BACKGROUND In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr.

中文翻译：

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MOMO - 多目标代谢混合整数优化：在酵母菌株工程中的应用。

背景在本文中，我们探讨了模型中同时涉及连续和整数决策变量时代谢工程领域的多目标优化的概念。特别是，我们提出了一种多目标模型，可用于建议同时最大化和/或最小化多个功能的反应删除。这些应用尤其可以包括生物产品和生物质的同时最大化，或生物产品的最大化同时最小化给定副产品的形成，这是微生物代谢工程中的两个常见要求。结果采用广泛使用的细胞工厂酿酒酵母生产乙醇作为案例研究，以证明所提出的方法在识别遗传操作方面的有用性，从而提高这种经济上高度相关的生物产品的生产率和产量。我们进行了体内验证，我们可以证明，与野生型菌株相比，一些预测的缺失表现出乙醇水平增加。结论 我们开发的多目标编程框架称为 MOMO，是开源的并使用 POLYSCIP（可在 http://polyscip.zib.de/ 获取）。作为底层多目标求解器。MOMO 的网址为 http://momo-sysbio.gforge.inria.fr。