Fondi Marco, Fani Renato
Dep. of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Florence, Italy.
Dep. of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Florence, Italy.
Mar Genomics. 2017 Aug;34:1-10. doi: 10.1016/j.margen.2017.06.003. Epub 2017 Jun 23.
Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.
基于约束的代谢建模（CBMM）包括使用计算方法和工具来进行全基因组规模的模拟，并在整个细胞水平上预测代谢特征。这种方法在微生物学领域正迅速发展，因为它将可靠的预测能力与概念和技术上简单的框架相结合。在CBMM可能的成果中，能够：i）指导代谢工程实验的重点规划；ii）提供对（单个或群落水平）微生物代谢回路的系统层面理解，这也是当今海洋微生物学的主要目标。在这项工作中，我们简要介绍了CBMM背后的理论公式，然后回顾了海洋微生物和群落CBMM的最新且有效的案例研究。此外，还讨论了在海洋微生物学/生物技术背景下使用此类方法时出现的挑战和可能性。由于CBMM的潜在应用范围非常广泛，本综述中提出的主题涵盖了大量领域，如生态学、生物技术和进化。
