Vernon, Ian. R. and Goldstein, Michael (2010) 'A Bayes Linear approach to systems biology.', Project Report. MUCM, Sheffield.
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.
|Item Type:||Monograph (Project Report)|
|Additional Information:||This is a Technical Report in the Managing Uncertainty for Complex Models (MUCM: funded by a Basic Technology Grant) Technical Report Series.|
|Keywords:||Emulation, Computer Models, Stochastic Models, Systems Biology, Rate Parameter Inference|
|Full text:||(VoR) Version of Record|
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|Publisher Web site:||http://www.mucm.ac.uk/Pages/Dissemination/TechnicalReports.html|
|Record Created:||19 Oct 2017 09:58|
|Last Modified:||20 Oct 2017 12:40|
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