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

细胞生化反应网络理论：理解和利用随机波动

• 批准号: RGPIN-2019-06443
• 负责人: Hilfinger, Andreas
• 金额: $ 2.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738913
• 关键词: Theory; biochemical; reaction; networks; cells

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.

分析随机波动以推断组分的潜在相互作用在物理学中有着悠久的历史。在生命科学中，最近的许多实验工作都集中在测量单细胞中的非遗传变异性，其中随机效应显着影响生化过程。因此，有一个巨大的需求的理论方法来分析和解释复杂的生物系统中的随机波动。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，以及细菌种群中抗生素耐药性的传播。 然而，由于生命系统不运行在热力学平衡，我们不能使用一般的关系，如涨落耗散定理，我们被迫研究每个生物过程单独。这意味着我们必须对所有组件之间的所有交互进行建模，要么猜测许多未知的细节，要么进行全面的近似。这种方法是不可靠的，甚至当理论论文分析相同的实验数据时，也会导致矛盾的答案。我们研究的目标是通过建立适用于整个系统类别的普适属性来解决这一根本挑战，而无需进行大量的显式或隐式假设。我们的第一个主题重点是了解随机波动如何在复杂的生化反应网络中产生，传输和消除的原理。我们将通过分析不同的“网络图案”如何塑造嵌入在未指定的反应网络中的单个组件的随机动力学来实现这一点。例如，我们将得出前馈回路，复合物形成和随机酶-底物相互作用的动力学固有的基本限制和权衡，所有这些都嵌入在任意复杂的调控网络中。在我们的第二个重点，我们认为随机波动不是一个复杂的理解生物过程，但作为一个机会，提取额外的信息。为了实现这一目标，我们将开发一个理论框架，利用自然发生的随机波动作为非微扰工具来探测大型网络中的局部相互作用。该框架的最终成功将是一种算法，该算法仅基于观察到的组分的联合概率分布产生建议的机制和分子反应列表。除了开发新的理论工具，我们还将进行实验合作，并应用我们的新方法来分析来自单细胞实验的高通量显微镜数据。