A Bayes Linear Approach to Systems Biology

Vernon, Ian. R.; Goldstein, Michael

Abstract

As post-genomic biology becomes more predictive, the inference of rate parameters that feature in both genetic and biochemical networks becomes increasingly important. Here we present a novel methodology for inference of such parameters in the case of stochastic networks, based on concepts from the area of computer models combined with Bayes Linear variance learning methodology. We apply these techniques to a simple, analytically tractable Birth-Death pro- cess model, followed by a more complex stochastic Prokaryotic Auto-regulatory Gene Network.

Citation

Vernon, I. R., & Goldstein, M. (2010). A Bayes Linear Approach to Systems Biology. MUCM

• Report Type: Project Report
• Online Publication Date: Sep 21, 2010
• Publication Date: Sep 21, 2010
• Deposit Date: Mar 21, 2011
• Publicly Available Date: Oct 19, 2017
• Series Title: MUCM technical reports
• Keywords: Emulation, Computer Models, Stochastic Models, Systems Biology, Rate Parameter Inference
• Public URL: https://durham-repository.worktribe.com/output/1608304
• Publisher URL: http://www.mucm.ac.uk/Pages/Dissemination/TechnicalReports.html
• Additional Information: Additional Information: This is a Technical Report in the Managing Uncertainty for Complex Models (MUCM: funded by a Basic Technology Grant) Technical Report Series.; University Name: Sheffield University; Publisher: MUCM; Type: monograph; Subtype: project_report