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.
