Metabolic network properties help assign weights to elementary modes to understand physiological flux distributions | Zendy

Qingzhao Wang | Zendy; Yudi Yang | Zendy; Hongwu Ma | Zendy; Xueming Zhao | Zendy

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Metabolic network properties help assign weights to elementary modes to understand physiological flux distributions

Author(s) -

Qingzhao Wang,

Yudi Yang,

Hongwu Ma,

Xueming Zhao

Publication year - 2007

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/btm074

Subject(s) - logarithm , computation , flux (metallurgy) , set (abstract data type) , metabolic network , computer science , function (biology) , power (physics) , distribution (mathematics) , algorithm , mathematical optimization , mathematics , data mining , physics , bioinformatics , biology , mathematical analysis , materials science , quantum mechanics , evolutionary biology , metallurgy , programming language

Elementary modes (EMs) analysis has been well established. The existing methodologies for assigning weights to EMs cannot be directly applied for large-scale metabolic networks, since the tremendous number of modes would make the computation a time-consuming or even an impossible mission. Therefore, developing more efficient methods to deal with large set of EMs is urgent.

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