Abstract:  Biochemical networks frequently contain a large number of coupled chemical reactions and complex architectural features such as redundancy, feedback and crosstalk. While this complexity ensures robustness, it also complicates the reprogramming of network function. The objective of our research is to develop mathematical modeling tools to rationally reprogram intracellular carbon, energy and information flows. Toward our objective, we developed computational tools to automate model generation, identification and analysis. These tools, together with our growing database of network components, have allowed us to construct and test several large-scale human signaling models (200 - 1000 molecular components) on a rapid time-scale. In this study, we’ll discuss examples which demonstrate these tools. First, we’ll discuss a model of the signaling program driving All-Trans Retinoic Acid (ATRA) induced differentiation in HL-60 cells. HL-60 is a well established albeit simplified model of hematopoietic differentiation that we have used to understand the basic architecture of this important program. From our modeling studies, we identified a minimal memory circuit potentially responsible for the pre-commitment memory observed in RA-induced HL-60 differentiation. Second, we’ll discuss another important differentiation program, the epithelial to mesenchymal transition (EMT). Decades of work has identified the critical signaling pathways controlling EMT. However, each of these pathways is responsive to multiple ligands and display a high degree of crosstalk. We showed, using a combined computational and experimental strategy, that pathway crosstalk  between VEGFA and TGF-B1/2 signaling is responsible for modulating the phenotypic balance between the epithelial and mesenchymal state for a variety of cell types. Interestingly, the EMT program, although different in function, shares many common architectural features with the HL-60 program, suggesting conserved but highly adaptable motifs. Lastly, we’ll discuss on-going work in the lab exploring the behavior of both whole cell and cell free metabolic networks, and present strategies for building genome scale cell free metabolic models.

Bio:  Jeffrey Varner holds a Ph.D degree in Chemical Engineering from Purdue University where he explored modeling and analysis of metabolic networks in the lab of Prof. Ramkrishna. After a postdoc in the Department of Biology at the ETH-Zurich under the direction of Jay Bailey and a research position at Genencor International Inc, Palo Alto, CA, Prof. Varner joined the faculty of the Chemical and Biomolecular Engineering department at Cornell University as an Assistant Professor in 2006. In the fall of 2011, Prof. Varner was promoted to Associate Professor with tenure. The Varnerlab is interested in modeling and analysis of signal transduction and metabolic networks.