basis to develop more rigorous dynamical models for cell cycle variants.