This article tackles the topic of the special issue “Biology in AI: New Frontiers in Hardware, Software and Wetware Modeling of Cognition” in two ways. It addresses the problem of the relevance of hardware, software, and wetware models for the scientific understanding of biological cognition, and it clarifies the contributions that synthetic biology, construed as the synthetic exploration of cognition, can offer to artificial intelligence (AI). The research work proposed in this article is based on the idea that the relevance of hardware, software, and wetware models of biological and cognitive processes—that is, the concrete contribution that these models can make to the scientific understanding of life and cognition—is still unclear, mainly because of the lack of explicit criteria to assess in what ways synthetic models can support the experimental exploration of biological and cognitive phenomena. Our article draws on elements from cybernetic and autopoietic epistemology to define a framework of reference, for the synthetic study of life and cognition, capable of generating a set of assessment criteria and a classification of forms of relevance, for synthetic models, able to overcome the sterile, traditional polarization of their evaluation between mere imitation and full reproduction of the target processes. On the basis of these tools, we tentatively map the forms of relevance characterizing wetware models of living and cognitive processes that synthetic biology can produce and outline a programmatic direction for the development of “organizationally relevant approaches” applying synthetic biology techniques to the investigative field of (embodied) AI.

Synthetic or semi-synthetic minimal cells are those cell-like artificial compartments that are based on the encapsulation of molecules inside lipid vesicles (liposomes). Synthetic cells are currently used as primitive cell models and are very promising tools for future biotechnology. Despite the recent experimental advancements and sophistication reached in this field, the complete elucidation of many fundamental physical aspects still poses experimental and theoretical challenges. The interplay between solute capture and vesicle formation is one of the most intriguing ones. In a series of studies, we have reported that when vesicles spontaneously form in a dilute solution of proteins, ribosomes, or ribo-peptidic complexes, then, contrary to statistical predictions, it is possible to find a small fraction of liposomes (<1%) that contain a very large number of solutes, so that their local (intravesicular) concentrations largely exceed the expected value. More recently, we have demonstrated that this effect (spontaneous crowding) operates also on multimolecular mixtures, and can drive the synthesis of proteins inside vesicles, whereas the same reaction does not proceed at a measurable rate in the external bulk phase. Here we firstly introduce and discuss these already published observations. Then, we present a computational investigation of the encapsulation of transcription-translation (TX-TL) machinery inside vesicles, based on a minimal protein synthesis model and on different solute partition functions. Results show that experimental data are compatible with an entrapment model that follows a power law rather than a Gaussian distribution. The results are discussed from the viewpoint of origin of life, highlighting open questions and possible future research directions.

Spherical bounded structures such as those formed by surfactant aggregates (mostly micelles and vesicles), with an inside that is chemically and physically different from the outside medium, can be seen as primitive cell models. As such, they are fundamental structures for the theory of autopoiesis as originally formulated by Varela and Maturana. In particular, since self-reproduction is a very important feature of minimal cellular life, the study of self-reproduction of micelles and vesicles represents a quite challenging bio-mimetic approach. Our laboratory has put much effort in recent years into implementing self-reproduction of vesicles as models for self-reproduction of cellular bounded structures, and this article is a further contribution in this direction. In particular, we deal with the so-called matrix effect of vesicles, related to the fact that when fresh surfactant is added to an aqueous solution containing preformed vesicles of a very narrow size distribution, the newly formed vesicles (instead of being polydisperse, as is usually the case) have dimensions very close to those of the preformed ones. In practice, this corresponds to a mechanism of reproduction of vesicles of the same size. In this article, the matrix effect is re-elaborated in the perspective of the origin of life, and in particular in terms of the prebiotic mechanisms that might permit the growth and reproduction of vesicles. The data are analyzed by dynamic light scattering with a new program that permits the calculation of the number-weighted size distribution. It is shown that, on adding a stoichiometric amount of oleate micelles to preformed oleate vesicles extruded at 50 and 100 nm, the final distribution contains about twice the initial number of particles, centered around 50 and 100 nm. The same holds when oleate is added to preformed phospholipid liposomes. By contrast, when the same amount of oleate is added to an aqueous solution (as a control experiment), a very broad distribution ranging between 20 and 1000 nm is obtained. The data can then be seen as a kind of reproduction of the same size vesicles, and the argument is advanced that this may correspond to a simple prebiotic mechanism of vesicle multiplication in prebiotic times, when only physical forces might be responsible for the basic mechanisms of early protocell growth and division. Preliminary data also show that repeated addition of oleate maintains the same basic initial features, and that surfactants other than oleate also respect the reproductive mode of the matrix effect.