title: In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.
abstract: Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.
authors: Department of Chemical Engineering and Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV ; Department of Microbiology, Immunology, & Cell Biology, West Virginia University, Morgantown, WV.
authors: David J
authors: DJ
authors: Klinke
authors: Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY.
authors: Marc R
authors: MR
authors: Birtwistle
keywords: JAK-STAT signaling pathways; Markov Chain Monte Carlo methods; inverse problems; quantitative and systems pharmacology