Bacterial Thermodynamics for Modeling Biodegradation of Anthropogenic Compounds

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

0219330 VanBriesen The goal of the proposed research is to improve biological models for prediction of microbiological growth yield and stoichiometry in systems involving in situ bioremediation or ex situ biotreatment of anthropogenic compounds. A series of hypothesis-driven tasks are designed to evaluate current available yield, thermodynamic, and quantitative pathway information for anthropogenic compounds and to develop improved microbiological models for these compounds. The crux of the work depends upon the hypothesis that maximum theoretical bacterial yield is a fundamental parameter of cell growth that controls the removal of anthropogenic compounds by biodegradation pathways in natural and engineered systems. Further, bacterial yield, coupled with the principle of mass balance, specifies the stoichiometry of biodegradation, thus allowing prediction of the formation of intermediates, the requirement of co-substrates and nutrients, and the utilization or generation of inorganic species (e.g., O2, NH4 + ) that can affect the system biogeochemistry. Specific research objectives are to: 1. Evaluate the available yield data for microbiological systems growing under a wide range of environmental conditions and using a wide variety of natural and anthropogenic compounds. Compile these data into a searchable, web-enabled database for microbiological stoichiometric and kinetic data. 2. Modify an existing thermodynamic efficiency-based yield and stoichiometry prediction methodology to account for biodegradation mechanisms observed for anthropogenic compounds. 3. Evaluate the predictive ability of the newly developed model for multiple microbial systems using the database generated in objective 1. The improved model structures developed on this project will have widespread utility in biological ecosystem modeling, bioremediation and microbial ecology modeling, and biotechnology and engineered bioprocess modeling. Modeling in all these fields will improve our understanding of the complexity of living systems and will enhance our ability to control this complexity to our benefit.