Combining metabolic modelling with machine learning accurately predicts yeast growth rate

Culley, Christopher and Vijayakumar, Supreeta and Zampieri, Guido and Angione, Claudio (2019) Combining metabolic modelling with machine learning accurately predicts yeast growth rate. In: UNSPECIFIED.

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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.

Item Type:
Contribution to Conference (Other)
ID Code:
162348
Deposited By:
Deposited On:
28 Oct 2022 15:20
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 14:51