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

Vijayakumar, Supreeta and Angione, Claudio (2021) Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002. STAR Protocols, 2 (4). pp. 1-57.

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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.

Item Type:
Journal Article
Journal or Publication Title:
STAR Protocols
Subjects:
ID Code:
162405
Deposited By:
Deposited On:
18 Nov 2021 11:55
Refereed?:
Yes
Published?:
Published
Last Modified:
02 Dec 2021 09:09