Department of Chemical Engineering, The University of Texas at Austin, 1 University Station, Austin, TX 78712, USA.
Biotechnol J. 2010 Jul;5(7):647-59. doi: 10.1002/biot.200900247.
The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.
高通量基因组规模生物信息学的出现导致了可用的细胞系统数据呈指数级增长。系统代谢工程试图使用基于高通量技术收集的数据的驱动方法来确定基因靶点，并在系统水平上优化表型特性。当前的系统代谢工程工具在预测和定义复杂表型方面存在局限性，如化学耐受性和其他全局多基因特性。代谢工程中最实用的基于系统的工具是计算机基因组规模代谢重建。该工具已广泛用于细胞生长建模和有益基因敲除的预测，我们在此探讨如何将这种方法扩展到新的生物体。本综述将重点介绍系统代谢工程方法的进展，包括从头开发和使用基因组规模代谢重建进行代谢工程应用。然后，我们将讨论这个新兴领域面临的挑战和前景，以实现基于模型的代谢工程。具体来说，我们认为当前最先进的系统代谢工程技术代表了提高产品产量的可行的第一步，但仍需结合组合技术或随机菌株诱变来实现最佳细胞系统。
