Elucidating biophysical mechanisms for force sensing and control using non-equilibrium statistical mechanics and AI

使用非平衡统计力学和人工智能阐明力传感和控制的生物物理机制

• 批准号: 10673871
• 负责人: Suriyanarayanan Vaikuntanathan
• 金额: $ 38.28万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10673871
• 关键词: Address; Artificial Intelligence; Biological; Biological Process; Biophysical Process; Biophysics; Cell Communication; Cell Shape; Cell division; Cell membrane; Cells; Complex; Computer Simulation; Cytoskeletal Modeling; Data; Development; Disease; Ensure; Equilibrium; Event; Generations; Goals; Health; Human; Length; Lysosomes; Machine Learning; Membrane Fusion; Microscopic; Modeling; Molecular; Morphogenesis; Morphology; Occupations; Pattern; Play; Process; Role; Shapes; Signal Transduction; Statistical Mechanics; System; Techniques; Time; Tissues; Work; biological systems; cell motility; computer framework; driving force; immune system function; response; single-cell RNA sequencing; tool

Non-equilibrium activity is crucial for maintain and modulating tissue shape, development and morphogenesis, lysosome dynamics, cell membrane remodeling during cell division or membrane fusion and fission events. Importantly many of these processes play a significant role in human health helping regulate for instance immune system function and ensuring accurate developmental morphogenesis. However, there is a major gap in our understanding of how microscopic non-equilibrium biophysical driving forces give rise to a desired molecular response, function, or control. Indeed, while the theoretical and computational frameworks for the study of equilibrium biological processes are very well developed, there are very limited analogous tools for the study of complex far-from-equilibrium biological systems. Further, the large length and time scales of biological systems and processes make explicit computational simulations impractical. Addressing this problem requires the development of a range of multiscale non-equilibrium statistical mechanics techniques in combination with tools from machine learning and artificial intelligence so that the large length and time scales associated with the above-mentioned biological processes can be appropriately captured. The work outlined in this proposal builds towards these long-term goals by focusing on three paradigmatic example systems 1) Understanding and predicting non-equilibrium lysosomal dynamics and morphologies 2) Understanding and modelling cytoskeletal processes responsible for developmental patterning, cell-cell communication, and force generation 3) Developing frameworks for determining drivers of cell fate and differentiation from single cell RNA sequencing data. Each of these paradigmatic examples has implications for diseases. These paradigmatic examples build on the recent foundational non-equilibrium statistical mechanics frameworks developed by my group and expand them so that they can be utilized in biological contexts.

非平衡活动对于维持和调节组织形状、发育和发育至关重要 形态发生、溶酶体动力学、细胞分裂或膜融合过程中的细胞膜重塑 和裂变事件。重要的是，其中许多过程在帮助人类健康方面发挥着重要作用 例如调节免疫系统功能并确保准确的发育形态发生。 然而，我们对微观非平衡生物物理如何发生的理解存在重大差距。 驱动力产生所需的分子反应、功能或控制。确实，虽然理论上 用于研究平衡生物过程的计算框架已经非常发达， 用于研究复杂的非平衡生物系统的类似工具非常有限。 此外，生物系统和过程的大长度和时间尺度使得明确的计算 模拟不切实际。 解决这个问题需要开发一系列多尺度非平衡统计方法 力学技术与机器学习和人工智能工具相结合， 与上述生物过程相关的大长度和时间尺度可以是 适当捕捉。本提案中概述的工作通过重点关注这些目标来实现这些长期目标 三个典型示例系统 1) 理解和预测非平衡溶酶体 动力学和形态学 2) 理解和建模负责的细胞骨架过程 发育模式、细胞间通讯和力量生成 3) 开发框架 从单细胞 RNA 测序数据确定细胞命运和分化的驱动因素。这些中的每一个 典型例子对疾病有影响。这些典型例子建立在最近的 我的小组开发的基础非平衡统计力学框架并对其进行了扩展 以便它们可以在生物学环境中使用。

项目成果

Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems.

复杂系统突发转录动力学的剖析和集成。

• 发表时间: 2023-06-13
• 作者: Gao, Cheng Frank;Vaikuntanathan, Suriyanarayanan;Riesenfeld, Samantha J
• 通讯作者: Riesenfeld, Samantha J

Dissection and integration of bursty transcriptional dynamics for complex systems.

复杂系统突发转录动力学的剖析和整合。

• DOI: 10.1073/pnas.2306901121
• 发表时间: 2024-04-26
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Cheng Frank Gao;Suriyanarayanan Vaikuntanathan;Samantha J Riesenfeld
• 通讯作者: Samantha J Riesenfeld