The extension, application, and generalization of a phage T7 intracellular growth model

I used bacteriophage T7 as a model system to explore how the genetic information encoded in a genome determines the phenotype of an organism in a given environment. By incorporating the existing experimental data and mechanisms, our group previously developed a genetically structured model of T7 intracellular growth. I extended and improved the model by recasting it in an object-oriented framework, by accounting for the host physiology, and by implementing a more mechanistic description of several steps of T7 infection.; I used the revised T7 model to explore several biological questions that have broad relevance. I examined the effects of host physiology on T7 growth. This work provided insight into the interplay between the genome of an organism and its growth environment. Next I probed the design features of T7 by investigating the response of phage T7 to perturbations in its parameters and genomic structure. This work led to a better understanding of the design principles of a biological system in the context of its environment. Furthermore, I employed the model to investigate the interactions among deleterious mutations at the population level. This work had profound implications in elucidating many important biological phenomena, such as the evolution of sex. Finally, the T7 model generated as a byproduct the time-series of T7 mRNAs and proteins. I employed these data to evaluate a novel data-mining algorithm, which is potentially useful to interpret the gene expression data being produced from high-throughput techniques, such as DNA microarrays and protein 2D gel electrophoresis.; To facilitate the modeling of biological systems in general, I have developed a program called Dynetica—a biologist-friendly simulator of dynamic networks. Dynetica provides an intuitive environment for constructing, and analyzing kinetic models of biological systems. I expect that Dynetica, along with other modeling tools, will synergistically benefit biological research at large by serving as a computational framework for creating and sharing mathematical models.