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Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
Article number100837
<mark>Journal publication date</mark>17/12/2021
<mark>Journal</mark>STAR Protocols
Issue number4
Volume2
Number of pages57
Pages (from-to)1-57
Publication StatusPublished
Early online date29/09/21
<mark>Original language</mark>English

Abstract

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data.