A Network Biology Approach to Antibiotic Action and Bacterial Defense Mechanisms

抗生素作用和细菌防御机制的网络生物学方法

基本信息

批准号：
7341401
负责人：
JAMES J COLLINS
金额：
$ 81.25万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-30 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7341401
关键词：
Accounting Address Affect Algorithms American Anti-Bacterial Agents Antibiotic Resistance Antibiotic Therapy Antibiotics Antitoxins Apoptosis Area Arrhythmia Attenuated Award Bacteria Bacterial Genes Bacterial Toxins Behavior Biochemical Biological Biological Phenomena Biological Process Biology Biomedical Engineering Biomedical Research Biotechnology Boston Brain Cardiac Categories Cell Communication Cell Death Cell Density Cell Surface Proteins Cell Therapy Cell Wall Cell physiology Cells Cessation of life Chelating Agents Chemicals Chronic Class Clinical Communication Communities Complex Computational Technique Computer Simulation Condition DNA DNA Damage DNA Gyrase DNA Replication Inhibition DNA biosynthesis Data Defense Mechanisms Detection Development Devices Diabetic Neuropathies Discipline Dissection Drug Delivery Systems Drug Design Elderly Engineering Enhancement Technology Environment Equilibrium Escherichia coli Event Facility Construction Funding Category Feedback Foundations Future Gene Expression Gene Expression Regulation Gene Proteins Gene Targeting Generations Genes Genetic Genetic Transcription Genome Genomics Global Change Goals Gram-Positive Bacteria Grant Growth Heart Hip region structure Human Human Characteristics Hydroxyl Radical Indium Infection Internet Iron Knock-out Knowledge Lead Lesion Logic Mammalian Cell Maps Measurement Mechanics Mediating Mediator of activation protein Medical Device Membrane Lipids Memory Metabolic Metabolic Control Methods Microbe Microbial Biofilms Microbiology Modeling Molecular Biology Molecular Profiling Nature Neurology Nobel Prize Noise Nonlinear Dynamics Norfloxacin Numbers Nutrient Nutritional Operon Organ Oryctolagus cuniculus Oxidation-Reduction Pathway interactions Patients Personal Satisfaction Pharmaceutical Preparations Physiological Pilot Projects Play Poisoning Population Population Control Population Heterogeneity Preparation Prevalence Probability Process Production Property Protein Biosynthesis Protein Synthesis Inhibition Proteins Quinolone Antibiotic Quinolones RNA Range Rate Reaction Reactive Oxygen Species Regulator Genes Regulatory Pathway Replication-Associated Process Repression Research Resistance Risk Role Route SOS Response Science Scientist Sensory Sequence Analysis Series Shapes Shotguns Signal Transduction Son of Sevenless Proteins Staging Stress Stroke Structure Study Section Sulfur Superoxides Synthetic Genes System Systems Biology Techniques Technology Testing Therapeutic Intervention Thinking Time Toxic effect Toxin Universities VAI-2 Work abstracting aminoacid biosynthesis antimicrobial bactericide base biocomputing biological adaptation to stress comparative concept design design and construction drug development drug discovery falls foot sole gene function genome sequencing improved in vivo inhibitor/antagonist innovation insight interest interfacial killings medical schools microbial model design mutant network models neurophysiology novel novel therapeutics prevent programs protein metabolite quorum sensing repaired research study resistance mechanism response sudden cardiac death synthetic biology tool transcription factor transmission process

项目摘要

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.

该项目的目的是使用创新的系统生物学和合成生物学方法来定量表征和分析细菌调节网络的细菌基因调节网络对抗生素的反应，持久的形成和抵抗的出现。与抗生素耐药菌菌株的惊人传播，更好地理解特定将来需要导致细胞死亡的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方法是辨别基因，蛋白质和途径之间的相互作用的方法我们对细菌如何反应和抗生素的理解。这遗传网络基础逻辑的含义很难通过仅实验技术，在许多情况下，成功的方法将涉及工会新实验和计算建模技术。为了解决这个问题，我们有开发的计算实验方法，可以构建定量模型基因，蛋白质和代谢物调节网络的使用，没有表达测量，没有先验有关网络结构或功能的信息。在这个项目中，我们将使用这些方法来逆向工程师细菌基因调节网络对抗生素的反应，抗生素，毅力的形成和抵抗的出现。最终的网络和将分析途径，以深入了解相关生物学的调节控制流程和网络模型将用于确定关键调节器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对药物发现的新靶标的鉴定，例如克服的靶标细菌保护机制或激活细菌编程的细胞死亡。这个项目可能因此，可以开发新颖的抗生素类别，以说明并利用遗传网络的复杂调节特性。