Combining metabolic modelling with machine learning accurately predicts yeast growth rate
Abstract
New metabolic engineering techniques hold great potential for a range of bio-industrial applications.However, their practical use is hindered by the huge number of possible modifications, especially in eukaryotic organisms.To address this challenge, we present a methodology combining genome-scale metabolic modelling and machine learning to precisely predict cellular phenotypes starting from gene expression readouts.Our methodology enables the identification of candidate genetic manipulations that maximise a desired output -potentially reducing the number of in vitro experiments otherwise required.We apply and validate this methodology to a screen of 1,143 Saccharomyces cerevisiae knockout strains.Within the proposed framework, we compare different combinations of feature selection and supervised machine/deep learning approaches to identify the most effective model.
Authors: Christopher Culley, Supreeta Vijayakumar, Guido Zampieri, Claudio Angione
Published in: TeesRep (Teesside University) (2019)