Principal Investigator Paul Barton
Biological systems have evolved so that they can undertake sophisticated decisions and control complex processes while remaining resilient and flexible. For example, signal transduction cascades provide an important set of pathways that sense and process extracellular signals and trigger internal cellular events. We are collaborating with Bruce Tidor’s laboratory to develop modeling tools to analyze and compare biological networks and classes of networks. Our hypothesis is that because biological networks have evolved to function well in a complex environment, we expect that they are at or near their optimal state among a class of related networks. Therefore, the key to understanding enzyme networks is to elucidate the precise sense in which they are optimal and the class of related networks with respect to which they are optimal. This can be accomplished by posing and solving an optimization problem and comparing the solution with networks that can be observed in nature. We are using this approach with two model systems: mitogen-activated protein (MAP) kinase, an enzyme superfamily that is involved in a variety of signaling pathways; and synthetic genetic networks, a well-defined experimental system composed of DNA regulatory elements that can be used to re-create signal processing units involving feedback control and other mechanisms.