Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of . Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.
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http://dx.doi.org/10.3389/fbinf.2021.716112 | DOI Listing |
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Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China.
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College of Science, Australia National University, Canberra, ACT 2600, Australia.
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January 2025
Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.
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Center for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, China.
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School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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