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

非均衡活动对于维持和调节组织的形状、发育和 细胞分裂或膜融合过程中的形态发生、溶酶体动力学、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，调节免疫系统的功能和确保准确的发育形态发生。 然而，在我们对微观非平衡生物物理如何 驱动力产生所需的分子反应、功能或控制。事实上，虽然理论上 研究平衡生物过程的计算框架也发展得很好， 用于研究复杂的远离平衡的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大时间尺度使得显式计算 模拟不切实际。 解决这一问题需要发展一系列多尺度的非平衡统计 力学技术与机器学习和人工智能的工具相结合，从而 与上述生物过程相关的大长度和时间尺度可以是 恰当地捕捉到了。本提案中概述的工作通过集中精力实现这些长期目标 关于三个范例系统：1)对非平衡溶酶体的理解和预测 动力学和形态2)理解和模拟细胞骨架过程负责 发育模式、细胞-细胞交流和力量生成3)开发框架 根据单细胞RNA测序数据确定细胞命运和分化的驱动因素。这其中的每一个 典范的例子对疾病有影响。这些范例建立在最近的 我的团队开发的基本非平衡统计力学框架并对其进行扩展 这样它们就可以在生物学的背景下被利用。

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.