Combining metabolic modelling with machine learning accurately predicts yeast growth rate

Lancaster Environment Centre

Research output: Contribution to conference - Without ISBN/ISSN › Abstract › peer-review

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Combining metabolic modelling with machine learning accurately predicts yeast growth rate. / Culley, Christopher; Vijayakumar, Supreeta; Zampieri, Guido et al.
2019. 1-2.

Research output: Contribution to conference - Without ISBN/ISSN › Abstract › peer-review

Bibtex

@conference{ecf6e391df61430ebafe8e439cc75eaf,

title = "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.",

author = "Christopher Culley and Supreeta Vijayakumar and Guido Zampieri and Claudio Angione",

year = "2019",

month = jul,

day = "8",

language = "English",

pages = "1--2",

}

RIS

TY - CONF

T1 - Combining metabolic modelling with machine learning accurately predicts yeast growth rate

AU - Culley, Christopher

AU - Vijayakumar, Supreeta

AU - Zampieri, Guido

AU - Angione, Claudio

PY - 2019/7/8

Y1 - 2019/7/8

N2 - 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.

AB - 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.

UR - https://research.tees.ac.uk/ws/portalfiles/portal/7567220/extended_abstract.pdf

M3 - Abstract

SP - 1

EP - 2

ER -

Research