Information Integration and Energy Expenditure in Eukaryotic Gene Regulation

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Project Abstract Gene regulation – how genes are turned on in the right place, at the right time and in the right amount – is a problem central to most areas of biology and medicine. Our understanding of gene regulation arose from classical studies in bacteria: proteins called “transcription factors” (TFs) bind to regulatory DNA sequences and recruit RNA polymerase (RNAP). The situation in eukaryotes is far more complicated. For example, eukaryotic DNA is packaged around nucleosomes into chromatin and external sources of energy, such as ATP, are used to reorganise chromatin, remodel nucleosomes and post-translationally modify regulatory proteins. Pioneering studies from several laboratories have identified many of the molecular components involved in this regulatory complexity. However, the quantitative concepts used to reason about eukaryotic gene regulation are still largely based on the bacterial paradigm. Our work focuses on addressing this alarming gap. Previously, we developed a strategy of “following the energy” by integrating mathematical models rooted in physics with quantitative and synthetic experiments in the early Drosophila embryo. The fruit fly offers an unrivaled model system for measuring and perturbing gene regulation in a living organism. The mathematics exploits a graph- based approach to Markov processes that permits algebraic calculation of required quantities. This allowed us to identify the functional limits to energy expenditure, while avoiding fitting models to data or numerically simulating differential equations. We have provided strong evidence that energy expenditure away from thermodynamic equilibrium is essential for the functional properties of eukaryotic genes. In the present proposal, we build on this previous strategy. We hypothesize that data from the Drosophila hunchback gene cannot be accounted for by any thermodynamic equilibrium model of regulated recruitment of RNAP, no matter how complicated the molecular details. We believe we can exploit a method of “coarse graining” within the linear framework to establish this remarkably powerful result. We will then extend our experimental methods and modeling beyond regulated recruitment, to analyze the dynamics of RNAP itself and the stochastic production of mRNA. We will introduce real-time imaging of mRNA and optogenetic perturbations of TFs to measure quantitative aspects of gene expression, and will extend our algebraic methods to accommodate such data. We hypothesize that energy expenditure in gene regulation is essential to modulate RNAP dynamics and generate the observed stochastic patterns of hunchback mRNA expression. Our efforts will formulate a new model of hunchback that integrates regulation, energy expenditure, RNAP dynamics and mRNA stochasticity.