The aim of the talk is to present a novel, integrated theoretical framework for the analysis of stochastic biochemical kinetics models. The framework includes efficient methods for statistical parameter estimation from experimental data, as well as allows for fast computation of the Fisher Information. Therefore, it provides the tool to study parameter identifiability, sensitivity and robustness and leads to novel conclusions about functionality and statistical properties of stochastic chemical kinetics systems.
The linear noise approximation is used to derive model equations and a likelihood function which leads to efficient computational algorithms. 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 used to infer model parameters, 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.
A number of experimental and theoretical examples will be presented to show how the techniques can be applied to analyse models and extract information from the noise structure inherent to experimental data. Examples will include inference of parameters of gene expression using a fluorescent reporter gene data, a Bayesian hierarchical model for estimation of transcription rates and a study of the p53 protein dynamical system. For these models novel and surprising insights into the effects of stochasticity in biochemical systems are obtained by the analysis of the Fisher Information Matrices.