Model Calibration of a Protein-Signaling Network

• Activity Number: 8 - Computational Methods and Bayesian Inference for Networks
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Council of Chapters
• Author(s): Derek Bingham* and David Alexander Campbell and David C. Clarke and Mike Grosskopf and Luyao Lin and Biljana Jonoska Stojkova
• Companies: Simon Fraser University and Simon Fraser University and Simon Fraser University and Simon Fraser University and Simon Fraser University and University of British Columbia
• Keywords: Bayesian model calibration; Computer model; Experimental design; Sampling

Abstract

Protein signaling network models are often used for investigating complex biochemical pathways. In recent years, computational models for such networks have become quite popular for understanding different processes. Of course, using experimental data helps constrain such networks and provide further insight into the physical system. In this talk, an approach for calibrating a protein-signaling network model is presented. The data structure of interest is a complex network with non-linear transfer functions between nodes. The data include observations under different experimental stimuli, but with incomplete sampling of network nodes. We cast this application as a computer model calibration problem and develop a new sequential MCMC approach for exploring the network structure and quantifying uncertainty.