A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Supreeta Vijayakumar; Pattanathu K S M Rahman; Claudio Angione

doi:10.1016/j.isci.2020.101818

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

iScience. 2020 Nov 18;23(12):101818. doi: 10.1016/j.isci.2020.101818. eCollection 2020 Dec 18.

Authors

Supreeta Vijayakumar¹, Pattanathu K S M Rahman^{2

3}, Claudio Angione^{1

4

5}

Affiliations

¹ Department of Computer Science and Information Systems, Teesside University, Middlesbrough, North Yorkshire TS1 3BX, UK.
² Centre for Enzyme Innovation, Institute of Biological and Biomedical Sciences, School of Biological Sciences, University of Portsmouth, Portsmouth, Hampshire PO1 2UP, UK.
³ Tara Biologics, Woking, Surrey GU21 6BP, UK.
⁴ Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, UK.
⁵ Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK.

Abstract

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

Keywords: Artificial Intelligence; Bioengineering; In Silico Biology; Metabolic Engineering.