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

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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.
Original languageEnglish
Publication statusPublished - 11 Aug 2017
EventIWBDA 2017: 9th International Workshop on Bio-Design Automation - Pittsburgh, United States
Duration: 8 Aug 201711 Aug 2017

Conference

ConferenceIWBDA 2017: 9th International Workshop on Bio-Design Automation
Abbreviated titleIWBDA 2017
CountryUnited States
CityPittsburgh
Period8/08/1711/08/17

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    Vijayakumar, S., & Angione, C. (2017). Poly-omic statistical methods describe cyanobacterial metabolic adaptation to fluctuating environments. Paper presented at IWBDA 2017: 9th International Workshop on Bio-Design Automation, Pittsburgh, United States.