Patterns of Adaptive Evolution

适应性进化的模式

• 批准号: 8663921
• 负责人: Craig R Miller
• 金额: $ 27.61万
• 依托单位: UNIVERSITY OF IDAHO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8663921
• 关键词: Affect; Awareness; Back; Bacteria; Bacteriophages; Biological; Biological Assay; Biological Models; Boxing; Communicable Diseases; Data; Engineering; Environment; Error Sources; Evolution; Failure; Frequencies; Future; Genetic Epistasis; Genome; Genome engineering; Genotype; Goals; Growth; Human; Immune system; Knowledge; Laboratories; Libraries; Life Cycle Stages; Light; Maps; Mathematics; Measures; Medicine; Modeling; Modification; Molecular; Motivation; Mutation; Organism; Pathway interactions; Pattern; Phenotype; Play; Population Sizes; Property; Proteins; Protocols documentation; Research; Role; System; Technology; Testing; Theoretical model; Time; Vaccines; Viral; Virus; Walking; Work; base; design; fighting; fitness; flexibility; life history; mathematical model; microbial; next generation sequencing; novel; pathogen; pleiotropism; predictive modeling; research study; simulation; success; theories; trait

DESCRIPTION (provided by applicant): Project Description With growing awareness of how pathogen adaptation impacts the battle against infectious disease, mathematical models of adaptation have become central to this fight. However, most of the theoretical work focuses on general patterns of adaptation, while the empirical work provides rich details specific to the pathogen under study. For those who wish to predict adaptation of a specific pathogen, the extensive biological information cannot be easily incorporated into existing models. The long term goal of this research is to develop a flexible framework for predicting evolution that is rich enough to accommodate empirical data from organisms that evolve in real time. The 'GPF' model proposed here builds on the knowledge that genotypes (G) affect phenotypes (P) and phenotypes affect fitness (F). This framework traces back to Fisher's geometric model, which serves as a baseline for comparison. There are three Aims. Aim 1: Test three key assumptions of the geometric model on viral phenotypes and fitness. In the G to P part of the GPF model, the assumptions that mutations show universal pleiotropy at the phenotype level and that phenotypic effects of mutations are additive at the phenotype level are tested. In mapping P to F, the model departs from the standard assumption of a multidimensional Gaussian function by allowing the relationship to emerge from a basic life-cycle model with observable phenotypes as predictors of fitness. These assumptions will be tested using a well-developed viral model system for which a large library of previously observed adaptive mutations is available. A subset of mutations will be engineered into single, double, and triple mutation combinations and assaying each at six phenotypic traits plus fitness. Aim 2: Synthesize the results of Aim 1 into a unified model, make predictions about adaptations and test them. Biologically reasonable modifications will be evaluated through model selection. Mathematical simulations under the refined GPF model will be used to make quantitative predictions about important general properties of adaptive walks, and these properties will be tested by carrying out adaptation in the laboratory. The model will be evaluated based on how close predictions match observed data. Aim 3: Use the unified model to design genomes and test predicted fitness. In this Aim, the GPF model will be refocused from general patterns to specific predictions about the phenotypes and fitnesses. Multistep genotypes will be engineered from the single mutations tested in Aim 1, their phenotypes and fitnesses assayed, and the results compared to predictions. Next, the growth environment will be altered in a specific way and the GPF model will have the more challenging task of predicting what multistep genotypes will have high fitness in the novel environment. These genotypes will be engineered, and their fitness assayed and compared to the GPF predictions and to laboratory adaptations in the novel environment. Finally, the predictive successes and failures will be critically evaluated to shed light on how future research can advance the larger goal of producing a predictive model of microbial evolution useful to the study of human pathogens.

描述（由申请人提供）：项目描述随着病原体适应如何影响与传染病的斗争的越来越认识，适应性的数学模型已成为这场战斗的核心。但是，大多数理论工作都集中在适应的一般模式上，而经验工作则提供了针对正在研究的病原体的丰富细节。对于那些希望预测特定病原体的适应的人，广泛的生物学信息不能轻易地纳入现有模型中。这项研究的长期目标是开发一个灵活的框架来预测足够丰富的进化，以适应实时进化的生物体的经验数据。这里提出的“ GPF”模型基于以下知识：基因型（G）影响表型（P）和表型会影响适应性（F）。该框架可以追溯到费舍尔的几何模型，该模型是比较的基线。有三个目标。 AIM 1：测试几何模型对病毒表型和适应性的三个关键假设。在GPF模型的G到P的一部分中，突变在表型水平上显示普遍的多效性的假设以及突变的表型效应在表型水平上是加性的。在映射p到f中，该模型通过允许这种关系从具有可观察到的表型作为适应性的基本生命周期模型中出现，偏离了多维高斯函数的标准假设。这些假设将使用良好的病毒模型系统进行测试，该模型系统可提供一个先前观察到的自适应突变的库。突变的子集将被设计为单个，双重和三突变组合，并在六个表型性状和适应性下进行分析。 AIM 2：将AIM 1的结果综合为统一模型，对适应进行预测并测试它们。生物学合理的修改将通过模型选择评估。精制GPF模型下的数学模拟将用于对自适应步行的重要一般特性做出定量预测，这些特性将通过在实验室进行适应来测试。将根据观察到的数据匹配的近距离预测如何评估该模型。 AIM 3：使用统一模型设计基因组并测试预测的健身。在此目标中，GPF模型将从一般模式重新聚焦到有关表型和健身的特定预测。多步基因型将从AIM 1中测试的单个突变，它们的表型和健身测定以及与预测相比的结果进行设计。接下来，增长环境将以特定的方式改变，GPF模型将具有更具挑战性的任务，以预测哪些多步型基因型在新型环境中具有很高的适应性。这些基因型将进行设计，并将其适应性测定，并将其与GPF预测和新型环境中的实验室适应进行比较。最后，将对预测的成功和失败进行严格评估，以阐明未来的研究如何促进产生对人类病原体研究的微生物进化预测模型的更大目标。