A New Model for Kinetic Parameter Estimation in Biochemical Reactions.

We present a novel method for estimating rate coefficients from noisy observations of concentration levels at discrete time points. This is traditionally done by computing the least-squares estimator. However, estimation of the error function generally requires solving the reaction rate equations, which can become computationally unfeasible. Here we present an alternative approach based on a probabilistic, generative model of the variations in reactant concentration. Our method returns 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, and it allows a number of further extensions.