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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways
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An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

机译：代谢途径中目标过量生产的计算机模拟鉴定的优化规则

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摘要

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.
机译：在先前工作的扩展中，我们介绍一种用于确定从底物到代谢网络目标产品的最佳路径的二阶优化方法，通过该路径目标的量最大。考虑并最小化了用于所述目的的目标函数以及某些线性约束。计算跨越表示化学代谢网络的化学计量矩阵的零空间的基本向量，并将满足约束的凸向量视为通量向量。考虑了一组其他限制条件，其中包含了与该途径中的酶相对应的加权系数。这些加权系数出现在目标函数中以使其最小。在最小化期间​​，估计和学习这些加权系数的值。这些值在最小化时代表最佳途径，描绘了最佳酶浓度，导致靶标的过量生产。各种网络上的结果证明了该方法在代谢工程领域的有用性。还与标准梯度下降法和极限路径分析技术进行了比较。与梯度下降法不同，本方法独立于学习参数，显示出改进的结果。

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