Elucidating ‘design’ principles for engineering synthetic protein networks

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Through natural selection, cells have evolved protein networks to perform essential functions such as cell growth, differentiation and death. Evolution has converged on general network 'designs' capable of functioning precisely in the context of variability in intracellular and extracellular conditions (e.g. availability of metabolites and the concentrations and specificity of proteins). The uncovering of these ‘design’ principles will allow first, a deeper understanding of natural biological systems and second, insight into engineering synthetic protein networks for programming cells to perform a wide variety of tasks (e.g. biosensor and bioremediation applications). While synthetic biologists have uncovered many ‘design’ principles in transcriptional networks, protein networks differ by operating at faster time scales (e.g. ms to s) and wider spatial distribution (e.g. membrane morphologies), requiring unique ‘design’ principles. My long term research objective is to elucidate general ‘design’ principles for engineering protein networks through combined computational and experimental approaches. The first part of the proposal focuses on the development of computational tools needed for the molecular design of protein switches and the simulation of their interactions with natural protein networks. For molecular design, we will further develop our computational tool for generating protein conformational spaces to include protein backbone flexibility and thematic functions. The tool rapidly samples the conformational space of protein switches in the relevant time scales (e.g. ms to s) by deferring expensive energy calculations. For formulation of ‘design’ principles, we will further develop our computational tool for simulating protein networks to include millions of molecules in spatially distinct regions (e.g. membrane morphologies). The tool will simulate large scale biomolecular interactions within the constraints of complex spatially distinct regions that will be defined using a polygon-based model. In the second part, we will use these tools to first model and then experimentally validate 'design' principles related to accurate signal propagation in protein networks. Protein networks accurately propagate their signals despite the fact that many proteins have low specificity to their substrates. The caspase protein network for cell death is a good model system because caspases are notoriously non-specific to their target substrates, but yet distinct paths to cell death are achieved. Furthermore, caspases are curiously found in many spatially distinct regions. To explain the caspase mystery, we will formulate and test a ‘design’ principle for accurate signal propagation that uses non-specific local activity on certain local substrates to drive specific global activity. Spatiotemporally localized caspase signals will be generated experimentally by light-activated caspases invented by our group. Our discoveries of 'design' principles for protein networks will allow their application in engineering synthetic networks for reliably programming cells to perform a diverse set of tasks. Imagine programming a cell that detects, encapsulates and consumes environmental toxins. Once completed, the cell dies leaving no genetic trace. This proposal will develop the computational and experimental tools for modeling and testing 'design' principles, respectively, that will help realize this dream.