[en] Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions.
Results:We present FASTGAPFILL, a computationally efficient,tractable extension to the COBRA toolbox that permits theidentification of candidate missing knowledge from a universal biochemical reaction database (e.g., KEGG) for a given (compart-mentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more
relevant solutions. We demonstrate the efficiency and scalability of fastGapFill on a range of metabolic reconstructions.
Centre de recherche :
Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group) Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group) Luxembourg Centre for Systems Biomedicine (LCSB): Systems Biochemistry (Fleming Group)
Disciplines :
Sciences du vivant: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
THIELE, Ines ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
