Bilateral BBSRC-NSF/BIO: Excitocell: A rewired eukaryotic cell model for the analysis and design of cellular morphogenesis

事实上，细胞和组织所做的每一件重要的事情都涉及细胞形态的变化：细胞分裂、细胞迁移和伤口愈合都涉及细胞形态的深刻变化。这些形状的变化是由被称为“皮层”的细胞外层驱动的。皮层含有纤维蛋白网，可以收缩、突出和滑动。这些运动为细胞形状的变化提供了基础。控制这种纤维网络的是一种叫做Rho的蛋白质结构域。Rho以不同的模式被激活，每一种模式对应着不同的细胞形状变化。因此，为了理解和控制细胞分裂、细胞迁移和伤口愈合等过程，了解如何理解和控制Rho激活模式至关重要。在这个合作项目中，来自美国和英国的研究人员使用合成细胞生物学和数学模型来测试皮层模式形成的机制。该项目将为本科生、研究生和博士后提供定量细胞生物学和数学建模之间的跨学科培训的研究机会。该项目还为高中教师提供嵌入式研究机会，以及特定主题的K-12和公共宣传活动。pi先前的研究表明，细胞可以支持动态耦合Rho活性和肌动蛋白组装的持续波，数学模型和实验验证表明，皮层波的传播是基于Rho自激活和肌动蛋白介导的Rho失活。这构成了一个“可兴奋”系统的基础，这是一个已经建立的理论模型家族，很少有以前已知的细胞表现。本项目将推导和实验验证一个最小的分子机制和基本设计元素所需的皮质兴奋性。为了实现该项目的目标，将采用一种新的半合成平台，利用简单的大分子部分复制复杂的细胞行为，使分子网络的操作变得更加复杂，而不是通过解剖复杂的生理网络。该项目将把可兴奋动力学的计算建模与全细胞活细胞成像和新的皮质动力学离体模型结合起来。首先，使用自然调节剂及其突变体在静止的非有丝分裂细胞（青蛙和棘皮动物的卵母细胞）中重建可兴奋的动力学，然后用合成的等量调节剂代替。其次，将开发一个可量化的离体模型（使用青蛙卵母细胞或卵提取物和支持脂质双层），以便在简化的背景下精确控制系统组成。第三，为了实现对皮质图案形成的按需控制，将使用动物卵母细胞的光遗传学方法来探索自然和合成皮质图案形成的全部功能。迭代实验和计算建模将在整个项目中使用，以1)解释生物数据，2)以数学模型的形式表达候选机制，3)生成预测，以及4)通过实验测试这些预测。

Virtually every important thing cells and tissues do involve changes in cell morphology: cell division, cell migration, and wound healing all involve profound changes in cell shape. These shape changes are powered by the outer layer of the cell, which is called the "cortex". The cortex contains a meshwork of fibrous proteins that can contract, protrude, and slide. These movements provide the basis of cell shape changes. The control of this fibrous network is the domain of a protein called Rho. Rho is somehow activated in distinct patterns, each of which corresponds to a different cell shape change. Thus, in order to understand and control processes such as cell division, cell migration and wound healing, it is essential to learn how to understand and control patterns of Rho activation. In this collaborative project, investigators from the US and the UK use synthetic cell biology and mathematical modeling to test mechanisms underlying cortical pattern formation. This project will provide research opportunities for interdisciplinary training at the interface between quantitative cell biology and mathematical modeling for undergraduates, graduate students, and postdocs. The project also offers embedded research opportunities for high-school teachers as well as topic-specific K-12 and public outreach activities. Previous studies by the PIs demonstrated that cells can support sustained waves of dynamically-coupled Rho activity and actin assembly, and mathematical modeling followed by experimental verification revealed that cortical wave propagation is based on Rho autoactivation and actin-mediated Rho inactivation. This constitutes the basis of an "excitable" system, a family of well-established theoretical models with few previously-known cellular manifestations. This project will deduce and experimentally validate a minimal molecular mechanism and basic design elements required for cortical excitability. To achieve the goals of this project, a novel semi-synthetic platform for replicating complex cell behaviors using simple macromolecular parts will be employed, enabling manipulation of the molecular network at escalating complexity instead of by dissecting a complex physiological network. The project will couple computational modeling of excitable dynamics to live-cell imaging in whole cells and in a new ex vivo model of cortical dynamics. First, excitable dynamics will be reconstituted in resting, non-mitotic cells (oocytes of frogs and echinoderms) using natural regulators and their mutants, followed by their replacement with synthetic equivalents. Second, a quantifiable ex vivo model will be developed (using frog oocyte or egg extracts and supported lipid bilayers) that permits precise control of system composition in a simplified context. Third, to achieve on-demand control of cortical pattern formation, an optogenetic approach in animal oocytes will be used to explore the repertoire of both natural and synthetic cortical pattern formation. Iterative experimentation and computational modeling will be employed throughout the project to 1) interpret biological data, 2) express candidate mechanisms in the form of mathematical models, 3) generate predictions, and 4) test those predictions experimentally.