Improving flux predictions by integrating data from multiple strains | Zendy

Matthew Long | Zendy; Jennifer L. Reed | Zendy

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Improving flux predictions by integrating data from multiple strains

Author(s) -

Matthew Long,

Jennifer L. Reed

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw706

Subject(s) - flux balance analysis , computer science , flux (metallurgy) , rss , gene knockout , data set , set (abstract data type) , metabolic flux analysis , data mining , strain (injury) , constraint (computer aided design) , biological system , algorithm , computational biology , mathematics , biology , gene , chemistry , genetics , artificial intelligence , biochemistry , metabolism , organic chemistry , anatomy , programming language , operating system , geometry

Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient to significantly improve metabolic flux predictions.

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