Embedded within cells are complex signaling mechanisms that transfer information from one part of a cell to another and intercellular mechanisms that transfer information from one part of a multicellular organism to another. Indeed, signal transduction pathways—and the proteins associated with them—appear to serve the functions of information processing and transfer, rather than those of more “traditional” biology (e.g., chemical transformation of metabolic intermediates or the building of cellular structures).

For example, a simple enzyme protein could be viewed as a computational element that takes an input—the concentration of its “substrate,” the molecule with which it interacts—and produces an output: a concentration of the catalyzed reaction product. An enzyme that becomes active only when it binds to two separate regulator molecules will function something like a Boolean AND gate, and so on. Circuits formed from these elements can be as simple as a switch or an oscillator, or as complex as to drive a bacterium’s chemotaxis response. Indeed, the cell even possesses a kind of short-term, “random-access” memory, in the sense that events in its environment have profoundly shaped the concentration and activity of many thousands of molecules in the cell. In short, these protein-based circuits constitute a kind of nervous system for the cell, providing it with much of what it needs to control its behavior.

Additional insights can be gained from the notion that both computational processes and biological pathways can be viewed as processes that affect the state of a system according to well-defined (though possibly probabilistic) rules. Thus, it is possible to describe regulatory, metabolic, and signaling pathways, as well as multicellular processes such as immune responses, as systems of interacting computations operating in parallel. In particular, languages such as Petrinets, Statecharts, and the Pi-calculus, originally developed for the specification and study of systems of interacting computations, can be used to represent such systems. Such representations enable researchers to simulate their behavior, and to support qualitative and quantitative reasoning on the properties of these systems.

- From
Catalyzing Inquiry at the Interface of Computing and Biology, pp. 207-208