Abstract
SummaryStoichiometric 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; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions but cannot avoid thermodynamically infeasible cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill, which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle-free gapfilling solutions. In addition, OptFill can be adapted to automate inherent TICs identification in any GSM. Overall, OptFill can address critical issues in automated development of high-quality GSMs.
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