Dynamics and evolution of synthetic and natural gene regulatory networks

合成和天然基因调控网络的动力学和进化

• 批准号: 9897606
• 负责人: Gabor Balazsi
• 金额: $ 36.92万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9897606
• 关键词: Address; Affect; Behavior; Binding; Biology; Cancerous; Cells; Code; Complex; Computer Models; DNA; DNA Sequence; Disease; Drug resistance; Environment; Evolution; Gene Expression Profile; Genes; Human; Individual; Learning; Light; Malignant Neoplasms; Mammalian Cell; Microbial Drug Resistance; Nucleic Acid Regulatory Sequences; Population; Proteins; Regulator Genes; Research; Resistance; Signal Transduction; Synthetic Genes; Time; Tissues; Untranslated RNA; Yeasts; base; behavior influence; cell behavior; design; drug-sensitive; gene product; genetic evolution; human tissue; microbial; non-genetic; prevent; protein expression; tumor progression

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.

项目概述：MIRA