Learning models of metabolism and gene expression from biological big data

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Background Cell metabolism consists of thousands of biochemical reactions needed to sustain vital cellular processes. The metabolic capabilities of a cell are constrained by the repertoire of enzymes expressed. Computational models such as genome-scale metabolic models integrate metabolism with gene expression to predict phenotype from genotype. Genome-scale metabolic models predict cell phenotype by formulating the metabolic response as an optimization model, driven by a biochemical goal (objective function) while being subject to constraints: physicochemical properties, thermodynamics, and gene regulation. These models have been applied successfully to produce valuable chemicals from renewable resources, and for knowledge advancement in the life sciences and bioengineering since the early 90s. Research program The ultimate goal of this research program is to develop computer-aided design (CAD) tools to predictively design genetically engineered cells for the production of chemicals, fuels, and biopharmaceuticals. A prerequisite is having accurate models of cell metabolism and gene expression. Recent modeling advances allow E. coli protein expression to be predicted with up to 85% coverage. However, for other biotechnologically important organisms like yeast or human cells, the higher biological complexity and relatively sparser mechanistic knowledge makes achieving such broad model scope challenging. This program develops new CAD and modeling tools for E. coli, yeast, and human cells. To address the vastly different biological complexity and available knowledge across these organisms, both data-driven and mechanistic (knowledge-driven) modeling approaches are developed. 1) For human cells, for which mechanistic knowledge is the sparsest, new algorithms learn optimization models directly from 'omics' data (transcriptomics, proteomics, fluxomics). 2) For yeast, a new multiscale model is constructed that integrates metabolism and gene expression. 3) For E. coli, for which we previously developed advanced mechanistic models, CAD tools are developed and used to produce valuable proteins using model-designed strains. The developed software will be distributed freely for the research community. Benefits to the research community and Canada 1) The modeling methods can be used to advance knowledge of any organism given omics data of multiple types. 2) The new cell design tools can be applied to multiple industries including biopharmaceutical manufacturing, and waste conversion to chemicals or fuels, and to multiple platform organisms (E. coli, yeast, human cell lines). 3) I will train highly qualified personnel in computational systems biology, genomics, and optimization algorithms.