EAGER: Formal Analysis of Stochastic Models in Systems Biology Under Uncertainty

EAGER：不确定性下系统生物学随机模型的形式分析

• 批准号: 2227898
• 负责人: Krishnendu Ghosh
• 金额: $ 25万
• 依托单位: College of Charleston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227898&HistoricalAwards=false
• 关键词: EAGER; Formal; Analysis; Stochastic; Models

Biological system models comprise of multiple biological processes that are stochastic and often, multiscale, processes executing at different time scales such as communication of molecular processes with cellular processes. Formal methods such as model checking have been used as a querying mechanism to understand the details of communication of biological processes. The imprecision and incompleteness of experimental data in system level modeling of biological processes necessitates construction of novel theoretical framework and algorithms for formal analysis. The project is timely and appropriate in addressing the challenges of the state explosion problem in model checking combined with construction of multiscale and stochastic models in biology under uncertainty. The construction of novel model reduction algorithms for model checking of multiscale and stochastic systems becomes essential for a rigorous evaluation of queries represented in temporal logic. The goal of this project is to create a solid theoretical framework for formal analysis of multiscale models of stochastic systems and develop innovative models for efficient querying mechanisms for biological processes under uncertainty. Tools developed in the project will be open-sourced. The interdisciplinary nature of the research will provide opportunities for undergraduates for cross-fertilization of ideas between computer science, mathematics, chemistry and biology.The impact of proposed work will push the limits in modeling a large system under uncertainty for rigorous formal analysis and modeling in systems biology. The goal of this project is pursued by the aims, i) Develop theoretical formalism representing multiscale processes in stochastic systems under uncertainty for formal analysis and ii) Create and evaluate efficient algorithms for querying on models of stochastic systems under uncertainty. Models of stochastic models under uncertainty will be created and evaluated. The novel framework will provide succinct and precise models of biological processes when combined with experimental data. The project will be conducted at a primarily undergraduate institution (PUI). Educational materials from the project will be used at different levels of undergraduate curriculum. The project will provide research experiences through focused mini-course projects in undergraduate classes with the goal of developing interdisciplinary researchers.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.

生物系统模型包括多个生物过程，这些过程是随机的，并且通常是多尺度的，在不同时间尺度上执行的过程，例如分子过程与细胞过程的通信。 形式化的方法，如模型检测已被用作一种查询机制，以了解生物过程的通信细节。在生物过程的系统级建模中，实验数据的不精确性和不完整性要求建立新的理论框架和算法进行形式化分析。该项目是及时和适当的，在解决模型检验的状态爆炸问题的挑战，结合在不确定性下的生物学多尺度和随机模型的建设。构造新的模型约简算法，用于多尺度和随机系统的模型检测，对于时态逻辑中表示的查询的严格评估变得至关重要。该项目的目标是为随机系统的多尺度模型的形式化分析创建一个坚实的理论框架，并为不确定性下的生物过程开发高效查询机制的创新模型。该项目开发的工具将是开源的。该研究的跨学科性质将为本科生提供计算机科学、数学、化学和生物学之间思想交叉的机会。拟议工作的影响将推动在不确定性下对大型系统进行建模的极限，以进行严格的形式化分析和系统生物学建模。该项目的目标是追求的目标，i）发展理论形式主义表示多尺度过程的随机系统下的不确定性进行形式化分析和ii）创建和评估有效的算法查询模型的随机系统下的不确定性。将创建和评估不确定性下的随机模型。当与实验数据相结合时，新的框架将提供简洁和精确的生物过程模型。该项目将在一个主要的本科院校（PUI）进行。该项目的教育材料将用于不同层次的本科课程。该项目将通过重点微型课程项目在本科班提供研究经验，以培养跨学科的研究人员为目标。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。