Systems biology, models, and concurrency
Models will play a central role in the representation, storage, manipulation, and communication of knowledge in systems biology. Models capable of fulfilling such a role will likely differ from the familiar styles deployed with great success in the physical sciences. Molecular systems at the basis of cellular decision processes are concurrent and combinatorial. Their behavior is as much constrained by relationships of causality between molecular interactions as it is by chemical kinetics. Understanding how such systems give rise to coherent behavior and designing effective interventions to fight disease will require a notion of model that is akin to the concept of program in computer science. I will discuss recent progress in implementing a platform and tools for formal analysis that bring us closer to this vision. Protein interactions are represented by means of rules expressed in a formal language that captures a very simple, yet effective and biologically meaningful level of abstraction. Models, then, are collections of rules operating on an initial set of agents, in complete analogy to rules of organic chemical reactions. I will describe tools for analyzing and navigating rule collections as well as exploring their dynamics. We draw on concepts familiar to computer science, especially event structures, and adapt them to biological needs with the goal of formalizing the notion of "pathway". The challenges are many, but a roadmap for the future is discernible. Computer science will play a central role in providing an additional foundational layer, both theoretical and practical, that neither physics nor chemistry can offer on their own in the future definition of the biological sciences.
systems biology models concurrency languages experimentation kappa