Modeling and Evolution of Biological Networks

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TECHNICAL SUMMARY:This award supports theoretical research and education in modeling and evolution of biological networks. The research undertaken addresses evolution of genetic patterns using theoretical models that represent the genetic code and the changes that are possible while selecting changes as favorable with a ''fitness function.'' The theoretical approach draws on analogies with learning models in Computer Science and optimization in Statistical Physics. This theoretical machinery predicts biological networks by how rapidly they can be learned from the random examples provided by mutation and selection. The approach assumes the networks in living things can be built incrementally and grow continually by stepwise increases in fitness. They are not necessarily global optimum. Network evolution will be modeled with simplified differential equations for the central molecular components of developmental pathways, e.g., transcription factors, ligand-receptors interactions, protein-protein complexes, kinases and phosphorylated proteins etc. Evolution requires a fitness function and rapid evolution is facilitated by a smooth monotone function, not a jagged landscape. This is in accord with the goal of evolving patterning networks common to all animals, not specific phyla. A plausible fitness function quantifies how well embryonic position is related to gene expression patterns. This research follows a preliminary application of these ideas to periodic segmentation in animals (somitogenesis) where the earliest versions of the approach found an encouraging degree of success.The effort undertaken has broader impacts with both scientific and educational consequences. The research, employing the approaches from a condensed matter physics perspective, takes place at Rockefeller University in an environment focused on biological studies. Graduate students involved in the research gain a unique interdisciplinary education with the foundations of theoretical physics immersed in the research environment of biological sciences. The research makes contributions to the scientific community beyond publishing and the usual forms of dissemination. The bioinformatics tools employed in the modeling and analysis are packaged into web sites for broad dissemination. The relevance of the tools developed to real world medicine were illustrated in a paper where this group collaborated in tracking the evolution of drug resistant Staphylococcus aureus (''Super bugs'') in a human patient by whole genome resequencing.NONTECHNICAL SUMMARY:This award supports theoretical research and education in modeling and evolution of biological networks. The research models evolution of genetic patterns using theoretical models that represent the genetic code and the changes that are possible while selecting changes as favorable with a ''fitness function.'' The theoretical approach draws on analogies with learning models in Computer Science and optimization in Statistical Physics. This theoretical machinery predicts biological networks based on how rapidly they can be learned from the random examples provided by mutation and selection. The approach assumes the networks deployed in living things can be built incrementally and grow continually by stepwise increases in fitness. They are not necessarily global optimum. Network evolution will be modeled with simplified equations for the central molecular components of developmental pathways. This research follows a preliminary application of these ideas to periodic segmentation in animals (somitogenesis) where the earliest versions of the approach found an encouraging degree of success.The effort undertaken has broader impacts with both scientific and educational consequences. The research, employing the approaches from a condensed matter physics perspective, takes place at Rockefeller University in an environment focused on biological studies. Graduate students involved in the research gain a unique interdisciplinary education with the foundations of theoretical physics immersed in the research environment of biological sciences. The research makes contributions to the scientific community beyond publishing and the usual forms of dissemination. The bioinformatics tools employed in the modeling and analysis are packaged into web sites for broad dissemination. The relevance of the tools developed to real world medicine were illustrated in a paper where this group collaborated in tracking the evolution of drug resistant Staphylococcus aureus (''Super bugs'') in a human patient by whole genome resequencing.