A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Proceedings of the National Academy of Sciences of the United States of America
Christopher CulleyClaudio Angione

Abstract

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an addit...Continue Reading

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Citations

Nov 23, 2020·Metabolic Engineering·Christopher E LawsonHector Garcia Martin
Jul 26, 2021·Trends in Biotechnology·Hongzhong LuJens Nielsen
Sep 3, 2021·Computational and Structural Biotechnology Journal·Ankur SahuNadine Töpfer

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