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

通过分析随机波动来推断组分之间潜在的相互作用在物理学中有着悠久的历史。在生命科学中，最近的实验工作主要集中在测量单细胞的非遗传变异性，其中随机效应显著影响生化过程。因此，对分析和解释复杂生物系统中的随机波动的理论方法有巨大的需求。将观察到的细胞间变异性与潜在的分子相互作用可靠地联系起来，对于理解由随机效应形成的许多关键细胞过程是必要的，例如干细胞的分化、癌细胞对药物治疗的反应以及细菌群体中抗生素耐药性的传播。

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.