(252a) Optrecon – an Optimization-Based Framework for Automated Genome Scale Model Reconstruction upon Resolving Thermodynamically Infeasible Cycles

Genome-Scale Models (GSMs) are important tools for modeling cell metabolism. Current methods of GSM reconstruction require extensive manual curation, which is time consuming and laborious to produce a quality model, and often ignore problems such as Thermodynamically Infeasible Cycles (TICs). These TICs can cause unbounded fluxes, negatively influencing biological consistency and insights derivable from the GSMs. To overcome these challenges, we introduce OptRecon, a freely available multi-step automated optimization-based approach for model refinement and TIC resolution. Using our previously developed OptFill algorithm [1], OptRecon splits constructed models into minimal and secondary networks, reincorporating secondary reactions into the minimal model to create a TIC-free reconstruction by carefully steering specific directionalities. We demonstrate OptRecon’s ability to automatically refine predictive models without TICs using automatically generated prokaryotic and eukaryotic models and checked them using growth-no-growth and synthetic lethality analyses to show improved model consistency. Specifically, we tested GSMs for Escherichia coli, Bacillus subtilis, and Streptomyces coelicolor bacterias, and the yeast Yarrowia lipolytica. Results indicate the model predictions match closely with in vivo findings as evidenced by increased true-negative synthetic gene knockouts compared with the GSMs pre-OptRecon. OptRecon has also been successfully implemented in one of our recent works for developing a Human Alveolar Macrophage GSM [2], which shows its utility in large-scale eucaryotic GSM development. In future, OptRecon could be incorporated into tools such as the COBRA Toolbox as an easy and simple way to develop and refine GSMs of varied complexity (i.e., prokaryotic and eukaryotic). Thus, OptRecon’s automated approach provides researchers with a valuable method to develop, expand or refine GSMs while effectively addressing the challenges posed by TICs.

References:

[1] W. Schroeder & R. Saha, "OptFill: A Tool for Infeasible Cycle-Free Gapfilling of Stoichiometric Metabolic Models," iScience, vol. 23, iss. 1, p. 100783, Jan 2020, doi: 10.1016/j.isci.2019.100783

[2] S. Malla, K.A. Sajeevan, B. Acharya, R. Chowdhury, R. Saha, "Dissecting Metabolic Landscape of Alveolar Macrophage,” pre-print, Sep 2023, doi: 10.1101/2023.09.08.556783v1