Dynamics and evolution of synthetic and natural gene regulatory networks

项目总结：Mira R35 GM122561续订 人工和天然基因调控网络的动态和进化 人体组织或微生物细胞群可以由数百万个细胞组成，每个细胞包含 数十亿个分子。在这些分子中，DNA以蛋白质编码的形式存储信息 基因，但也在非编码的基因调节区。基因产物结合到这些区域或结合到 彼此形成复杂的基因调控网络，影响单个细胞的行为和 因此，细胞群体。DNA序列突变可以改变这些网络，因为细胞群体适应 不同的环境，对基因进化做出贡献。然而，要彻底理解适应和 在不断进化的细胞群体中，我们还必须问细胞以及细胞群体对基因是如何反应的 网络动力学和随机性，与DNA突变分开或结合在一起。回答这些问题 问题应该加深对细胞群体行为和进化的理解，这是 癌症进展和微生物耐药性是其基础。 2017年前，我们开发了天然基因调控网络的计算模型，以了解 它们如何调节细胞群体中的非遗传多样性并设计合成基因电路来 控制酵母和哺乳动物细胞中蛋白质表达的变异性。自2017年以来，与Mira一起 我们开始通过寻找天然基因的控制点来拉近这些研究方向 网络和设计合成基因电路来控制自然基因在太空中的表达模式 还有时间。我们现在将通过精确扰动来结合和研究自然基因网络和合成基因网络 特定原生基因的表达，以检查对原生基因网络的后续影响， 单细胞和细胞群体表型，以及通过计算模型进化，单细胞 分析和实验进化。总体而言，这些研究将揭示复杂的网络如何实现 生物控制跨越不同的空间和时间尺度，从分子到细胞，从秒到 几周。解决这些问题将教会我们如何控制不断进化的细胞群体，这是 与了解、预测和可能预防癌症和微生物耐药性有关。

Project Summary: MIRA R35 GM122561 renewal Title: Dynamics and evolution of synthetic and natural gene regulatory networks Human tissues or microbial cell populations can consist of millions of cells, each of which contains billions of molecules. Central among these molecules, DNA stores information in protein-coding genes, but also in noncoding, gene-regulatory regions. Gene products binding to such regions or to each other form complex gene regulatory networks that influence the behavior of individual cells and thereby cell populations. DNA sequence mutations can alter these networks as cell populations adapt to various environments, contributing to genetic evolution. Yet, to thoroughly understand adapting and evolving cell populations, we must also ask how cells and thereby cell populations respond to gene network dynamics and stochasticity, apart from or combined with DNA mutations. Answering these questions should deepen the understanding of the behavior and evolution of cell populations, which underlie cancer progression and microbial drug resistance. Before 2017, we developed computational models of natural gene regulatory networks to understand how they modulate nongenetic diversity in cell populations and designed synthetic gene circuits to control the variability of protein expression in yeast and mammalian cells. Since 2017, with MIRA support we started bringing these research directions closer, by seeking control points in natural gene networks and devising synthetic gene circuits to control expression patterns of native genes in space and time. We will now combine and study natural and synthetic gene networks by precisely perturbing the expression of specific native genes, to examine subsequent effects on the native gene network, single-cell and cell population phenotypes, as well as evolution by computational modeling, single-cell analysis and experimental evolution. Overall, these studies will reveal how complex networks enable biological control across disparate scales of space and time, from molecules to cells, from seconds to weeks. Addressing these questions will teach us how to control evolving cell populations, which is relevant for understanding, predicting and possibly preventing cancer and microbial drug resistance.