Milo Lin, a physicist and Assistant Professor in the Department of Bioinformatics at the University of Texas Southwestern Medical Center, has developed a "mapping” that, when combined with thermodynamic circuit theory, allows one to transform a biochemical network into an equivalent electrical circuit that obeys Ohm’s law. This allows the theorems and powerful quantitative methods of electrical-network-analysis developed over the past century to be applied to biochemical networks. These electrical engineering tools have -for the electronics industry- enabled large-scale system prediction and design through abstraction and modularity as opposed to simulation of all the components and interactions in a given system. Funds from this grant support efforts by Professor Lin to deploy this framework in the analysis of biochemical systems. Lin will begin by systematically mapping a wide variety of biochemical networks found in organisms (regulatory networks, metabolic pathways, molecular motors, etc.) to equivalent electric circuits and then using electrical engineering tools to obtain the simplest circuit of that type. He will then use computer simulations to “evolve” this simple circuit to meet various targets, including determining circuits necessary and sufficient to execute arbitrary computations, circuits that exhibit robustness to input noise, and circuits that exhibit robustness to changes in the fitness landscape.  Lin will then explore the possible existence of a threshold thermodynamic force above which biomolecular computational capacity is dramatically increased, which, if there is a such a threshold, may shed some light on the puzzling observation that living systems overwhelmingly choose nonequilibrium over equilibrium chemistry for computation. If successful, Lin’s project will facilitate our understanding of complex biochemical networks and therefore of how organisms use chemistry to achieve life-sustaining functions. More speculatively, characterizing the computational capacity of a wide range of biochemical networks may provide insights that allow one to delineate living from nonliving matter.