The increasing availability of gene expression data of different types of normal and cancer cells has created new opportunities for integrating these datasets into mathematical models able to make novel predictions. I will introduce a model, originally developed for the physics of spin glasses, that has the merit of capturing the multi-stable nonlinear dynamics in complex cell signaling networks. The model uses large-scale biological data, specifically genome wide RNA-sequencing data on pooled cell samples and single cells, and predicts specific combinations of transcription factors or receptor ligands that could induce a specific cellular response. I will discuss how stochastic optimization and iterative in vitro-in silico methods can be used to optimize combinations of drugs targeting these genes, and lead to personalized therapy solutions.
Carlo Piermarocchi is a professor of Physics at Michigan State University. He obtained his Ph. D. in theoretical physics from the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland in 1998. He joined Michigan State University in 2002, after a postdoctoral period at UC San Diego. His research interests are related to the theory of control of physical and biological systems, and he has worked on the theory of quantum information, and more recently on the control of cellular signaling networks, which are also systems designed to process information. Piermarocchi has more than 80 scientific publications, received support from several NIH, NSF, and DOE grants and in 2005 won the Donald D. Harrington Faculty Fellowship at the University of Texas, Austin. He recently co-founded a company that offers consulting services to pharmacological companies.
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