Predictive Modeling of Pattern Formation Driven by Synthetic Gene Networks

合成基因网络驱动的模式形成的预测模型

• 批准号: 9755460
• 负责人: Yang Kuang
• 金额: $ 36.5万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755460
• 关键词: Affect; Back; Biological; Biomedical Engineering; Cells; Characteristics; Communication; Complex; Cues; Development; Diffusion; Disease; Engineering; Equation; Essential Amino Acids; Event; Experimental Designs; Gene Expression; Gene Expression Regulation; Genes; Homeostasis; Individual; Mathematics; Medical; Modeling; Molecular; Nature; Nutrient; Organism; Pattern; Pattern Formation; Phenotype; Phytoplankton; Play; Population; Production; Proteins; Reaction; Research; Research Personnel; Role; Sepharose; Series; Signal Transduction; Structure; Study models; Synthetic Genes; System; Testing; Theoretical Studies; Treatment outcome; Zooplankton; base; cell growth; cell motility; design; direct application; experimental study; mathematical analysis; mathematical model; personalized medicine; predictive modeling; quorum sensing; synthetic biology; theories; tool; treatment planning

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.

虽然自然现象通常似乎很复杂，因此很难预测，但在这些现象之间 看似混乱的事件，可能会有惊人的美丽图案和形式的时刻。肯定 感觉，合成生物学是关于识别和再现这些模式，数学是关于 描述和理解其形成背后的机制。虽然空间模式是 无处不在的生物体中，识别基本机制的任务可能是由于 活细胞和生物的压倒性复杂性。确实，自然模式的研究可以追溯到 过去多个世纪。在此提案中，团队建议将基因电路工程和 数学分析以促进我们对基于反应扩散（RD）生物学模式的理解 形成。具体而言，团队希望在拟议中实现三个主要目标 研究：AIM 1，实验和数学表征基于RD的细胞模式形成 由理性设计的基因电路驱动。目标2，研究养分限制对模式的含义 形成。 AIM 3，工程和测试相互作用种群的模式形成。 具体而言，该团队建议设计一组基因回路，以指导细菌细胞形成自组织 没有预定义空间提示的模式。网络拓扑，非线性，基因的作用 表达随机性和对观察到空间结构的贡献的环境信号 将检查模式。为此，这个跨学科团队计划机械地制定 一系列合理的RD模型，这些模型准确地描述了基因调节，蛋白质产生，法定人数 传感和由合成电路驱动的分散。此外，团队计划开发适当的 基于单细胞琼脂糖平台的实验，计算和数学工具 应允许我们定量和实验探测空间模式的基本机制 跨分子，单细胞和菌落尺度的形成。