Dynamics and evolution of synthetic and natural gene regulatory networks

项目摘要：MIRA R35 GM122561更新 合成和天然基因调控网络的动力学和进化 人体组织或微生物细胞群可以由数百万个细胞组成，每个细胞都含有 数十亿个分子。在这些分子中，DNA以蛋白质编码的方式存储信息。 基因，但也在非编码，基因调控区。与这些区域结合的基因产物或与 相互形成复杂的基因调控网络，影响单个细胞的行为， 从而形成细胞群。随着细胞群体的适应，DNA序列突变可以改变这些网络 适应不同的环境，促进基因进化。然而，要彻底理解适应和 我们还必须问，细胞和细胞群如何对基因表达作出反应， 网络动力学和随机性，除了或结合DNA突变。回答这些 这些问题应该加深对细胞群体行为和进化的理解， 是癌症进展和微生物耐药性的基础。 在2017年之前，我们开发了天然基因调控网络的计算模型， 他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， 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，MIRA 通过在自然基因组中寻找控制点， 网络和设计合成基因电路来控制太空中天然基因的表达模式 和时间我们现在将联合收割机结合并研究自然和合成基因网络， 特定天然基因的表达，以检查对天然基因网络的后续影响， 单细胞和细胞群体表型，以及通过计算建模，单细胞 分析和实验进化。总的来说，这些研究将揭示复杂网络如何使 从分子到细胞，从几秒钟到几秒钟， 周解决这些问题将教会我们如何控制不断进化的细胞群， 与理解、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.