Last modified: 2014-03-31

#### Abstract

Background: The behavior of small-volume biological systems is often influenced by stochastic effects. When estimating kinetic parameters of such systems, it can easily be infeasible to fit a stochastic model, due to the high computational complexity of the available methods.

Methods: To circumvent the computational complexity of stochastic models, one can use deterministic approximations to the moments of the probability densities of the measured observables. In this project, we will compare parameter estimation using different orders of system size expansions. For these deterministic schemes, the approximation error will be dependent on the volume of the system, which can result in a volume-dependence of the respective parameter estimates.

Conclusion: We find that the mean squared error in parameter estimates can be up to two magnitudes lower when using the system size expansion to approximate mean and variance of the observables of the system. To obtain an unbiased parameter estimate, it is hence crucial to select the appropriate deterministic moment approximation scheme.