In metabolic engineering, growth-coupled production is a key idea, in which microbial cells produce target metabolites while maintaining growth rates. For growth-coupled production, it is often required to modify the original constraint-based metabolic networks that do not produce the target metabolites. In this research, the mathematical definitions for various problems of discovering modification strategies that result in growth-coupled production are provided and proved to be NP-hard or NP-complete. And then a ratio-based generalized approach that is capable of fulfilling a variety of modification criteria is proposed. Additionally, this algorithm also processes the results with a two-step way to remove the redundant genes in the strategy. The computational experiments were conducted at both the gene deletion and addition levels. We found that it improved the success ratio for modification strategies and efficiently presented a reduced addition strategies compared with the original strategies.
Bioinformatics, Metabolic engineering
| 氏名 | コース | 研究室 | 役職/学年 |
|---|---|---|---|
| MA YIER | 知能情報学コース | 数理生物情報 | 博士2回生 |
| 田村 武幸 | 知能情報学コース | 数理生物情報 | 准教授 |