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A Bayes Linear Approach to Systems Biology

Vernon, Ian. R.; Goldstein, Michael

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Michael Goldstein


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.


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
Publisher URL
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


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