School of Mathematics

Ramon Grima

Ramon Grima abstract

Ramon GrimaSynthSys, School of Biological Sciences, UoE

Title: Inferring enzyme kinetic parameters from noisy time series data


Enzymes are ubiquitous molecular machines which speed up reactions inside cells. A considerable effort has been invested in biochemistry to develop methods to infer the kinetic parameters regulating enzyme catalysis. The standard approach consists of measuring the rate of product formation for short times for several different substrate concentrations and then using this data together with the Michaelis-Menten equation to infer the rate parameters. This approach has been shown to be inaccurate - firstly because of the difficulty of experimentally measuring the initial rates of product formation and secondly because the vast majority of the available temporal information is discarded. Here I describe a novel method which allows one to do parameter inference from one single time trace of data collected at one substrate concentration. The method's accuracy is verified by means of synthetic time series data generated using stochastic simulations. The method is shown to lead to accurate parameter estimates using noisy data obtained from systems with any number of enzyme molecules including the case of a single enzyme molecule.