Sensitivity analysis of stochastic models of bistable biochemical reactions

Authors:
Andrea Degasperi;Stephen Gilmore
Affiliations:
Department of Computing Science, University of Glasgow;Laboratory for Foundations of Computer Science, University of Edinburgh
Venue:
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Year:
2008

Citing 2
Cited 6

Sensitivity analysis of model output: variance-based methods make the difference

Proceedings of the 29th conference on Winter simulation
Accuracy limitations and the measurement of errors in the stochastic simulation of chemically reacting systems

Journal of Computational Physics

A Ticking Clock: Performance Analysis of a Circadian Rhythm with Stochastic Process Algebra

EPEW '08 Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering
Modelling Biological Clocks with Bio-PEPA: Stochasticity and Robustness for the Neurospora crassa Circadian Network

CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
Design and development of software tools for Bio-PEPA

Winter Simulation Conference
Grid computing for sensitivity analysis of stochastic biological models

PaCT'11 Proceedings of the 11th international conference on Parallel computing technologies
Modelling and analysis of the NF-κB pathway in bio-PEPA

Transactions on Computational Systems Biology XII
Exploring parameter space of stochastic biochemical systems using quantitative model checking

CAV'13 Proceedings of the 25th international conference on Computer Aided Verification

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sensitivity Analysis (SA) provides techniques which can be used to identify the parameters which have the greatest influence on the results obtained from a model. Classical SA methods apply to deterministic simulations of ODE models. We extend these to stochastic simulations and consider the analysis of models with bifurcation points and bistable behaviour. We consider local, global and screening SA methods applied to multiple runs of Gillespie's Stochastic Simulation Algorithm (SSA). We present an example of stochastic sensitivity analysis of a real pathway, the MAPK signalling pathway.