Predictive biophysical models of evolution

进化的预测生物物理模型

基本信息

批准号：
9234799
负责人：
EUGENE I SHAKHNOVICH
金额：
$ 56.94万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-04-01 至 2020-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9234799
关键词：
Animal Model Antibiotic Resistance Antibiotics Area Binding Biochemistry Biological Biology Biophysics Budgets Case Study Cell model Chemicals Clinical Communities Cytoplasm Demography Development Dihydrofolate Reductase Directed Molecular Evolution Drug resistance Electrostatics Elements Environment Enzymes Escherichia coli Escherichia coli Proteins Event Evolution Experimental Models Foundations Genes Genetic Genetic Epistasis Genetic Variation Genomics Genotype Goals Immune response Induced Mutation Laboratories Malaria Metabolic Metaphor Methodology Microbe Microfluidics Modeling Molecular Mutate Mutation Nucleotides Organism Outcome Peptide Hydrolases Pharmaceutical Preparations Phenotype Population Population Dynamics Positioning Attribute Process Property Protein Engineering Proteins Proteomics Quality Control Reproducibility Research Resistance Resolution Robotics Role Schedule Stress Structure System Systems Analysis Technology Temperature Time Translational Research Trimethoprim Viral Work adenylate kinase biophysical model biophysical properties biophysical tools cancer therapy chaperonin computerized tools experimental study fighting fitness genome editing genome sequencing insight laboratory experiment loss of function multi-scale modeling mutant novel strategies pathogen population based predictive modeling resistance mutation response simulation stressor temporal measurement theories tool trait tumor progression whole genome

项目摘要

Project Summary/Abstract The overarching goal of the proposed research is to develop predictive multiscale biophysical models of adaptive evolutionary dynamics. In earlier work we demonstrated for several cases of biomedical importance that fitness effect of genetic variation can be accurately predicted from a unique combination of molecular traits of the mutated protein. This finding transforms the concept of fitness landscape from an artful metaphor into a quantitative tractable tool to predict the genotype-phenotype relationship (GPR). Here we take these findings as a foundation to further extend our understanding of interplay between biophysical and population factors that determine the dynamics and outcome of adaptive evolution. We will apply microfluidics and automatic robotics setup along with protein engineering and genomic editing tools to explore evolutionary dynamics in laboratory experiments under conditions that allow tight control on all scales – from molecules to populations. To that end, we carry out a set of evolution experiments with adapting populations of E. coli escaping from antibiotic stress and structural instability of the essential protein Dihydrofolate Reductase. We characterize on all scales – genotyping, molecular traits, systems proteomics and population - multiple evolutionary paths to resistance and adaption of emerging bacterial strains and determine at which level of description (genotype, biophysical properties, systems responses) evolution becomes reproducible – and by implication predictable. In parallel we model the evolutionary dynamics using multiscale models where cytoplasm of model cells is presented in a biophysically realistic manner, and fitness of model organisms is predicted from its molecular traits using experimentally derived GPR. Molecular traits of mutant forms are predicted using state of the art computational tools of molecular biophysics allowing reproducing and predicting complete evolutionary trajectories of adapting populations of model cells. A tight integration between theory and experiment will provide an opportunity to develop predictive evolutionary models of ever increasing accuracy and realism. Progress along these lines will transform our approaches to study evolutionary dynamics from descriptive into predictive and quantitative, which will be instrumental to the development of novel approaches to fight antibiotic resistance and, potentially, viral escape from stressors such as drugs and immune response.

项目概要/摘要拟议研究的总体目标是开发预测性多尺度生物物理在早期的工作中，我们演示了几种自适应进化动力学模型。生物医学上的重要性是，可以从遗传变异的适应性效应中准确预测这一发现改变了突变蛋白分子特征的独特组合。健身景观的概念从艺术隐喻转变为易于预测的定量工具在这里，我们以这些发现为基础来研究基因型-表型关系（GPR）。进一步扩展我们对生物物理因素和人口因素之间相互作用的理解我们将应用微流体技术来确定适应性进化的动力学和结果。自动机器人设置以及蛋白质工程和基因组编辑工具可供探索在严格控制所有条件下的实验室实验中的进化动力学尺度——从分子到群体。为此，我们进行了一系列的进化。适应逃避抗生素应激和结构性大肠杆菌种群的实验我们在各个方面表征了必需蛋白质二氢叶酸还原酶的不稳定性—— 基因分型、分子特征、系统蛋白质组学和种群——多重进化路径新兴细菌菌株的抗性和适应，并确定在哪个水平描述（基因型、生物物理特性、系统反应）进化成为可重复 - 并且意味着可预测。使用多尺度模型，其中模型细胞的细胞质以生物物理现实的方式呈现方式，并使用模型生物的分子特征来预测其适应性使用最先进的技术预测实验衍生的 GPR 突变形式的分子特征。分子生物物理学的计算工具可以再现和预测完整的适应模型细胞群体的进化轨迹。理论和实验将为开发预测进化模型提供机会不断提高的准确性和现实性将改变我们的方法。研究从描述性到预测性和定量的进化动力学，这将是有助于开发对抗抗生素耐药性的新方法，病毒可能会逃避药物和免疫反应等压力源。