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.
My group works at the interface of the physical sciences and Cell and Developmental Biology. We generally use systems that have been already well characterized by biologists by traditional genetics and assays on populations of cells and then generate more quantitative data on single cells as a function of time. From these movies of cell behavior we can make quantitative models that integrate the cooperative action of many genes into a few parameters, and make predictions. Applications have included: 1. Quantifying the output from random libraries of regulatory DNA in yeast. The task is analogous to learning a language, that of regulation, by presenting randomly generated sentences to an oracle (the yeast cell) and getting a quantitative response ('not a valid sentence', or 'sentence requests amount X'). From these responses we have to infer the grammar of the language. 2. Phase locking the yeast cell cycle. Our circadian biological clocks are phase locked to the 24 hr light-dark cycle (jetlag ensues when we are not phase locked) , can the same locking be achieved for another periodic event in biology, the cell division cycle? We have done this and made a simplifed model to explain the effect. 3. Are there any principals that govern gene networks that perform a specified signaling task, ie respond to one type of signal (and not others) and extract a given component of it? We have designed a computer algorithm to evolve genetic networks and find unexpected solutions to biological design problems. These projects have all been carried out by students and postdocs from Physics and Math backgrounds generally working collaboratively with biological labs at Rockefeller University. They contribute quantitive methods and hardware to their respective labs and learn how to handle the bio-materials and cells from professionals. They typically move on to faculty positions in the life sciences. My group thus helps bridge the interface between the quantitative sciences and biology which is a priority for the NSF and NIH.
