CompBio: Simulation of self-emerging properties of coupled biochemical and cellular networks in social behavior of Myxobacteria

粘细菌是一种迷人的生物：当食物充足时，它们以多细胞群的形式进食。虽然每个细菌在新陈代谢和繁殖方面都是自主的，但它们共同组成了一个多细胞生物体。蜂群是一个捕食性的集体，它们合作移动和进食，在消化猎物细菌时一起狩猎和汇集胞外酶。当食物短缺时，数十万个群细胞改变它们的行为，启动一个自组织程序，建立密集的聚集体，称为子实体，其中杆状细胞分化成球形，耐饥饿的孢子。如果一个移动的物体，如昆虫的腿，与子实体接触，整个孢子包-子实体-很可能会被昆虫作为一个单位携带。这样，如果携带到一个新的食物来源（昆虫正在前往），成千上万的孢子可以发芽并成为一个“即时”群体，而不是必须从一个细胞重新建立一个群体。了解群体和子实体的自组织对于了解多细胞生物的发展具有潜在的影响，可能包括某些出生缺陷。我们将建立一个基于极短程（细胞接触）相互作用、分化和运动的粘细菌子实体形成的多尺度三维（艾德）计算模型。我们将分析特定突变对子实体发育的影响，将实验与模拟进行比较，以改进模型。生物化学和细胞网络的单一尺度模型无法捕捉复杂性，因为即使是非常基本的生物现象也发生在不同的空间和时间尺度上。这就是为什么确定如何最好地在不同尺度下组合联合收割机模型以及几种不同类型模型的组合如何影响一般多尺度模型的准确性至关重要。这些模型通常作为分布式系统上的迭代工作流运行。这些分布式工作流的管理对于最终用户来说可能是复杂的，并且需要面向数据和结果而不是面向任务的方便的描述性模型。我们将在分布式问题解决环境中实现我们的多尺度模型。该环境将允许轻松配置和操作工作流程，以通过系统生物学标记语言（SBML）的扩展来执行生化和细胞网络的分析。建议的3D计算模型也将作为一种工具，用于分析依赖于细胞接触信号的其他多细胞结构的构建机制。我们将在一个易于部署的建议的系统生物学工具包（SBT）中传播所有结果，以模拟分子和亚细胞水平，并在CellAggregate包中模拟多细胞水平。跨学科研究团队包括一名计算机科学家，一名数学家，一名生物学家，一名发育生物学家和生物化学家，在包括圣母大学，斯坦福大学和洛斯阿拉莫斯国家实验室在内的多机构合作中。

Myxobacteria are fascinating creatures: when food is plentiful, they feed as a multicellular swarm. Though each bacterium is autonomous with respect to metabolism and reproduction, together they make up a multicellular organism. A swarm is a predatory collective that moves and feeds cooperatively, hunting together and pooling extracellular enzymes when digesting prey bacteria. When food runs short, the hundreds of thousands of swarm cells change their behavior to initiate a self-organized program that builds densely packed aggregates, called fruiting bodies, within which rod shaped cells differentiate into spherical, starvation-resistant spores. If a moving object, such as the leg of an insect, comes in contact with a fruiting body, the entire package of spores -- the fruiting body -- will likely be picked up and carried as a unit by the insect. This way, if carried to a new food source (toward which the insect was heading), the thousands of spores can germinate and emerge as an "instant" swarm, rather than having to re-establish a swarm from a single cell. Understanding the self-organization of swarms and fruiting bodies has potential impact for understanding the development of multi-cellular organisms, possibly including certain birth defects.We will develop a multiscale 3 dimensional (ED) computational model of Myxobacterial fruiting body formation based on very short range (cell contact) interactions, differentiation and motility. We will analyze the effect of particular mutations on fruiting body development, comparing experiments with simulations for improving the model. Single scale models of biochemical and cellular networks are unable to capture the complexity, for even very basic biological phenomena occurr over diverse space and time scales. This is why it is crucial to determine how best to combine models at different scales and how the combination of several different types of models impacts the accuracy of the general multiscale model. These models are typically run as an iterative workflow over a distributed system. Management of these distributed workflows can be complicated to an end user and necessitates a convenient descriptive model that is data and result oriented, rather than task oriented. We will implement our multiscale models in a distributed problem solving environment. This environment will allow easy configuration and manipulation of the workflows to perform analysis of biochemical and cellular networks through an extension to the Systems Biology Markup Language (SBML). Proposed 3D computational model will also serve as a tool for analyzing mechanisms for building other multicellular structures dependent on cell contact signaling.We will disseminate all results in an easily deployable bundle of the proposed Systems Biology Toolkit (SBT) to model molecular and subcellular levels, and the CellAggregate package to model the multicellular level. The interdisciplinary research team encompasses a computer scientist, a mathematician, a biophysicist, and a developmental biologist and biochemist, in a multi-institution collaboration including Notre Dame, Stanford, and Los Alamos National Lab.