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dc.contributor.authorMinas, Giorgos
dc.contributor.authorRand, David A.
dc.identifier.citationMinas , G & Rand , D A 2019 , ' Parameter sensitivity analysis for biochemical reaction networks ' , Mathematical Biosciences and Engineering , vol. 16 , no. 5 , pp. 3965-3987 .
dc.identifier.otherORCID: /0000-0001-7953-706X/work/57331041
dc.descriptionFunding: This research was funded by the BBSRC Grant BB/K003097/1 (Systems Biology Analysis of Biological Timers and Inflammation) and the EPSRC Grant EP/P019811/1 (Mathematical Foundations of Information and Decisions in Dynamic Cell Signalling). DAR was also supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 305564.en
dc.description.abstractBiochemical reaction networks describe the chemical interactions occurring between molecular populations inside the living cell. These networks can be very noisy and complex and they often involve many variables and even more parameters. Parameter sensitivity analysis that studies the effects of parameter changes to the behaviour of biochemical networks can be a powerful tool in unravelling their key parameters and interactions. It can also be very useful in designing experiments that study these networks and in addressing parameter identifiability issues. This article develops a general methodology for analysing the sensitivity of probability distributions of stochastic processes describing the time-evolution of biochemical reaction networks to changes in their parameter values. We derive the coefficients that efficiently summarise the sensitivity of the probability distribution of the network to each parameter and discuss their properties. The methodology is scalable to large and complex stochastic reaction networks involving many parameters and can be applied to oscillatory networks. We use the two-dimensional Brusselator system as an illustrative example and apply our approach to the analysis of the Drosophila circadian clock. We investigate the impact of using stochastic over deterministic models and provide an analysis that can support key decisions for experimental design, such as the choice of variables and time-points to be observed.
dc.relation.ispartofMathematical Biosciences and Engineeringen
dc.subjectParameter sensitivity analysisen
dc.subjectReaction networksen
dc.subjectMolecular Biologyen
dc.subjectQA Mathematicsen
dc.subjectQD Chemistryen
dc.subjectQH301 Biologyen
dc.titleParameter sensitivity analysis for biochemical reaction networksen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.description.statusPeer revieweden

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