Integrated Mechanistic and Statistical Network Analysis for Biological and Medical Discovery
Series: CBE Departmental Seminars
Location: Elgin Room (E-Quad A224)
Date/Time: Wednesday, October 23, 2013, 4:00 p.m. - 5:00 p.m.
To harness the power of genomics, it is essential to link genotype to phenotype through the construction of quantitative systems models. I will discuss approaches for the creation of such quantitative models that can simulate a variety of cellular functions. I will focus particularly on automated methods for integrating metabolic and gene regulatory networks such as our approach, Probabilistic Regulation of Metabolism (PROM). PROM is notable in that it represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology models that are rarely combined quantitatively. Additionally, I will discuss our new strategy -- Gene Expression and Metabolism Integrated for Network Inference (GEMINI) -- that is the first method that curates the inference of regulatory interactions from high throughput data using metabolic networks. This novel approach provides multiple layers of biological context to the problem of regulation. Finally, I will describe our latest approach to building tissue and cell type specific metabolic models (mCADRE), which we have now done for 130 different cell types and tissues in the human body. These approaches together lay the framework for Integrated Multi-Omic Networks (IMON) that form the basis for "hybrid" models that ground data-driven statistical learning of novel hypotheses by incorporating mechanism to the extent it is known. I will discuss how we have used these types of approaches to drive forward discovery in biology and medicine.