权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结/文摘