Instituto Cajal, Consejo Superior de Investigaciones Científicas, Avenida Dr. Arce 37, 28002 Madrid, Spain.
Proc Natl Acad Sci U S A. 2009 Dec 1;106(48):20544-9. doi: 10.1073/pnas.0905336106. Epub 2009 Nov 16.
Optimization theory has been used to analyze evolutionary adaptation. This theory has explained many features of biological systems, from the genetic code to animal behavior. However, these systems show important deviations from optimality. Typically, these deviations are large in some particular components of the system, whereas others seem to be almost optimal. Deviations from optimality may be due to many factors in evolution, including stochastic effects and finite time, that may not allow the system to reach the ideal optimum. However, we still expect the system to have a higher probability of reaching a state with a higher value of the proposed indirect measure of fitness. In systems of many components, this implies that the largest deviations are expected in those components with less impact on the indirect measure of fitness. Here, we show that this simple probabilistic rule explains deviations from optimality in two very different biological systems. In Caenorhabditis elegans, this rule successfully explains the experimental deviations of the position of neurons from the configuration of minimal wiring cost. In Escherichia coli, the probabilistic rule correctly obtains the structure of the experimental deviations of metabolic fluxes from the configuration that maximizes biomass production. This approach is proposed to explain or predict more data than optimization theory while using no extra parameters. Thus, it can also be used to find and refine hypotheses about which constraints have shaped biological structures in evolution.
优化理论被用于分析进化适应。该理论解释了许多生物系统的特征，从遗传密码到动物行为。然而，这些系统显示出与最优性的重要偏差。通常，这些偏差在系统的某些特定组件中较大，而其他组件似乎几乎是最优的。进化中的许多因素可能导致偏离最优性，包括随机效应和有限的时间，这可能不允许系统达到理想的最优状态。然而，我们仍然期望系统有更高的概率达到具有更高拟议适应间接测量值的状态。在多组件系统中，这意味着在对适应间接测量值影响较小的组件中，预期会出现最大的偏差。在这里，我们表明，这个简单的概率规则可以解释两个非常不同的生物系统中的最优性偏差。在秀丽隐杆线虫中，这个规则成功地解释了神经元位置偏离最小布线成本配置的实验偏差。在大肠杆菌中，概率规则正确地获得了代谢通量的实验偏差结构，该结构最大化了生物量的产生。该方法被提出用于解释或预测比优化理论更多的数据，而不使用额外的参数。因此，它也可以用于寻找和完善关于哪些约束在进化中塑造了生物结构的假设。
