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的工具，用于提高我们的理解，预测能力以及控制生物系统行为的能力。这种模型通常被编码为化学反应网络（CRN），其中涉及大量反应，这些反应描述了系统的构成，例如蛋白质，随着时间的推移会改变其状态。 CRN模型组件通常仅是部分已知的，因此，将基于理论物理模型的已知组件模型与估计未知组件及其从实验数据中的动态进行混合的方法可以提高其预测能力。这是科学机器学习（SCIML）的领域。该项目将开发新的SCIML方法，用于构建，模拟和分析的CRN模型，包括在蛋白质态的演化中随机性，这是准确预测单个生物细胞内化学系统行为的重要特征。新方法将应用于系统和合成生物学的问题（对天然的理解以及新颖的细胞系统的发展），但也将适用于涉及的CRN（包括流行病学，物理化学和药理学）的广泛领域。通过其行业进入SCIML组织的广泛使用的开源软件库，这些方法将免费使用，任何研究人员都可以使用科学和工程跨越科学和工程的问题。将为博士后科学和一名本科研究人员提供培训机会，他们将获得在跨学科团队中工作的经验，开发SCIML方法，将这些方法集成到开源软件中，并将新软件应用于研究生物系统。该项目扩展了对离子衍生估算器的数学理解，以对量表进行量表的计算（SCIML）进行量表（SCIML）（SCIML）（SCIML）（SCIML）。将SCIML扩展到细胞系统的一个明显困难是它们对噪声行为的倾向，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。对于许多SCIML工作流程，这些过程在非常依赖自动分化（AD）来缩放训练技术方面是有问题的。这是因为以前没有以较低方差估计器的方式将AD应用于它们的通用方法。该项目建立在AD的最新扩展基于离散随机过程的基础上，该过程能够产生无偏差的低方差衍生估计器。将证明为衍生物估计量的生成随机过程与灵敏度方程式给出的衍生概率演变之间的严格联系，从而确立了CRN上下文中衍生物估计量的无偏差和方差的公司理论基础。将证明在细胞模型上部署离散随机AD（DSAD）的可行性，以执行模型校准，并将概括用于模型发现的SCIML通用微分方程方法，将推广到基于理论的数据驱动的CRN中缺失反应的发现。 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 honestly of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.