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

From genetic architecture to adaptation dynamics

从遗传结构到适应动力学

基本信息

批准号：
7267714
负责人：
KATHLEEN S MATTHEWS
金额：
$ 28.33万
依托单位：
RICE 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：
2004-08-01 至 2009-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7267714
关键词：
Accounting Affect Affinity Algorithms Architecture Behavior Binding Biological Models Cell Adhesion Cell Cycle Kinetics Cells Characteristics Complex Condition Coupled Coupling Data Dependence Drug Formulations Equilibrium Experimental Models Flow Cytometry Fluorescence Microscopy Gene Expression Regulation Genes Genetic Genetic Heterogeneity Germ Cells Green Fluorescent Proteins Individual Lac Operon Microscopy Modeling Modification Molecular Biology Techniques Phenotype Population Population Distributions Population Heterogeneity Regulator Genes Relative (related person)Reporter Research Personnel Source Structure Surface Synthetic Genes System Techniques Temperature Testing Validation Variant Video Microscopy base cell population study computer framework environmental change neglect programs promoter research study response simulation

项目摘要

DESCRIPTION (provided by applicant): The overarching objective of the proposed theoretical and experimental studies is to elucidate the complex relationship between genetic architecture and the ability of a cell population to adapt to environmental challenges. Adaptation is tightly coupled with the dynamics of regulatory networks, which, in turn, determine the phenotype of each individual cell. However, cell populations are heterogeneous systems in the sense that phenotypic responses vary between genetically identical cells. To study cell population heterogeneity and its relationship to regulation of gene expression, we developed a general cell population balance modeling framework and the numerical algorithms necessary for its efficient simulation. We also employed flow cytometry and microscopy to study the distribution characteristics of E.coli cell populations. The E.coli cells contained an artificial genetic network, known as the genetic toggle, consisting of two promoter-repressor pairs and the green fluorescent protein (GFP) as a reporter. Both the experimental and simulation results indicated that neglecting population heterogeneity leads to significant qualitative and quantitative errors in predicting system behavior. In addition, we applied our modeling and computational framework to the well-characterized lac operon system and studied the effects that a) systemic parameters such as binding affinities and promoter strengths, b) operating conditions, and c) specific modifications of network topology have on the distribution of cellular phenotypes. Moreover, the modeling/computational framework was used to study the dependence of adaptation dynamics on the initial cell distribution characteristics. Our preliminary studies led to the formulation of the following hypotheses: A) The structure of regulatory networks as well as their systemic characteristics, will vastly influence the distribution characteristics of heterogeneous cell populations and B) The initial distribution characteristics of heterogeneous cell populations will have a profound, predictable impact on the dynamics of adaptation to sudden changes in environmental conditions. To test these hypotheses, we propose to develop an integrated modeling and experimental framework. We will use the lac operon genetic network as well as its combination with two specific artificial genetic networks as our model systems. The comparison between modeling and experimental results will be used for model validation and refinement.

描述（由申请人提供）：