Luca Cardelli
Microsoft Research

Living cells are extremely well-organized autonomous systems, consisting of discrete interacting components. Key to understanding and modeling their behavior is modeling their system organization. Four distinct chemical toolkits (classes of macromolecules) have been characterized, each combinatorial in nature. Each toolkit consists of a small number of simple components that are assembled (polymerized) into complex structures that interact in rich ways. Each toolkit abstracts away from chemistry; it embodies an abstract machine with its own instruction set and its own peculiar interaction model. These interaction models are highly effective, but are not ones commonly used in computing or concurrency theory (or mathematics): proteins stick together, genes have fixed output, membranes carry activity on their surfaces. "Systems biology" consists, largely, in understanding how these interaction models work, separately and together. To that end, biologists have invented a number of notations attempting to describe, abstractly, these abstract machines and the processes and networks they implement. I discuss the notations currently used by biologists, and the advantages of using programming language (process calculus) approaches. The long-term goal is to represent the structure and function of biological systems via formal languages, for description, simulation, analysis and (eventually) synthesis.

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