Network Inference From Time-Course Data

The network inference method presented in this chapter is inspired by the theory of time-lagged correlation inference, and uses a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations from noisy time-resolved experimental data.

We show the inference of a gemcitabine metabolic network as a case study. Time-lagged-correlation-based inference paired with a probabilistic model of parameter inference allows the identification of the microscopic pharmacokinetics and pharmacodynamics of gemcitabine with minimal a priori knowledge, and with good accuracy and sensitivity. Indeed, the inference procedure we describe here is completely unsupervised, as it takes as input only the time series of the concentrations of the parent drug and its metabolites.