Singh Vineeta, Haque Shafiul, Niwas Ram, Srivastava Akansha, Pasupuleti Mukesh, Tripathi C K M
Microbiology Division, Council of Scientific and Industrial Research - Central Drug Research InstituteLucknow, India; Department of Biotechnology, Institute of Engineering and TechnologyLucknow, India.
Department of Biosciences, Jamia Millia Islamia (A Central University)New Delhi, India; Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan UniversityJazan, Saudi Arabia.
Front Microbiol. 2017 Jan 6;7:2087. doi: 10.3389/fmicb.2016.02087. eCollection 2016.
Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical "one-factor-at-a-time" to modern statistical and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.
需要优化生产培养基以使代谢产物产量最大化。这可以通过使用从经典的“一次一个因素”到现代统计和数学技术等广泛的技术来实现，即人工神经网络（ANN）、遗传算法（GA）等。每种技术都有其自身的优点和缺点，尽管存在缺点，但一些技术仍被应用以获得最佳结果。组合使用各种优化技术也能产生理想的结果。本文试图综述目前在代谢产物生产发酵过程中应用的培养基优化技术。对各种传统以及现代优化技术的优缺点进行了比较分析，并在本综述中给出了发酵培养基设计的合理选择依据。总体而言，本综述将为在代谢产物生产发酵过程中选择合适的培养基设计优化技术提供理论依据。
