Soft- and statistical-physics models of coarse-grained biological structure and dynamics

粗粒度生物结构和动力学的软物理和统计物理模型

• 批准号: RGPIN-2019-05888
• 负责人: Rutenberg, Andrew
• 金额: $ 2.99万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763168
• 关键词: Soft; statistical; physics; models; coarse

Living creatures have length- and time-scales ranging from the molecular to the organismal. We often have genetic control over molecular details, but we are ultimately interested in organismal function. Coarse-grained models can bridge between these scales. We will develop new coarse-grained models for each of three important biological systems. We will "right size" each model so that it is able to confront a variety of experimental behaviors while remaining usefully tractable. Collagen plays essential roles from tendons to the cornea. We will develop a combined structural and mechanical model of fibrillar collagen. Periodic structure along the fibril length will be included with methods developed for coarse-grained studies of metallic crystals, while the mechanical contributions of cross-linking will be treated with methods developed for liquid-crystalline rubbers. Our improved understanding of how to control the mechanical properties of fibrils will facilitate better design of collagenous materials. Living organisms age and die. We will further develop a network-based model for organismal aging and mortality. We will explore why it works, how we can better test it with observational data, and how we can use it to improve individual predictions of aging and mortality. We will first determine the evolutionarily-optimal network topology. We will then compare it to the network inferred from observational data using Bayesian statistics. We will use these networks to examine when damage could be repaired before it leads to further damage, for a variety of modelled organisms. The result will be a deeper understanding of how aging, damage, and mortality intertwine; and how much they can be adjusted. Pathogenic bacteria can invade and reproduce within layers of host cells. We will examine what determines the propagation of bacteria in host-cell layers. To do this, we will start with computational models of cell layers. From experimental video-microscopy, we will use computational image-analysis to parameterize detailed models of the invasion and propagation of bacteria between individual cells. These detailed models will be used to control bacterial infection, growth, and transmission within our cell-layer models. The result will allow us to bridge between simplified but convenient experiments in petri-dishes and more realistic but difficult experiments within living organisms. We will build models of stochastic, non-linear, and non-equilibrium biological systems at appropriate coarse-grained scales. We will do this in collaboration with experimental colleagues in order to confront, understand, and expand available data. These models will serve as bridges between the microscopic, molecular detail that can be experimentally controlled and the macroscopic behavior that is important to us. Our work will unify understanding of these systems, speed discovery of novel behavior, and facilitate our ability to modify these important biological systems.

生物有从分子到有机体的长度和时间尺度。我们通常对分子细节有遗传控制，但我们最终感兴趣的是生物体的功能。粗粒度模型可以在这些尺度之间架起桥梁。我们将为三个重要的生物系统开发新的粗粒度模型。我们将“正确地调整”每个模型，使其能够面对各种实验行为，同时保持有用的易处理性。从肌腱到角膜，胶原蛋白起着至关重要的作用。我们将建立一个纤维胶原的结构和力学模型。周期性结构沿着的原纤长度将包括与粗粒度的金属晶体的研究开发的方法，而交联的机械贡献将与液晶橡胶开发的方法处理。我们对如何控制原纤维的机械性能的更好理解将有助于更好地设计胶原材料。生物体衰老和死亡。我们将进一步开发一个基于网络的生物衰老和死亡模型。我们将探索它为什么有效，我们如何更好地用观测数据来测试它，以及我们如何使用它来改善对衰老和死亡率的个人预测。我们将首先确定进化最优的网络拓扑。然后，我们将它与使用贝叶斯统计从观测数据推断的网络进行比较。我们将使用这些网络来检查在导致进一步损害之前，何时可以修复损害，对于各种模拟生物体。其结果将是更深入地了解衰老、损伤和死亡是如何相互影响的，以及它们可以在多大程度上得到调整。病原菌可以侵入宿主细胞层并在其中繁殖。我们将研究是什么决定了宿主细胞层中细菌的繁殖。为此，我们将从细胞层的计算模型开始。从实验视频显微镜，我们将使用计算图像分析参数化的详细模型的入侵和繁殖的细菌之间的个别细胞。这些详细的模型将用于控制我们的细胞层模型中的细菌感染，生长和传播。这一结果将使我们能够在简化但方便的培养皿实验和更现实但困难的生物体内实验之间架起桥梁。我们将在适当的粗粒度尺度上建立随机，非线性和非平衡生物系统的模型。我们将与实验同事合作，以面对，理解和扩展可用数据。这些模型将作为可以通过实验控制的微观分子细节和对我们来说重要的宏观行为之间的桥梁。我们的工作将统一对这些系统的理解，加速发现新的行为，并促进我们修改这些重要生物系统的能力。