CAREER: Stochastic Biochemical Network Processes in Cellular Commitment to Fate

在分子水平上，细胞是一个拥挤的地方，有无数的方式，分子碰撞和相互作用，为基本的生化过程提供基础。一个错综复杂的生化反应网络位于所有细胞反应的核心，引导每个细胞走向特定的命运。然而，分子的数量和它们的局部微环境从一个细胞到另一个细胞处于恒定的波动中，从而引起每个细胞对相同信号的多种潜在响应。受随机性（有时称为“噪音”）支配的生化过程中，细胞群体的持久行为是如何产生的？细胞如何在如此嘈杂的环境中做出何时生存何时死亡的重大决定？ 这项工作有助于我们了解这个具有挑战性的问题，探索如何在网络驱动的细胞过程中的噪音可以影响细胞的命运承诺。具体来说，我们将探讨蛋白质数量的变化以及内在的化学反应噪声如何导致不同的结果。 这项工作的成功结果将为理解生化反应网络作为概率而不是确定性过程奠定基础，并使我们能够开发新的理论来解释噪声在细胞群体行为及其相关决策过程中的作用。该奖项还将为培养下一代定量生物学家提供重要资源，这些生物学家将在化学，物理，生物学和计算的界面上获得经验。随着现代技术在单细胞分辨率上的应用，我们了解到随机性是细胞过程中普遍存在的特征。尽管有证据表明细胞群体中存在非遗传细胞反应变异性，但单细胞实验的机制解释通常诉诸于确定性的、不存在的“平均细胞”来描述网络驱动的生化机制。因此，随机分子过程在生化网络和细胞对命运的承诺中的作用知之甚少。这个缺点不是由于缺乏物理化学理论来描述细胞过程，而是由于与受分子噪声影响的细胞网络驱动过程的模拟相关的计算和统计挑战，以及完全捕获随机现象所需的数据采集。该奖项的总体目标是开发细胞过程的机械解释，可以解释分子噪声如何影响生化网络中的信息流，并预测由于生化线索导致的网络驱动执行。为了实现这些目标，这项工作利用高性能计算方法，再加上贝叶斯统计和随机反应动力学，以获得对噪声如何影响生化网络中信号处理的基本理解。具体来说，这项工作将探讨如何从外在（例如基因表达）和内在（例如复合物形成）分子来源的随机性有助于细胞反应的变异性和细胞群体的结果。这项工作的重点是细胞凋亡的执行机制，以解释非遗传细胞异质性的程序性细胞死亡线索的反应。 这项工作还将确定有助于细胞凋亡中异质性细胞反应的分子来源。从这项工作中获得的知识可以改变我们对细胞命运承诺的理解，并推广到其他领域。该奖项提供的培训、传播和合作机会将确保该工作对生物学的多个领域产生重大影响，并为培训下一代定量生物学家提供环境。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

At the molecular level, the cell is a crowded place and there are myriad ways that molecules collide and interact to provide the underpinnings for essential biochemical processes. A tangled web of biochemical reactions lies at the core of all cellular responses that guide each cell toward a specific fate. However, the number of molecules and their local microenvironment are in constant fluctuation from one cell to another, giving rise to multiple potential responses from each cell to the same signal. How is it that persistent cell-population behaviors emerge from biochemical processes that are governed by randomness (sometimes called "noise")? How do cells make such monumental decision of when to live and when to die in such a noisy environment? This work contributes to our understanding of this challenging question by exploring how noise in network-driven cellular processes can affect cellular commitment to fate. Specifically, we will explore how changes in the number of proteins, as well as intrinsic chemical reaction noise, contribute to different outcomes. A successful result from this work will lay the foundation to understand biochemical reaction networks as probabilistic rather than deterministic processes and enable us to develop novel theories to explain the role of noise in cell-population behaviors and their associated decision processes. This award will also provide significant resources to train the next generation of quantitative biologists who will gain experience at the interface of chemistry, physics, biology and computation. As modern technologies are brought to bear at single-cell resolution, we are learning that stochasticity is ubiquitous feature in cellular processes. Despite evidence for non-genetic cell-response variability in cell populations, mechanistic interpretation of single-cell experiments typically appeal to a deterministic, non-existent "average cell", to describe network-driven biochemical mechanisms. Therefore, the role of stochastic molecular processes in biochemical networks and cellular commitment to fate is poorly understood. This shortcoming is not due to a lack of physicochemical theories to describe cellular processes, but rather to the computational and statistical challenges associated with the simulation of cellular network-driven processes subject to molecular noise, and the acquisition of data necessary to fully capture stochastic phenomena. The overarching goal of this award is to develop a mechanistic interpretation of cellular processes that can explain how molecular noise affects information flow in biochemical networks and predicts network-driven execution due to biochemical cues. To attain these goals, the work leverages high performance computing approaches, coupled with Bayesian statistics, and stochastic reaction kinetics to gain a foundational understanding of how noise impacts signal-processing in biochemical networks. Specifically, the work will explore how stochasticity from extrinsic (e.g. gene-expression) and intrinsic (e.g. complex formation) molecular sources contribute to cell-response variability and cell-population outcomes. The work focuses on apoptosis execution mechanisms, to explain non-genetic cellular heterogeneity in the response to programmed cell death cues. The work will also identify molecular sources that contribute to heterogeneous cellular response in apoptosis. The knowledge gained from this work could shift existing paradigms for our understanding of cellular commitment to fate, generalizable to other areas. The opportunities for training, dissemination, and collaboration afforded by this award will ensure that the work will have significant impact across multiple areas of biology as well as provide the environment to train the next generation of quantitative biologists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.