title: BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods.
abstract: Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability.
  
  
  
  We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools.
  
  
  
  MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ).
  
  
  
  toyetunde@wustl.edu or muhan@wustl.edu .
  
  
  
  Supplementary data are available at Bioinformatics online.
authors: Department of Energy, Environmental and Chemical Engineering.
authors: Tolutola
authors: T
authors: Oyetunde
authors: Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.
authors: Muhan
authors: M
authors: Zhang
authors: Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.
authors: Yixin
authors: Y
authors: Chen
authors: Department of Energy, Environmental and Chemical Engineering.
authors: Yinjie
authors: Y
authors: Tang
authors: Department of Energy, Environmental and Chemical Engineering.
authors: Cynthia
authors: C
authors: Lo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D000069550:Machine Learning; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D012984:Software