摘要：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 ofEscherichia 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.Graphical AbstractDisplay OmittedHighlights•This work presents an alternative to state-of-the-art methods for gapfilling•Unlike current methods, this method is holistic and infeasible cycle free•This method is applied to three tests and one published model•This method might also be used to address infeasible cyclingMetabolic Engineering; Bioinformatics; Systems Biology; Metabolic Flux Analysis
