SBIR-STTR Award

With the completion of the human genome project and recent advances in high throughput technologies, much of the ongoing effort is now to find all the functional elements on the sequence and to create computational tools to analyze and interpret the large volume of data available. This overwhelming amount of information together with the complexity of biological systems has created a need for in silico modeling and the development of model-driven systems biology. The reconstruction of the reaction networks that these components form allows for the formulation of in silico models. Investing on our success in modeling microbial cells using constraints-based approach, we intend to assess the scientific and computational feasibility of building the first genome-scale metabolic model for human cells. We will build two cell-specific metabolic models, test and characterize them, and define the issues associated with building multi-cellular metabolic models. Given the prevalence of metabolic involvement in human disease, this effort is both timely and of great significance. The integration of the sequence annotation, the association relationships for splice variants, data representation and computational issues will be addressed at the completion of this project. Success of this proposal would build the foundation for the development of a comprehensive model of human metabolism that would be extremely valuable for drug discovery and development efforts and has the potential to drive the process of therapeutic research

At Genomatica, we have developed a novel technology platform, called SimPheny that enables the efficient development of genome-scale models of metabolism and their simulation using a constraint-based modeling approach. In the Phase I of this SBIR program, we demonstrated the scientific and technical feasibility of extending our modeling approach from single-cell microbial networks to multi-cellular human metabolism using an integrated two-cell model of human adipocyte (fatty cell) and myocyte (muscle cell). Using the reconstructed multi-cellular model, we computed the integrated function of the two cell types in SimPheny and formulated hypotheses that may be verified experimentally and increase our understanding of human metabolism and physiology. Based on our initial success with modeling multi-cellular human metabolism in SimPheny and the growing market demand for in silico modeling and data integration platform, we have initiated a 3-5 year plan to develop a computational platform for modeling higher eukaryotes and to construct cell-, tissue-, and disease-specific models for human metabolism. As a part of this long-term plan, we seek to develop a computational infrastructure and biological content for reconstructing and modeling integrated multi-cellular human networks in this Phase II SBIR program. The overall goal of this Phase II proposal is to develop a data-integration and computational software platform and a comprehensive database for human metabolism that accounts for the annotated human genes, proteins, and metabolic pathways. Upon the successful completion of this Phase II proposal, we will have a complete infrastructure for content management, data analysis, and visualization of high throughput data within SimPheny software platform and a comprehensive compendium of network information for human metabolism, as well as an expanded genome based integrated multi-cellular model for adipocyte-myocyte metabolism. The infrastructure and content developed in this proposal will enable us to develop tissue- and disease-specific models in collaboration with academic, biotechnology, and pharmaceutical research groups in the future