technical report A noise-robust method for parameter inference in biochemical network models: an NF-kappaB case study


We present here a new method for estimating rate coefficients from noisy observations of concentration levels at discrete time points. The inference procedure is based on a probabilistic model of the variations in reactant concentration and it estimates the rate coefficients, the level of noise and an error range on the estimates of rate constants. Its probabilistic formulation is key to a principled handling of the noise inherent in biological data. We apply the inference model implemented by KInfer on real experimental data for deducing the "binding affinity" of the enzyme (Inhibitor kappa B kinase) to its substrate (inhibitor kappa B alpha), an integral part of the transduction of signals in the NF-kappa B signalling pathway.

Paper Details


P. Lecca,  A. Palmisano,  C. Priami,  A. Ihekwaba