The design of synthetic gene networks has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using synthetic gene networks and present an example of such a scheme and in silico simulation of its performance. In addition to designed synthetic gene networks, we also consider the option to establish conditions in which a population of synthetic gene networks can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence and the future challenges raised by bio-artificial intelligence.
Nesbeth, D. N., Zaikin, A., Saka, Y., Romano, M. C., Giuraniuc, C. V., Kanakov, O., & Laptyeva, T. (2016). Synthetic biology routes to bio-artificial intelligence. Essays in Biochemistry, 60(4), 381-391. https://doi.org/10.1042/EBC20160014