Dynamic solution space division-based methods for calculating reaction deletion strategies for constraint-based metabolic networks for substance production: DynCubeProd

• Item type: SIG Technical Reports(1)
• 公開日: 2021-09-23
• タイトル: Dynamic solution space division-based methods for calculating reaction deletion strategies for constraint-based metabolic networks for substance production: DynCubeProd
• タイトル / 言語: en
• タイトル: Dynamic solution space division-based methods for calculating reaction deletion strategies for constraint-based metabolic networks for substance production: DynCubeProd
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者名: Yier, Ma; Takeyuki, Tamura
• 著者名(英): Yier, Ma; Takeyuki, Tamura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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 Escherichia coli. 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. This study was published in https://doi.org/10.3389/fbinf.2021.716112 [4].
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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 Escherichia coli. 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. This study was published in https://doi.org/10.3389/fbinf.2021.716112 [4].
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2021-BIO-67, 号 2, p. 1-6, 発行日 2021-09-23
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会