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

生物具有从分子到有机体的长度和时间尺度。我们经常用基因控制分子的细节，但我们最终感兴趣的是有机体的功能。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“适当大小”每个模型，使其能够面对各种实验行为，同时保持有用的可处理性。从肌腱到角膜，胶原蛋白起着至关重要的作用。我们将开发一种纤维胶原蛋白的结构和力学模型。沿着纤维长度的周期性结构将包含在用于粗粒度金属晶体研究的方法中，而交联的力学贡献将使用用于液晶橡胶的方法进行处理。我们对如何控制原纤维力学性能的理解的提高将有助于更好地设计胶原材料。生物体会衰老和死亡。我们将进一步开发一个基于网络的机体老化和死亡率模型。我们将探索为什么它是有效的，我们如何用观察数据更好地测试它，以及我们如何利用它来提高对衰老和死亡率的个人预测。我们将首先确定进化最优网络拓扑。然后，我们将其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何时可以在损伤导致进一步损伤之前修复，用于各种模型生物。结果将是对衰老、损伤和死亡是如何交织在一起的更深层次的理解；以及它们可以在多大程度上进行调整。致病菌可以侵入并在宿主细胞层内繁殖。我们将研究是什么决定了细菌在宿主细胞层中的繁殖。为了做到这一点，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来参数化单个细胞之间细菌入侵和繁殖的详细模型。这些详细的模型将用于控制我们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活体实验之间架起一座桥梁。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进行这项工作，以便面对、理解和扩展现有数据。这些模型将作为微观分子细节之间的桥梁，这些细节可以通过实验控制，而宏观行为对我们来说很重要。我们的工作将统一对这些系统的理解，加速新行为的发现，并促进我们修改这些重要生物系统的能力。

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.