eMB: Collaborative Research: Discovery and calibration of stochastic chemical reaction network models

eMB：协作研究：随机化学反应网络模型的发现和校准

• 批准号: 2325184
• 负责人: Alan Edelman
• 金额: $ 37.5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325184&HistoricalAwards=false
• 关键词: eMB; Collaborative; Research; Discovery; calibration

Mathematical models are a widely used tool for improving our understanding, ability to predict, and ability to control the behavior of biological systems. Such models are often encoded as chemical reaction networks (CRNs), involving large collections of reactions that describe how constituents of the system, such as proteins, change their states over time. CRN model components are often only partially known, and thus methods which mix theoretical physics-based models for known components, with techniques that estimate unknown components and their dynamics from experimental data, can improve their predictive capabilities. This is the domain of Scientific Machine Learning (SciML). This project will develop new SciML methods for constructing, simulating, and analyzing CRN models that include randomness in the evolution of protein states, an important feature to accurately predict the behavior of chemical systems within individual biological cells. The new methods will be applied to problems in systems and synthetic biology (the understanding of native, and the development of novel, cellular systems), but will also be applicable across a wide range of fields involving CRNs (including epidemiology, physical chemistry, and pharmacology). Via their incorporation into widely used open source software libraries of the SciML organization, the methods will be freely available for use by any researcher studying problems across science and engineering. Training opportunities will be provided for a postdoctoral scholar and an undergraduate researcher, who will gain experience working in interdisciplinary teams, developing SciML methods, integrating these methods into open source software, and applying the new software to study biological systems.This project extends the mathematical understanding of discrete stochastic derivative estimators to facilitate scaling Scientific Machine Learning (SciML) training techniques to chemical reaction networks (CRNs). One distinct difficulty in extending SciML to cellular systems is their proneness to noisy behaviors, as they are modeled as discrete stochastic jump processes via stochastic simulation algorithms such as the Gillespie method. Such processes are problematic for many SciML workflows which critically depend on automatic differentiation (AD) to scale training techniques. This is because there previously existed no general method for applying AD to them in an unbiased manner with low variance estimators. This project builds on a recent extension of AD to discrete stochastic processes which is capable of generating unbiased low variance derivative estimators. The rigorous connection between the generated stochastic process for the derivative estimator and the derivative probability evolution given by the sensitivity equations will be proven, thus establishing a firm theoretical underpinning for the unbiasedness and variance of the derivative estimator in the context of CRNs. The feasibility to deploy discrete stochastic AD (DSAD) on cellular models to perform model calibration will be demonstrated, and SciML universal differential equation methods for model discovery will be generalized to the theory-based data-driven discovery of missing reactions in CRNs. Finally, the applicability of these methods will be demonstrated on a range of cellular systems (including the B-cell antigen receptor signaling system, the σV lysozyme stress response system, and a mixed feedback oscillator).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.

数学模型是一种广泛使用的工具，用于提高我们对生物系统行为的理解、预测和控制能力，此类模型通常被编码为化学反应网络 (CRN)，即描述化学成分如何变化的大量反应。系统（例如蛋白质）会随着时间的推移而改变其状态，因此，将已知成分的基于理论物理的模型与根据实验数据估计未知成分及其动态的技术相结合的方法可以得到改进。这是他们的预测能力的领域。科学机器学习 (SciML)。该项目将开发新的 SciML 方法，用于构建、模拟和分析 CRN 模型，其中包括蛋白质状态演化的随机性，这是准确预测单个新生物细胞内化学系统行为的重要特征。方法将应用于系统和合成生物学中的问题（对天然的理解和新颖的开发，涉及细胞系统），但也将适用于 CRN 的广泛领域（包括流行病学、物理化学和药理学） ）。 通过这些方法将被纳入 SciML 组织广泛使用的开源软件库中，任何研究科学和工程问题的研究人员都可以免费使用这些方法，并将为博士后学者和本科生研究人员提供培训机会，他们将获得经验。在跨学科团队中工作，开发 SciML 方法，将这些方法集成到开源软件中，并应用新软件来研究生物系统。该项目扩展了对离散随机导数估计器的数学理解，以促进将科学机器学习 (SciML) 训练技术扩展到化学反应网络（CRN）。将 SciML 扩展到细胞系统的一个明显困难是它们容易出现噪声行为，因为它们通过随机模拟算法（例如 Gillespie 方法）建模为离散随机跳跃过程，这样的过程对于许多严重依赖的 SciML 工作流程来说是有问题的。这是因为以前不存在使用低方差估计器以无偏方式将 AD 应用于它们的通用方法。能够生成无偏低方差导数估计量的随机过程将被证明，生成的导数估计量的随机过程与灵敏度方程给出的导数概率演化之间的严格联系，从而为无偏性和方差建立坚实的理论基础。将演示在 CRN 背景下部署离散随机 AD (DSAD) 来执行模型校准的可行性，并且将演示用于模型发现的 SciML 通用微分方程方法。最后，这些方法的适用性将在一系列细胞系统（包括 B 细胞抗原受体信号系统、σV 溶菌酶应激反应系统、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。