Recurrent neural networks in synthetic cells: a route to autonomous molecular agents?

Prompted by recent advancements in synthetic biology, we highlight the possible use of bio-chemical reactivity to generate tools and strategies for a genuinely new AI in the wetware domain. Chemical neural networks (CNNs) could be realized inside synthetic cells by employing elements of bacterial two-component signaling systems. The major novelty of CNNs, when compared to neural networks, consists in the embodiment of the network nodes and links: these network elements are no more logical entities but physical ones, whose behavior is subjected to the physico-chemical laws. Moreover, the results of network computation (i.e., molecules) belong to the same domain as the network elements, the physical domain. Building on this, we illustrate a viable way for implementing recurrent links in CNNs, making them able to have an internal state, i.e. a memory. This capability makes it possible to endow CNNs with some sort of autonomy, as they can take decisions also on the basis of their state and not just as a consequence of the computation of the inputs. At a more fundamental level, synthetic cell technology can be a platform for crucial investigations of theoretical biology principles. For instance, autonomy can be seen as a prerequisite for agency and other more complex characteristics of living beings.