A simple regulatory architecture allows learning the statistical structure of a changing environment

• Published in: eLife Vol. 10
• Main Authors: Landmann, Stefan, Holmes, Caroline M, Tikhonov, Mikhail
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 07.09.2021; eLife Sciences Publications, Ltd
• Subjects: Bacteria; Bacterial Physiological Phenomena; Behavior; Computational and Systems Biology; Control theory; Evolution; fluctuating environment; Learning; metabolic regulation; Models, Theoretical; Organisms; Physics of Living Systems; Physiology; Random variables; Signal Transduction; Statistics; Systems Biology; computational biology; systems biology; fluctuating environment; learning; none; physics of living systems; metabolic regulation
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.67455

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.