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Synthetic biology routes to bio-artificial intelligence

Nesbeth, DN; Zaikin, A; Saka, Y; Romano, MC; Giuraniuc, CV; Kanakov, O; Laptyeva, T; (2016) Synthetic biology routes to bio-artificial intelligence. Essays in Biochemistry , 60 (4) pp. 381-391. 10.1042/EBC20160014. Green open access

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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).

Type: Article
Title: Synthetic biology routes to bio-artificial intelligence
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1042/EBC20160014
Publisher version: http://dx.doi.org/10.1042/EBC20160014
Language: English
Additional information: © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution Licence 4.0 (CC BY).
Keywords: artificial intelligence, gene networks, synthetic biological circuits, synthetic biology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1531681
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