Synthetic biology routes to bio-artificial intelligence.

Journal: Essays in biochemistry
Published Date: Nov 30, 2016

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

  • Darren N Nesbeth
    Department of Biochemical Engineering, University College London, Bernard Katz Building, London WC1E 6BT, U.K. d.nesbeth@ucl.ac.uk.
  • Alexey Zaikin
    Department of Mathematics, University College London, London, UK; Institute for Women's Health, University College London, London, UK; Lobachevsky State University of Nizhniy Novgorod, Nizhniy Novgorod, Russia.
  • Yasushi Saka
    School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.
  • M Carmen Romano
    School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.
  • Claudiu V Giuraniuc
    School of Medicine, Medical Sciences and Nutrition, Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, U.K.
  • Oleg Kanakov
    Oscillation Theory Department, Lobachevsky State University of Nizhniy Novgorod, Novgorod, Russia.
  • Tetyana Laptyeva
    Department of Control Theory and Systems Dynamics, Lobachevsky State University of Nizhniy Novgorod, Novgorod, Russia.

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