Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002
AU - Vijayakumar, Supreeta
AU - Angione, Claudio
PY - 2021/12/17
Y1 - 2021/12/17
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - Metabolism
KW - Microbiology
KW - Systems biology
KW - Computer sciences
U2 - 10.1016/j.xpro.2021.100837
DO - 10.1016/j.xpro.2021.100837
M3 - Journal article
VL - 2
SP - 1
EP - 57
JO - STAR Protocols
JF - STAR Protocols
IS - 4
M1 - 100837
ER -