(338a) Optfill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models | AIChE

(338a) Optfill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models


Saha, R. - Presenter, University of Nebraska-Lincoln
Schroeder, W., The Pennsylvania State University
Stoichiometric metabolic modeling, particularly Genome-Scale Models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases such as NCBI, UniProt, KEGG, ModelSeed and KBase; however, incomplete systems knowledge leaves gaps in any genome-scale reconstruction. Current tools for addressing gaps, such as GapFind, GapFill, GenDev, and other such tools, use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions. However, their major limitation is that they cannot avoid Thermodynamically Infeasible Cycles (TICs), invariably requiring lengthy manual curation. This is in part due to their approach, which addresses gaps on a per-metabolite basis. To address these limitations, this work introduces an optimization-based multi-step method named OptFill which performs TIC-avoiding, whole-model, and holistic gapfilling. OptFill, as with other methods, makes use of a database of biochemical functionalities to address metabolic gaps. In contrast to other methods, OptFill uses a three-step approach to maximize metabolites connected, minimize the number of reactions, and maximize the number of reversible reactions in each solution. In addition, OptFill can be easily adapted to automate inherent TICs identification in any GSM, aiding manual curation efforts. OptFill was first to three fictional prokaryotic “toy” models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle free gapfilling solutions. Following this, the formulation of OptFill was improved for greater solution speed and iteratively applied to the reconstruction of a genome-scale model of Exophiala dermatitidis, iEde2091 which has been used to study the cost of polyextremotolerance and the similarities of human and E. dermatitidis melanin synthesis. Overall, OptFill can address critical issues in automated development of high-quality GSMs.

W. L. Schroeder and R. Saha. “OptFill: a tool for infeasible cycle-free gapfilling of stoichiometric metabolic models”. iScience, vol. 23 no. 1, pp. 1-14, Jan. 24, 2020. Available: https://www.cell.com/iscience/fulltext/S2589-0042(19)30528-0 (doi: https://doi.org/10.1016/j.isci.2019.100783).