Dynamics and evolution of synthetic and natural gene regulatory networks

项目摘要：Mira 人工和天然基因调控网络的动态和进化 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不仅存储蛋白质编码基因中的信息，而且还 在非编码的基因调控区。与这些区域结合的基因产物形成复杂基因 调节网络影响单个细胞的行为，从而影响细胞群体。 DNA序列的变化可以改变这些网络，使细胞群体更好地适应 不同的环境，有助于遗传进化。然而，要了解基因网络如何控制细胞 我们必须了解网络动力学和随机性如何影响细胞，从而 细胞群。回答这些问题应该有助于我们理解 细胞群，这是癌症进展和微生物耐药性的基础。 为了解决这个问题，我们开发了自然监管网络的计算模型，以 了解它们如何调节细胞群体中的非遗传多样性。我们还设计了 控制酵母和哺乳动物细胞中蛋白质表达的可变性的合成基因网络。 现在我们计划将这些研究方向联系起来，使用合成基因网络来产生特定的 作为自然基因网络信号的空间和时间基因表达模式，研究 通过计算模拟和模拟对细胞群体行为和进化的后续影响 实验进化论。总体而言，这些研究将阐明复杂的网络如何实现控制 跨越生物学中的时空尺度，从分子到细胞。解决这些问题将 教我们如何控制不断进化的细胞群体，这与理解、预测和 可能会预防癌症和微生物耐药性。

Project Summary: MIRA Title: Dynamics and evolution of synthetic and natural gene regulatory networks 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 form complex gene regulatory networks that influence the behavior of individual cells and thereby cell populations. Changes in DNA sequence can alter these networks, making cell populations better adapted in various environments, contributing to genetic evolution. Yet, to learn how gene networks control cell populations, we must understand how network dynamics and stochasticity affects cells and thereby cell populations. Answering these questions should help us understand the behavior and evolution of cell populations, which are the bases of cancer progression and microbial drug resistance. To attack this problem, we have developed computational models of natural regulatory networks to understand how they modulate nongenetic diversity in cell populations. We have also designed synthetic gene networks to control the variability of protein expression in yeast and mammalian cells. Now we plan to connect these research directions, using synthetic gene networks to generate specific gene expression patterns in space and time that serve as signals for natural gene networks, studying the subsequent effects on cell population behavior and evolution by computational modeling and experimental evolution. Overall, these studies will shed light on how complex networks enable control across scales of space and time in biology, from molecules to cells. Addressing these questions will teach us how to control evolving cell populations, which is relevant for understanding, predicting and possibly preventing cancer and microbial resistance.