Collaborative research: Information integration by gene regulatory networks controlling bacterial cell fate decisions

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Bacterial biofilms, which are surface associated aggregates of cells, are highly resistant to toxic chemicals such as chlorine and to many antibiotics that are used to treat human infections. This project aims to understand how cells decide to form a biofilm by analyzing a gene regulatory network that is crucial for the decision-making process. The mechanisms underlying these decision-making processes are not fully understood, even for the best-studied gene regulatory networks. Using the biofilm-forming bacterium Bacillus subtilis as a model, this research will define general principles underlying decision making by regulatory networks. B. subtilis is closely related to and shares many regulatory networks with medically important pathogenic bacteria, including Clostridium sp., B. cereus, and B. anthracis. Hence, this work may serve as a model for understanding how such bacteria decide to initiate biofilm formation. This project will provide students with interdisciplinary training in microbiology and mathematical modeling. A special emphasis will be placed on engaging underrepresented students from the University of Houston, which has been designated as a Hispanic-Serving Institution. This proposal aims to obtain a systems-level understanding of how B. subtilis cells sense starvation and make the decision to initiate either a unicellular (sporulation) or a multicellular (biofilm) differentiation program for survival. The proposal will test whether gene-regulatory networks are able to assess nutrient availability by sensing the cell growth rate. Further, it will investigate how different dynamics (i.e. modulation of activity in time) of a single master transcriptional regulator can direct cells to alternative cell fates. These questions will be addressed with a synergistic combination of systems and synthetic biology tools, including synthetic network perturbations and rewiring, single-cell imaging, statistical data analysis, and mathematical modeling. Thus, this project will illustrate various fundamental concepts of complex (but not too complex) living systems. The project will also provide abundant interdisciplinary training opportunities for the participating students and postdocs working together on experiments and/or mathematical modeling.