Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

中文翻译：

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简单的监管架构允许学习不断变化的环境的统计结构


细菌生活在不断波动和变化的环境中。利用这种波动的任何可预测性都可以提高适应性。在更长的时间尺度上，细菌可以通过进化“学习”这些波动的结构。然而，在较短的时间尺度上，推断环境的统计数据并根据这些信息采取行动将需要通过生理机制来完成。在这里，我们使用新陈代谢模型来表明，常见调节基序（终产物抑制）的简单概括足以学习环境统计结构的连续值特征并将这些信息转化为预测行为；此外，它可以近乎最佳地完成这些任务。我们讨论了可能实例化我们所描述的机制的遗传回路，包括类似于二元信号传导结构的一种，并认为细菌很容易获得这种预测行为所需的关键成分。