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Optimization of biochemical systems through mathematical programming: Methods and applications

机译:通过数学程序优化生化系统:方法和应用

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In this work we present a general (mono and multiobjective) optimization framework for the technological improvement of biochemical systems. The starting point of the method is a mathematical model in ordinary differential equations (ODEs) of the investigated system, based on qualitative biological knowledge and quantitative experimental data. In the method we take advantage of the special structural features of a family of ODEs called power-law models to reduce the computational complexity of the optimization program. In this way, the genetic manipulation of a biochemical system to meet a certain biotechnological goal can be expressed as an optimization program with some desired properties such as linearity or convexity.rnThe general method of optimization is presented and discussed in its linear and geometric programming versions. We furthermore illustrate the use of the method by several real case studies. We conclude that the technological improvement of microorganisms can be afforded using the combination of mathematical modelling and optimization. The systematic nature of this approach facilitates the redesign of biochemical systems and makes this a predictive exercise rather than a trial-and-error procedure.
机译:在这项工作中,我们提出了一个用于生化系统技术改进的通用(单目标和多目标)优化框架。该方法的出发点是基于定性生物学知识和定量实验数据的被研究系统的常微分方程(ODE)中的数学模型。在该方法中,我们利用称为能量定律模型的ODE系列的特殊结构特征来减少优化程序的计算复杂性。这样,可以将满足某些生物技术目标的生化系统的遗传操作表达为具有某些所需特性(例如线性或凸性)的优化程序。rn在其线性和几何编程版本中介绍并讨论了一般的优化方法。 。我们还将通过一些实际案例来说明该方法的使用。我们得出的结论是,可以结合使用数学建模和优化来实现微生物的技术改进。这种方法的系统性促进了生化系统的重新设计,并使之成为一种预测性活动,而不是反复试验的程序。

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