From genetic architecture to adaptation dynamics

描述（由申请人提供）： 拟议的理论和实验研究的总体目标是阐明遗传结构与细胞群体适应环境挑战的能力之间的复杂关系。适应与调控网络的动态紧密相连，而调控网络反过来又决定了每个细胞的表型。然而，细胞群体是异质系统，在这个意义上，表型反应在遗传上相同的细胞之间变化。 为了研究细胞群体的异质性及其与基因表达调控的关系，我们开发了一个通用的细胞群体平衡建模框架及其有效模拟所需的数值算法。我们还采用流式细胞仪和显微镜研究了大肠杆菌细胞群的分布特征。大肠杆菌细胞包含一个人工遗传网络，称为遗传切换，由两个启动子-阻遏物对和作为报告基因的绿色荧光蛋白（GFP）组成。实验和仿真结果都表明，忽略种群的异质性会导致预测系统行为的定性和定量错误。此外，我们将我们的建模和计算框架应用于充分表征的乳糖操纵子系统，并研究了a）系统参数如结合亲和力和启动子强度，B）操作条件和c）网络拓扑结构的特定修饰对细胞表型分布的影响。此外，建模/计算框架被用来研究初始细胞分布特性的适应动力学的依赖性。 我们的初步研究导致了以下假设的形成：A）调节网络的结构以及它们的系统特征，将极大地影响异质细胞群体的分布特征和B）异质细胞群体的初始分布特征将对适应环境条件突变的动态产生深远的、可预测的影响。为了验证这些假设，我们建议开发一个集成的建模和实验框架。我们将使用乳糖操纵子遗传网络以及它与两个特定的人工遗传网络的组合作为我们的模型系统。建模和实验结果之间的比较将用于模型验证和细化。

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.