In flux balance analysis, where flux distribution within a cell metabolic network is estimated by optimizing an objective function, there commonly exist multiple optimal flux distributions. Although finding all optimal solutions is possible, their interpretation is a challenge. A new four-phase algorithm (LAMOS) is therefore proposed in this work to efficiently enumerate all of these solutions based on iterative substitution of a current non-basic variable with a basic variable. These basic and non-basic variables are called key reaction pairs that their successive activity or inactivity causes alternate optimal solutions. LAMOS was implemented on E. coli metabolic models and the results proved it as a simple and fast method capable of finding the key reactions as well as reactions participating in the futile cycles. Key reactions were 1–3% of all reactions for the large-scale models and these reactions were identified using only 1% of optimal solutions.

在通量平衡分析中，通过优化目标函数估算细胞代谢网络内的通量分布，通常存在多个最佳通量分布。尽管找到所有最佳解决方案都是可能的，但是它们的解释却是一个挑战。因此，在这项工作中提出了一种新的四阶段算法（LAMOS），以基于当前非基本变量与基本变量的迭代替换有效地枚举所有这些解决方案。这些基本和非基本变量称为关键反应对，它们的连续活动或不活动导致替代的最优解。LAMOS在大肠杆菌上实施代谢模型及其结果证明，它是一种简单，快速的方法，能够发现关键反应以及参与无效循环的反应。对于大型模型，关键反应占所有反应的1-3％，而这些反应仅使用最佳解决方案的1％进行识别。