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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746968
• 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.

分析随机波动来推断成分之间的潜在相互作用在物理学中有着悠久的历史。在生命科学领域，最近的许多实验工作都集中在测量单细胞的非遗传变异性，其中随机效应显着影响生化过程。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着巨大的需求。将观察到的细胞间变异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抗生素耐药性的传播在细菌群体中。 然而，由于生命系统不在热力学平衡下运行，我们无法使用波动耗散定理等一般关系，我们被迫单独研究每个生物过程。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，即使理论论文分析相同的实验数据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通用属性来解决这一基本挑战，而无需做出大量显式或隐式假设。我们的第一个主题重点是了解复杂生化反应网络中随机波动如何产生、传播和消除的原理。我们将通过分析不同的“网络基序”如何塑造嵌入未指定反应网络中的各个组件的随机动力学来做到这一点。例如，我们将得出前馈循环、复杂形成和随机酶-底物相互作用的动态固有的基本限制和权衡，所有这些都嵌入在任意复杂的调节网络中。在我们的第二个重点中，我们认为随机波动不是理解生物过程的复杂因素，而是提取额外信息的机会。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内的局部相互作用。该框架的最终成功将是一种算法，该算法仅根据观察到的组件联合概率分布生成建议机制和分子反应列表。除了开发新的理论工具外，我们还将寻求实验合作并应用我们的新方法来分析单细胞实验的高通量显微镜数据。