Statistical Mechanics of Gene Regulation

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This theoretical research is funded jointly by the Divisions of Materials Research and Molecular and Cellular Biology. Rapid advances in genomic and molecular technology have led to much excitement and opportunities in molecular biology. A hugh amount of data have already been collected on genomic sequences, gene expression profiles, and protein-protein interactions. System-level issues involving gene, protein, and cellular networks have emerged as the new challenges in quantitative understanding of molecular and cellular biology. This research will study system-level issues in the context of gene regulation. An explicit model will be used to describe the combinatoric control of gene expression, and using simple generic interactions of transciption factors with each other and with their DNA building sites. The model will be used to investigate how the molecular components may be put together to implement regulatory functions of increasing complexity, and how these regulatory systems can in turn be coupled to each other to form genetic networks exhibiting robust patterns of gene expression. A key feature of this model is the identification of a subset of interactions that can be tuned by simple manipulation of DNA sequences in the regulatory regions. They are referred to as "programmable" parameters and can be individually adjusted by the system in order to implement the imposed functional requirements. The model can be cast in the form of an interacting spin system, and shares striking similarities with the "recurrent networks" studied in neural networks. It can also be reduced to a more versatile Boolean description in some cases. Analytical and numerical studies will be done to characterize the properties of the networks, e.g., the complexity of the regulatory functions the system can implement, and the capacity of the stable expression patterns the network can maintain. In particular, a stochastic "supervised learning rule" resembling the evolutionary dynamics of regulatory sequences will be studied to search for the programmable parameters of the system that best achieve the desired functional responses. This dynamics can therefore be used also to study the evolvability of the regulatory control systems and the gene networks. The evolution of the stability and complexity of these regulatory and interactive gene systems will also be studied.%%%This theoretical research is funded jointly by the Divisions of Materials Research and Molecular and Cellular Biology. Rapid advances in genomic and molecular technology have led to much excitement and opportunities in molecular biology. A hugh amount of data have already been collected on genomic sequences, gene expression profiles, and protein-protein interactions. System-level issues involving gene, protein, and cellular networks have emerged as the new challenges in quantitative understanding of molecular and cellular biology. This research will study system-level issues in the context of gene regulation.***