gms | German Medical Science

The problem of reverse engineering intracellular networks from experimental data often results in a highly nonlinear and underdetermined optimization problem. Data are usually noisy and sparse, and the systems are intrinsically stochastic. Therefore, a stochastic modeling framework and statistical approaches for network inference are appropriate in this setting, since they naturally take uncertainties and measurement errors into account.

In my talk I compare likelihood functions of different time-discrete stochastic models which have been suggested to capture stochastic effects in biological network models. I propose to classify those models into three groups, according to the interpretation of the origin of stochasticity. General expressions for likelihoods are developed, and a comparison of those across the groups is provided. This method also suggests a way to separate noise in biological systems, which is illustrated on a small sample network. Here, a challenge is the investigation of marginal likelihoods, which is computationally expensive and requires efficient sampling methods.