Tripods+X:Res: Collaborative Research: Identification of Gene Regulatory Network Function from Data

Tripods X:Res：协作研究：从数据中识别基因调控网络功能

• 批准号: 1839294
• 负责人: Konstantin Mischaikow
• 金额: $ 19万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839294&HistoricalAwards=false
• 关键词: Tripods+X; Res; Collaborative; Research; Identification

Molecular and cellular biology are at the forefront of our quest to understand life and improve human health. However, the details of how genes and signals interact in real time to produce cell behavior, are not well understood. The problem is a lack of approaches that can integrate experimental data into models that describe the important behavior of the system, yet do not describe nonessential details, that would make it too cumbersome to compute in a reasonable time. This project will develop a new set of tools, that can predict the entire range of behaviors of a network of interdependent genes and do it so efficiently that behavior under the effect of a few mutations or behavior of alternative sets of genes can be readily explored. Furthermore, once such a description of behavior of a set of genes is available, it will be shared and used by other scientists. Continuous build-up of these results will rapidly increase their value for data science, cellular biology, and broader science. Molecular and cellular biology have witnessed a huge leap forward since science has acquired the ability to sequence genomes. However, while genomes are relatively static, the phenotypes result from the interaction of many genes that are expressed dynamically in time. Furthermore, since phenotypes arise from complex transcriptional networks, where the interactions tend to be nonlinear, data-based models will play a key role in understanding and ultimately controlling these phenotypes. Current modeling techniques, motivated by physics, struggle to bridge a fundamental conflict between low resolution of biological measurements informing model parameter values and the fundamental fact that dynamical systems are sensitive to initial conditions and parameters. This project develops a novel mathematical framework that provides a quantitative description of global dynamics that are compatible with coarse, noisy biological measurements. Combined with computationally efficient algorithms for the construction of databases that capture biologically relevant dynamics of a network, it can become the basis for shared science in the space of gene networks. These tools will be used to construct and validate network models from experimental data, interrogate dynamic behavior of a given network, and compare dynamics summaries across networks.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.

分子和细胞生物学是我们寻求了解生命和改善人类健康的最前沿。然而，基因和信号如何在真实的时间内相互作用以产生细胞行为的细节还没有得到很好的理解。 问题在于缺乏能够将实验数据整合到模型中的方法，这些模型描述了系统的重要行为，但不描述非必要的细节，这将使得在合理的时间内计算过于繁琐。该项目将开发一套新的工具，可以预测相互依赖的基因网络的整个行为范围，并且可以有效地进行预测，从而可以很容易地探索一些突变或替代基因组的行为。此外，一旦对一组基因行为的描述可用，它将被其他科学家分享和使用。这些结果的不断积累将迅速增加它们对数据科学、细胞生物学和更广泛科学的价值。 分子和细胞生物学已经见证了一个巨大的飞跃，因为科学已经获得了基因组测序的能力。然而，虽然基因组是相对静态的，但表型是由许多随时间动态表达的基因相互作用产生的。此外，由于表型来自复杂的转录网络，其中相互作用往往是非线性的，基于数据的模型将在理解和最终控制这些表型方面发挥关键作用。目前的建模技术，物理学的动机，努力弥合的基本冲突之间的低分辨率的生物测量告知模型参数值和基本事实，即动力系统是敏感的初始条件和参数。该项目开发了一个新的数学框架，提供了一个与粗糙，嘈杂的生物测量兼容的全球动态的定量描述。结合用于构建数据库的计算效率算法，捕获网络的生物相关动态，它可以成为基因网络空间中共享科学的基础。这些工具将用于从实验数据中构建和验证网络模型，询问给定网络的动态行为，并比较网络之间的动态摘要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Conditioned Wiener processes as nonlinearities: A rigorous probabilistic analysis of dynamics

作为非线性的条件维纳过程：动力学的严格概率分析

• DOI: 10.3934/jcd.2023004
• 发表时间: 2023
• 期刊: Journal of Computational Dynamics
• 影响因子: 1
• 作者: Mischaikow, Konstantin;Thieme, Cameron
• 通讯作者: Thieme, Cameron

Computing Linear Extensions for Polynomial Posets Subject to Algebraic Constraints

计算受代数约束的多项式偏序集的线性扩展

• DOI: 10.1137/20m1343208
• 发表时间: 2021
• 期刊: SIAM Journal on Applied Algebra and Geometry
• 影响因子: 1.2
• 作者: Kepley, Shane;Mischaikow, Konstantin;Zhang, Lun
• 通讯作者: Zhang, Lun

Computing the Conley Index: A Cautionary Tale

计算康利指数：一个警示故事

• DOI: 10.1137/23m1558410
• 发表时间: 2023
• 期刊: SIAM Journal on Applied Algebra and Geometry
• 影响因子: 1.2
• 作者: Mischaikow, Konstantin;Weibel, Charles
• 通讯作者: Weibel, Charles

Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees

• DOI: 10.1109/icra48891.2023.10160428
• 发表时间: 2022-10
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Ewerton R. Vieira;A. Sivaramakrishnan;Yao Song;Edgar Granados;Marcio Gameiro;K. Mischaikow;Ying Hung;Kostas E. Bekris