Gilman A, Ross J
Department of Chemistry, Stanford University, California 94305, USA.
Biophys J. 1995 Oct;69(4):1321-33. doi: 10.1016/S0006-3495(95)79999-4.
A genetic algorithm (GA) is used to optimize parameters for allosteric regulation of enzymes in a model of a metabolic futile cycle, in which two metabolites are interconverted by a pair of irreversible enzymatic reactions. The cycle is regulated by end products of the surrounding pathway. The optimization criterion for the GA is the proper direction of chemical flux in the regulated cycle toward one or the other end product in response to a simple, time-dependent model of biochemical "need" based on externally imposed variation of the end product concentrations. An energetic cost, to be held to a minimum, is also imposed on the operation of the cycle. The best-performing individuals selected by the GA are found to switch rapidly the direction of net flux according to need. In different "environments" (specific time courses of end product concentrations), the GA produces better- or poorer-performing individuals. In some cases "generalists" and "specialists" are produced. The present approach provides, purely as a consequence of formally specifying the task of flux direction, the new result of numerical confirmation, in a simple model, of the intuition that negative feedback and reciprocal regulation are important for good flux direction in arbitrary environments, and gives rise to a diversity of structures, suggestive of the results of biological evolution.
在一个代谢无效循环模型中，使用遗传算法（GA）来优化酶变构调节的参数。在该模型中，两种代谢物通过一对不可逆酶促反应相互转化。该循环由周围途径的终产物调节。GA的优化标准是，在基于终产物浓度的外部施加变化的简单、时间依赖性生化“需求”模型的响应下，调节循环中的化学通量朝着一个或另一个终产物的正确方向。还对循环的运行施加了一个要保持在最低限度的能量成本。发现由GA选择的表现最佳的个体能够根据需求快速切换净通量的方向。在不同的“环境”（终产物浓度的特定时间进程）中，GA产生表现较好或较差的个体。在某些情况下会产生“通才”和“专才”。本方法纯粹由于形式上指定了通量方向的任务，在一个简单模型中得出了数值确认的新结果，即负反馈和相互调节对于在任意环境中实现良好的通量方向很重要这一直觉，并产生了多种结构，这暗示了生物进化的结果。
