Microbial metabolic models
The primary research goal of this project is to pioneer a systems biology approach to build and utilize a predictable-genome scale model for the lesser-characterized organism C. beijerinckii in order to enhance butanol production for use as a chemical feedstock and a second-generation biofuel. Specific goals include: (1) reconstruct and experimentally validate the first computable genome-scale metabolic network of Clostridium beijerinckii; (2) generate and integrate metabolomics data to enhance the metabolic reconstruction and to monitor changes in metabolism through various fermentation processes; (3) build a Matlab-based tools to assist other groups in rapidly generating new metabolic models, automating the process where possible; and (4) use the computational model to guide the construction of modified C. beijerinckii strains with enhanced butanol production capabilities.
Cell Type & Tissue-specific Metabolic Models
The reconstruction of global models of human metabolism—encompassing the full metabolic capability encoded in the genome—enables systems approaches to study the mechanistic basis of a variety of diseases. However, because each individual tissue and cell type expresses a fraction of all metabolic genes, these specific contexts are important in characterizing metabolism in any diseases with defined origins. We have developed a powerful new tool for rapid, evidence-driven reconstruction of cell type and tissue-specific metabolic networks in higher organisms. We have used the metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE) tool with transcriptomic evidence to generate draft metabolic networks for 128 tissues and cell types. With this comprehensive set of context-specific metabolic models, we can start to evaluate global properties of these networks and their relationship to human metabolism in the body.
Metabolic Network Models in Cancer
Altered function in several metabolic pathways has been shown to be critical in the development of many cancers. We are using metabolic network modeling as a framework to unveil metabolic signatures for physiologically distinct cancer types, identify key metabolic processes involved in tumorigenesis, and identify drug targets for tailored treatment of cancer.
We are working to systematically identify metabolic pathways essential for breast cancer growth and cancer subtype-specificity. Genome-scale metabolic networks for 6 commonly used breast cancer cell lines representing different breast cancer subtypes have been reconstructed using the mCADRE method by integrating transcriptomic and metabolomics data sets. A metabolic model for the non-transformed MCF10A cell line is also built. All cell line-specific metabolic models have been validated against shRNA knockdown data and can be used to simulate growth under typical cell line culture medium. Further analysis of this panel of breast cancer cell-line specific metabolic models may lead to potentially selective drug targets that may be tailored for the treatment of specific subtypes of breast cancer.