A Network Biology Approach to Antibiotic Action and Bacterial Defense Mechanisms

该项目的目标是使用创新的系统生物学和合成生物学方法， 定量表征和分析细菌基因调控网络， 对抗生素的反应，持久性的形成和耐药性的出现。与 令人震惊的耐药细菌菌株的传播，更好地了解具体的 未来需要研究导致细菌性抗生素导致细胞死亡的事件序列 抗菌药物开发因此，需要系统生物学和合成生物学。 生物学方法来辨别基因，蛋白质和途径之间的相互作用， 我们对细菌如何抵抗抗生素的理解。的 遗传网络的潜在逻辑的含义很难通过 实验技术本身，以及成功的方法，在许多情况下，涉及工会 新的实验和计算建模技术。为了解决这个问题，我们 开发了能够构建定量模型的计算实验方法 基因，蛋白质和代谢物调控网络的表达测量和无先验 关于网络结构或功能的信息。在本项目中，我们将使用这些方法来 反向工程细菌基因调控网络的基础细胞反应抗生素， 坚持者的形成和抵抗力的出现。由此产生的网络和 将分析途径，以深入了解相关生物学的调控 过程和网络模型将用于确定关键监管机构和调解人， 各种表型反应。这项工作可能会导致对压力反应的新见解， 细菌和药物发现的新靶点的鉴定，例如，那些克服 细菌保护机制或激活细菌程序性细胞死亡。这个项目可能 从而能够开发新型抗生素， 基因网络的复杂调控特性。

The goal of this project is to use innovative systems biology and synthetic biology approaches to quantitatively characterize and analyze bacterial gene regulatory networks underlying cellular responses to antibiotics, the formation of persisters and the emergence of resistance. With the alarming spread of antibiotic-resistant strains of bacteria, a better understanding of the specific sequences of events leading to cell death from bactericidal antibiotics is needed for future antibacterial drug development. Accordingly, there is a need for systems biology and synthetic biology approaches to discern the interplay between genes, proteins and pathways in furthering our understanding of how bacteria respond and defend themselves against antibiotics. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will in many cases, involve the union of new experiments and computational modeling techniques. To address this problem, we have developed computational-experimental methods that enable construction of quantitative models of gene, protein and metabolite regulatory networks using expression measurements and no prior information on the network structure or function. In this project, we will use these approaches to reverse engineer bacterial gene regulatory networks underlying cellular responses to antibiotics, the formation of persisters and the emergence of resistance. The resulting networks and pathways will be analyzed to gain insight into the regulatory control of the associated biological processes, and the network models will be used to identify key regulators and mediators for a variety of phenotypic responses. This work could lead to new insights into the stress response of bacteria and the identification of novel targets for drug discovery, e.g., ones that overcome bacterial protective mechanisms or activate bacterial programmed cell death. This project may thus enable the development of novel classes of antibiotics that account for and utilize the complex regulatory properties of genetic networks.