The Emergence of Systems Biology
Vijay Saraswat, BM TJ Watson Research Center
April 13, 2004

Abstract

The past several years have seen an emerging confluence of computer science and biology, focused on the tremendous opportunities for modeling and symbolic reasoning in biological systems. Fundamentally, one may think of the challenge at hand is to understand the normal and pathological functioning of the Biological Computer -- the intricate network of billions of cause-effect chains that make up life processes. From a computer science point of view, such work is extremely challenging because it requires the integration of discrete change (e.g. as involved in gene expression) with continuously varying phenomena (e.g. Michaelis-Menten reactions), which may possibly be stochastic in nature (e.g. using Gillespie simulation) and may need to be modeled across several orders of magnitude.

Several research groups across the world (e.g. Caltech, Harvard, Princeton, Institute for Systems Biology, U Auckland IBM) are now starting to focus on this area. For instance, researchers are collaborating on the development of the Systems Biology Markup language, and CellML. Several special-purpose simulators are being built for this area (e.g. Cellerator, Gepasi, JDesigner etc). Other researchers are applying ideas from constraint programming e.g. to model alternative gene splicing in HIV.

We will illustrate the basic challenges at hand with a few different examples (specifically HIV, Cell Div), and describe how our own research work on hybrid