Operant conditioning of stochastic chemical reaction networks

Adapting one's behavior to environmental conditions and past experience is a key trait of living systems. In the biological world, there is evidence for adaptive behaviors such as learning even in naturally occurring, non-neural, single-celled organisms. In the bioengineered world, advances in synthetic cell engineering and biorobotics have created the possibility of implementing lifelike systems engineered from the bottom up. This will require the development of programmable control circuitry for such biomimetic systems that is capable of realizing such non-trivial and adaptive behavior, including modification of subsequent behavior in response to environmental feedback. To this end, we report the design of novel stochastic chemical reaction networks capable of probabilistic decision-making in response to stimuli. We show that a simple chemical reaction network motif can be tuned to produce arbitrary decision probabilities when choosing between two or more responses to a stimulus signal. We further show that simple feedback mechanisms from the environment can modify these probabilities over time, enabling the system to adapt its behavior dynamically in response to positive or negative reinforcement based on its decisions. This system thus acts as a form of operant conditioning of the chemical circuit, in the sense that feedback provided based on decisions taken by the circuit form the basis of the learning process. Our work thus demonstrates that simple chemical systems can be used to implement lifelike behavior in engineered biomimetic systems. Author summary Programming the biological world in a rational manner is a key goal in synthetic biology and synthetic cell research. While much current work focuses on the practical tasks of realizing the fundamental biochemical processes of life in an engineered system, there is also the need to realize control circuitry that regulates the processes of life and enables adaptive behavior. This work builds toward this goal by designing chemical systems capable of probabilistic decision-making and adaptive behavior in stochastic reaction networks with species populations comparable to those found in naturally occurring single-celled microorganisms. By showing that these networks can exhibit non-trivial responses to their environment this work opens the door to lifelike behaviors in engineered synthetic cells.