Growth and Dynamics of Extended Actin Structures

这项工作旨在阐明细胞整合一组外部输入以确定运动反应的机制。算法开发用于处理交联和弹性生长肌动蛋白丝网络在几种类型的交联蛋白的存在。这些被用来确定细胞对影响交联和分支速率的外部信号的反应。该算法使用随机增长方法，该方法处理肌动蛋白丝的生长，解聚，覆盖，分支形成和脱离，以及通过交联相互连接的丝。采用特殊点法将弹性效应纳入算法中。肌动蛋白细胞骨架的三维结构以单个亚基的分辨率进行。模型中的速率参数值均为简化情况下的体外实验所得。该代码还用于评估局部突起自发形成的可能性。用几种交联和分支蛋白的聚合实验验证了模型的预测。实际上，所有类型的细胞都能够以与肌肉细胞非常不同的方式移动：白细胞追逐细菌，细胞在受伤后移动以重塑组织，以及迁移癌细胞。这种运动是基于肌动蛋白的，肌动蛋白在细胞中非常丰富，并以长丝的形式积累。这些细丝可以相互连接，形成网络或其他类型的结构，这些结构可以推动细胞膜，从而导致细胞移动。细胞是否运动以及如何运动的“决定”是基于外部信号，这些信号激活或关闭了各种类型的纤维连接。但目前还不清楚肌动蛋白的具体结构是如何响应特定的外部信号的。这些计算使用基于并行计算机的强大模拟方法来预测这些结构。通过建立细胞中肌动蛋白结构对外部信号的反应，人们有望更好地理解与这些肌动蛋白结构相关的疾病的本质。这也将有助于了解癌细胞的迁移，最终有助于癌症治疗的发展。这项资助是在DMS/NIGMS联合倡议下提供的，以支持数学生物学领域的研究资助。这是一个由美国国家科学基金会数学科学部（DMS）和美国国立卫生研究院国家综合医学科学研究所（NIGMS）联合主办的竞赛。

0240770Carlsson This work is aimed at clarifying the mechanisms by which cells integrate a set of external inputs to determine a motility response. Algorithms are developed for treating cross-linking and elasticity of growing actin-filament networks in the presence of several types of cross-linking proteins. These are used to determine the response of a cell to external signals that influence cross-linking and branching rates. The algorithms use a stochastic-growth methodology, which treats actin filament growth, depolymerization, capping, branch formation and detachment, and attachment of filaments to each other by cross-links. Elastic effects are included in the algorithms by the use of a special-points method. The three-dimensional structure of the actin cytoskeleton is followed at a resolution of individual subunits. The values of the rate parameters in the model are obtained from in vitro experiments for simplified cases. The code is also used to evaluate the potential for spontaneous formation of localized protrusions. The model predictions are tested by polymerization experiments with several types of cross-linking and branching proteins. Practically all types of cells are capable of moving in a way very different from muscle cells: white blood cells chasing bacteria, cells moving to remodel tissue after an injury, and migrating cancerous cells. Such motion is based on the protein actin, which is very abundant in cells, and accumulates in long filaments. These filaments can connect to each other, forming networks or other types of structures, which can push against a cell membrane and thus cause the cell to move. The "decision" about whether and how a cell moves is based on external signals, which activate or deactivate various types of filament connections. But it is not known exactly what actin structures form in response to a given set of external signals. The calculations predict these structures using powerful simulation methodologies based on parallel computers. By thus establishing the response of the actin structures in cells to external signals, one can hope to better understand the nature of diseases related to these actin structures. This will also help in understanding the migration of cancerous cells, which could eventually aid the development of cancer treatments. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.