Evolutionary programming as a platform for in silico metabolic engineering.
Evolutionary programming as a platform for in silico metabolic engineering.
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DOI：
10.1186/1471-2105-6-308
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发表时间：
2005-12-23
影响因子：
3
通讯作者：
Nielsen, J
中科院分区：
文献类型：
--
作者：
Patil, KR;Rocha, I;Förster, J;Nielsen, J
通过基因工程，有可能引入靶向的遗传变化，并由此工程化微生物细胞的代谢，目的是获得期望的表型。然而，由于代谢网络在结构和调控方面的复杂性，通常难以预测遗传修饰对所得表型的影响。最近，基因组规模的代谢模型已被编译为几种不同的微生物，结构和化学计量的复杂性是固有的占。正在开发新的算法，通过使用基因组规模的代谢模型，使基因敲除策略，以获得改善的表型的鉴定。然而，寻找最佳基因删除策略的问题是组合的，因此计算时间随着问题的大小呈指数级增加，因此开发新的更快的算法是有趣的。在这项研究中，我们报告了一种基于进化规划的方法，以快速确定基因删除策略优化所需的表型目标函数。我们说明了工业发酵中的两个重要的设计参数，一个线性和其他非线性，通过使用酵母酿酒酵母的基因组规模模型所提出的方法。潜在的代谢工程目标，以提高生产琥珀酸，甘油和香草醛的确定和潜在的通量变化的预测突变体进行了讨论。我们表明，进化规划，使解决大基因敲除问题在相对较短的计算时间。该算法还允许优化非线性目标函数或纳入非线性约束，并提供了一个家庭的接近最优的解决方案。所确定的代谢工程策略表明，非直观的遗传修饰跨越几个不同的途径，可能是必要的解决具有挑战性的代谢工程问题。
Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.
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