School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
School of Automation, Central South University, Changsha 410083, China.
Bioinformatics. 2020 Jun 1;36(11):3482-3492. doi: 10.1093/bioinformatics/btaa163.
Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run.
Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article).
Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA.
math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk.
Supplementary data are available at Bioinformatics online.
基于通量平衡分析（FBA）的双层优化在重新设计生物化学过度生产的代谢网络方面取得了巨大成功。迄今为止，已经开发了许多计算方法来解决由此产生的双层优化问题。然而，由于优化原理存在偏差、与代谢网络规模的可扩展性差、潜在的数值问题或在单次运行中设计解决方案数量较少，大多数方法的应用受到限制。
在这里，我们采用了一种无生长最优性假设的网络阻断模型，这是双层优化的一个特例，用于计算菌株设计，并开发了一种混合 Benders 算法（HBA）来处理模型中的复杂二进制变量，从而在不产生数值问题的情况下实现高效率搜索最佳设计策略。更重要的是，HBA 可以在搜索过程中列出满足用户生产要求的解决方案，使得在小的运行时开销（通常约为 1 小时，例如本文中研究的）下获得许多设计策略成为可能。
在 MATALAB Cobratoolbox 中实现的源代码可在 https://github.com/chang88ye/NIHBA 上免费获得。
math4neu@gmail.com 或 natalio.krasnogor@ncl.ac.uk。
补充数据可在“Bioinformatics”在线获取。
