Theory of biochemical reaction networks in cells: understanding and exploiting stochastic fluctuations

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Analyzing stochastic fluctuations to infer underlying interactions of components has a long history in physics. In the life sciences much recent experimental work has focused on measuring non-genetic variability in single cells where stochastic effects significantly affect biochemical processes. As a result, there is an enormous demand for theoretical approaches to analyze and interpret stochastic fluctuations in complex biological systems. Reliably relating the observed cell-to-cell variability to underlying molecular interactions is necessary to understand many key cellular processes shaped by stochastic effects, such as the differentiation of stem cells, the response of cancer cells to drug treatment, and the spread of antibiotic resistance in bacterial populations. ******However, because living systems do not operate at thermodynamic equilibrium we cannot use general relations like the Fluctuation-Dissipation-Theorem and we are forced to study each biological process individually. That means we have to model all interactions between all the components either guessing many unknown details or making sweeping approximations. This approach is unreliable and has led to contradictory answers even when theoretical papers analyze the same experimental data.******The goal of our research is to address this fundamental challenge by establishing universal properties that apply to entire classes of systems without making a large number of explicit or implicit assumptions. Our first thematic focus is to understand the principles of how stochastic fluctuations are generated, transmitted, and eliminated in complex biochemical reactions networks. We will do so by analyzing how different "network motifs" shape the stochastic dynamics of individual components embedded within unspecified reaction networks. For example, we will derive fundamental limits and trade-offs inherent to the dynamics of feed-forward loops, complex formation, and stochastic enzyme-substrate interactions, all embedded within arbitrarily complex regulatory networks.******In our second focus we consider stochastic fluctuations not as a complication to understand biological processes but as an opportunity to extract additional information. To accomplish that we will develop a theoretical framework that exploits naturally occurring stochastic fluctuations as a non-perturbative tool to probe local interactions within large networks. The ultimate success of this framework will be an algorithm that produces a list of suggested mechanisms and molecular reactions based solely on the observed joint probability distributions of components. In addition to developing new theoretical tools we will pursue experimental collaborations and apply our new methods to analyze high-throughput microscopy data from single-cell experiments.