Home > Research > Publications & Outputs > Protocol for hybrid flux balance, statistical, ...

Links

Text available via DOI:

View graph of relations

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

Standard

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

In: STAR Protocols, Vol. 2, No. 4, 100837, 17.12.2021, p. 1-57.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{e107be6ac8374b38b28cef71fcf132f4,
title = "Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002",
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.",
keywords = "Bioinformatics, Metabolism, Microbiology, Systems biology, Computer sciences",
author = "Supreeta Vijayakumar and Claudio Angione",
year = "2021",
month = dec,
day = "17",
doi = "10.1016/j.xpro.2021.100837",
language = "English",
volume = "2",
pages = "1--57",
journal = "STAR Protocols",
number = "4",

}

RIS

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 -