Predictive Modeling of Pattern Formation Driven by Synthetic Gene Networks

虽然自然现象往往看起来很复杂，因此难以预测，但在这些现象之间， 看似混乱的事件，可以有惊人的美丽的图案和形式的时刻。在某些 从某种意义上说，合成生物学是关于识别和复制这些模式，而数学是关于 描述和理解其形成背后的机制。虽然空间格局是 在生物体中无处不在，识别潜在机制的任务可能是艰巨的，因为 活细胞和有机体的巨大复杂性事实上，对自然模式的研究可以追溯到 回到过去的几个世纪。在这份提案中，该团队建议将联合收割机基因电路工程与 数学分析，以促进我们对基于反应扩散（RD）的生物模式的理解 阵具体而言，该团队希望在拟议的 研究：目标1，实验和数学表征基于RD的细胞图案形成 由合理设计的基因电路驱动。目的2，探讨营养限制对模式的影响 阵目标3，设计和测试相互作用群体的模式形成。 具体来说，该团队建议设计一套基因电路，以指导细菌细胞形成自组织 没有预定义的空间线索的模式。网络拓扑、非线性、基因的作用 表达随机性，以及环境信号对观察到的空间结构的贡献 模式将被检查。为此，这个跨学科的团队计划机械地制定一个 一系列合理的RD模型，准确描述了基因调控，蛋白质生产，群体 传感和由合成电路驱动的色散。此外，该小组计划制定适当的 基于单细胞琼脂糖垫平台的实验、计算和数学工具， 将使我们能够定量和实验探索空间格局的基本机制 跨分子、单细胞和菌落规模的形成。

While natural phenomena may often appear to be complex and hence difficult to predict, in between those seemingly chaotic events, there can be moments of strikingly beautiful patterns and forms. In certain sense, synthetic biology is about identifying and reproducing these patterns and mathematics is about describing and understanding the mechanisms behind their formations. Although spatial patterns are ubiquitous in living organisms, the task of identifying the underlying mechanisms can be daunting due to the overwhelming complexity of living cells and organisms. Indeed, the study of natural patterns dates back to many centuries in the past. In this proposal, the team proposes to combine gene circuit engineering and mathematical analysis to advance our understanding of reaction-diffusion (RD) based biological pattern formation. Specifically, there are three main objectives the team hopes to achieve in the proposed research: Aim 1, Experimentally and mathematically characterize RD based cellular pattern formation driven by rationally designed gene circuits. Aim 2, Investigate implications of nutrient limitation on pattern formation. Aim 3, Engineering and testing of pattern formation of interacting populations. Specifically, the team proposes to engineer a set of gene circuits to direct bacterial cells to form self-organized patterns without predefined spatial cues. The role of network topology, nonlinearity, gene expression stochasticity, and environmental signals in contributions to observed spatially structured patterns will be examined. To this end, this interdisciplinary team plans to mechanistically formulate a series of plausible RD models that accurately describe gene regulation, protein production, quorum sensing, and dispersion driven by synthetic circuits. Moreover, the team plans to develop appropriate experimental, computational, and mathematical tools based on the single-cell agarose pad platform that shall allow us to quantitatively and experimentally probe the fundamental mechanisms of spatial patterns formation across molecular, single-cell, and colony scales.