Elucidation of the Cardiac Myogenesis Regulatory Network

Heart development has been extensively studied in numerous organisms throughout the twentieth century. The timing of key inductive signals and the expression of many critical transcription factors have been mapped across a variety of model systems. A collective image of the various stages of cardiac development is beginning to emerge. Although most of the seminal events are conserved across evolution, it is increasingly clear that subtle differences can have substantive effects on models of heart development processes. Furthermore, the overwhelming majority of work contributing to these models has been performed on a gene-by-gene basis. As a result, we have a loosely stitched cross-evolutionary view of cardiogenesis that leaves much to be desired by way of completeness. Thus, in order to move toward a comprehensive model of heart development, we have a critical need for global network views of heart development processes conducted within one species.Cardiac myogenesis, the development of heart muscle cells, is the earliest heart development process and is required for the formation of all adult heart structures. Key signaling pathways, and their precise timing and targets, have only recently begun to be defined. The downstream targets of these pathways and their timing of activation or repression remain largely unknown. To address this, I compiled data from three genomic microarray studies, each addressing a distinct aspect of cardiac myogenesis signaling and expression, to construct a global preliminary network of the primary inductive signals and their downstream targets in the chick model embryology system.The preliminary cardiac myogenesis network obtained from these studies generates far too many hypotheses to test experimentally. The challenge that lies ahead for elucidating the fine structure of this, or any network model, is in determining the next most enlightening experiments. Headway in sorting out more profitable experiments can be made by selecting from among the universe of known interaction data as well as taking advantage of a property selected for throughout evolution - robustness. Network robustness is loosely defined as the ability of a network to maintain input and output properties in the face of perturbation. It is unsurprising that evolution would sculpt such a characteristic into molecular networks required to perform a task in varied environmental and genetic circumstances. However the way in which evolution has engendered this quality has opened the door to an exciting new avenue for in silico experimentation.I present in this dissertation the beginnings of a collaborative project for biological network elucidation software called BioNET. The long-term goal of BioNET is to take a description of a network model and phenotype as input and return a set of candidate network models capable of more robustly producing the phenotype. Fundamental to BioNET is the ability to acquire information from the universe of known molecular interaction data for in silico experimentation in any model system. To this end, I redesigned BioNetBuilder, open-source network integration software, to transfer any and all publicly available interaction data across species and serve them via the web. As these data grow in scale, BioNET will be increasingly useful for identifying the more plausible, among possible network architectures, such as the preliminary cardiac myogenesis network presented in this dissertation