Combining metabolic modelling with machine learning accurately predicts yeast growth rate

Christopher Culley, Supreeta Vijayakumar, Guido Zampieri, Claudio Angione

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Publication statusAccepted/In press - Jun 2019
Event11th International Workshop on Bio-Design Automation
- Department of Computer Science and Technology, Cambridge, United Kingdom
Duration: 8 Jul 201910 Jul 2019


Conference11th International Workshop on Bio-Design Automation
Abbreviated titleIWBDA 2019
Country/TerritoryUnited Kingdom
Internet address


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