title: Synthetic biology routes to bio-artificial intelligence.
abstract: The design of synthetic gene networks (SGNs) 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 SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI).
authors.affiliation: Department of Biochemical Engineering, University College London, Bernard Katz Building, London WC1E 6BT, U.K. d.nesbeth@ucl.ac.uk.
authors.forename: Darren N
authors.initials: DN
authors.lastname: Nesbeth
authors.affiliation: Department of Mathematics, University College London, Gower Street, London WC1E 6BT, U.K.|Institute for Women's Health, University College London, London WC1E 6AU, U.K.
authors.forename: Alexey
authors.initials: A
authors.lastname: Zaikin
authors.affiliation: School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Saka
authors.affiliation: School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.|Department of Physics, Institute for Complex Systems and Mathematical Biology, Meston Building, Old Aberdeen, Aberdeen, U.K.
authors.forename: M Carmen
authors.initials: MC
authors.lastname: Romano
authors.affiliation: School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.
authors.forename: Claudiu V
authors.initials: CV
authors.lastname: Giuraniuc
authors.affiliation: Oscillation Theory Department, Lobachevsky State University of Nizhniy Novgorod, Novgorod, Russia.
authors.forename: Oleg
authors.initials: O
authors.lastname: Kanakov
authors.affiliation: Department of Control Theory and Systems Dynamics, Lobachevsky State University of Nizhniy Novgorod, Novgorod, Russia.
authors.forename: Tetyana
authors.initials: T
authors.lastname: Laptyeva
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D002450:Cell Communication; D053263:Gene Regulatory Networks; D006801:Humans; D007858:Learning; D008954:Models, Biological; D058615:Synthetic Biology / Q000379:methods*
keywords: artificial intelligence; gene networks; synthetic biological circuits; synthetic biology