Sensitivity, robustness and identifiability in stochastic chemical kinetics models

Details Sensitivity, robustness and identifiability in stochastic chemical kinetics models Sensitivity, robustness and identifiability in stochastic chemical kinetics models

2011-04-07

arxiv.org/abs/1104.1274v1

Statistics

Applications

Scientific paper

We present a novel and simple method to numerically calculate Fisher Information Matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function which leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher Information Matrix to solving a set of ordinary differential equations. {This is the first method to compute Fisher Information for stochastic chemical kinetics models without the need for Monte Carlo simulations.} This methodology is then used to study sensitivity, robustness and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.

Affiliated with

Also associated with

No associations

Rating

Sensitivity, robustness and identifiability in stochastic chemical kinetics models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sensitivity, robustness and identifiability in stochastic chemical kinetics models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sensitivity, robustness and identifiability in stochastic chemical kinetics models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-717846