Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments

Lancaster Environment Centre

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper

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Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments. / Vijayakumar, Supreeta; Angione, Claudio.
2017. Paper presented at EventIWBDA 2017, Pittsburgh, Pennsylvania, United States.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper

Bibtex

@conference{cec52bcc8e824fd69aaf484fb26c3b71,

title = "Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments",

abstract = "In this work, a genome-scale metabolic model of Synechococcus sp. PCC 7002 which utilizes flux balance analysis across multiple layers is analyzed to observe flux response between 23 growth conditions. This is achieved by setting reactions involved in biomass accumulation and energy production as objectives for bi-level linear optimization, thus serving to improve the characterization of mechanisms underlying these processes in photoautotrophic microalgae. Additionally, the incorporation of statistical techniques such as k-means clustering and principal component analysis (PCA) contribute to reducing dimensionality and inferring latent patterns.",

author = "Supreeta Vijayakumar and Claudio Angione",

year = "2017",

month = aug,

day = "11",

language = "English",

note = "EventIWBDA 2017 : 9th International Workshop on Bio-Design Automation ; Conference date: 08-08-2017 Through 11-08-2017",

url = "https://www.aconf.org/conf_116750.html",

}

RIS

TY - CONF

T1 - Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments

AU - Vijayakumar, Supreeta

AU - Angione, Claudio

PY - 2017/8/11

Y1 - 2017/8/11

N2 - In this work, a genome-scale metabolic model of Synechococcus sp. PCC 7002 which utilizes flux balance analysis across multiple layers is analyzed to observe flux response between 23 growth conditions. This is achieved by setting reactions involved in biomass accumulation and energy production as objectives for bi-level linear optimization, thus serving to improve the characterization of mechanisms underlying these processes in photoautotrophic microalgae. Additionally, the incorporation of statistical techniques such as k-means clustering and principal component analysis (PCA) contribute to reducing dimensionality and inferring latent patterns.

AB - In this work, a genome-scale metabolic model of Synechococcus sp. PCC 7002 which utilizes flux balance analysis across multiple layers is analyzed to observe flux response between 23 growth conditions. This is achieved by setting reactions involved in biomass accumulation and energy production as objectives for bi-level linear optimization, thus serving to improve the characterization of mechanisms underlying these processes in photoautotrophic microalgae. Additionally, the incorporation of statistical techniques such as k-means clustering and principal component analysis (PCA) contribute to reducing dimensionality and inferring latent patterns.

UR - https://research.tees.ac.uk/ws/portalfiles/portal/5961714/Accepted_manuscript.pdf

M3 - Conference paper

T2 - EventIWBDA 2017

Y2 - 8 August 2017 through 11 August 2017

ER -

Research