Regulatory networks for integration of catabolism and behavior in a complex chemotaxis system

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Aromatic compounds are among the most prevalent and persistent pollutants in the environment. A group of bacteria called the pseudomonads are adept at detecting and degrading aromatics, and hence have potential as agents for the bioremediation of sites contaminated with aromatic compounds. The aim of this project is to understand how pseudomonads sense, respond and break down aromatic compounds using experimental and computational approaches. This study will contribute to the development of open source modeling software that will be made available to the general scientific community. The project will also provide interdisciplinary research training to students at the high school, college and postgraduate levels.While many aspects of bacterial chemotaxis systems are conserved, previous work from this group demonstrated that the aromatic acid chemotaxis system in Pseudomonas putida is different in that it is intricately linked to both uptake and catabolism of aromatic substrates. This project will utilize a newly developed 'phenotype-centric' mathematical algorithm to model the complex integrated gene circuitry controlling aromatic acid chemotaxis, transport and catabolism in P. putida. This systems-level quantitative mathematical modeling strategy will be used in combination with experimental analysis to dissect these interacting phenotypes. The modeling system will be used to predict the complex temporal series of gene expression events that underlie the sophisticated gene circuitry and resulting cellular phenotypes. The study will test the idea that control of inducer production modulates entry of substrates into the cell through the action of additional transporters. The merit of this study lies in its interdisciplinary approach to understanding the complex relationship between genotype and phenotype. This integrated approach to model prediction and experimental testing of a complex biochemical system has broad implications for organisms in complex microbiomes. The quantitative analysis of this complex integrated system will allow for further refinement of the open-source modeling software tool 'System Design Space Toolbox', thereby increasing its utility to the general scientific community for systems and synthetic biology studies.