Computational modeling of bacterial metabolism offers a promising approach to predict strain-to-strain variation in metabolic capabilities and microbial strategies used during host association. The number of available published genome-scale metabolic models (GEMs) that have been validated against experimental data has grown recently to encompass ~50 strain-specific microbial GEMs (eight of which I recently constructed, validated, and published [Baumler et al. 2011 BMC Systems Biology]), and they capture the metabolic capabilities of numerous microbial taxa important to human health, biotechnology and bioengineering.
Systems biology combines computational and experimental approaches to study the complexity of biological networks at a systems level, where the cellular components and their interactions lead to complex cellular behaviors. Genome-scale biological networks have proven useful for interpreting high-throughput data and generating computational models. Mathematical models are constructed from network reconstructions, and they include variables, parameters, and equations to describe the potential behavior of these networks. Numerous types of genome-scale biological networks have been constructed including metabolic, regulatory, and transcriptional and translational machinery for E. coli K-12.
The E. coli K-12 GEM has been used to engineer strains to increase valuable product formation, facilitate enzyme function discoveries, provide insight into the genome evolution of other enterobacteria, and improve the understanding of the connectivity of metabolic reactions within the cell. Furthermore, computational metabolic models can be validated and refined by comparing in silico predictions with experimental data, where the discovery of disagreements or incorrect in silico predictions can lead to improvements and/or hypotheses about component interactions and unknown network components.
An iterative process thus develops where the models are used to validate model predictions, analyze experimental data, and to develop hypothesis for additional biological discovery. Such approaches have proven successful for updates to the E. coli models for regulation and metabolism.
Currently the construction of metabolic networks relies primarily on information derived from genome annotations, enzymatic/pathway databases, and published literature. By combining these resources, the elementally- and charged-balanced reactions catalyzed by enzymes associated with a given gene can be identified. These reactions incorporate pertinent information such co-factors, substrates, products, reversibility, stoichiometry, and subcellular location.
A genome-scale metabolic network contains a list of reactions, as well as the gene to protein to reaction (GPR) associations, and is used to formulate constraint-based GEMs. By comparing GEMs for pathogenic and non-pathogenic strains of related organisms, metabolic differences can be identified that may lead to the development of new control strategies for associated diseases.
The central focus of my research group will consist of three core areas that use systems biology computational methods to:
-Study infectious disease of microbial pathogens, such as E. coli O157:H7, avian pathogenic E. coli, and Salmonella spp. during interactions with respective bovine, avian, and human hosts through a combination of experimental and computational methods
-Compare the physiology of numerous foodborne microbial pathogens through experimental and computational analysis using genome-scale metabolic models
-Infer metabolism of ancient/ancestral organisms of foodborne pathogens through a new sub-field that I have initiated called “paleo systems biology” that involves studying genome-scale metabolic models of ancient biological organisms to investigate how genera or families of related organisms have evolved with metabolic changes into pathogens or non-pathogens that live in various niches in plant, insect, avian, or mammalian hosts