Li, Zonglun;
Fattah, Alya;
Timashev, Peter;
Zaikin, Alexey;
(2022)
An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.
Sensors
, 22
(15)
, Article 5907. 10.3390/s22155907.
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Abstract
The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.
Type: | Article |
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Title: | An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation |
Location: | Switzerland |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/s22155907 |
Publisher version: | https://doi.org/10.3390/s22155907 |
Language: | English |
Additional information: | © 2022 MDPI. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | associative learning; molecular circuits; synthetic biology; mathematical modelling; Hill equation; Pavlov’s dog; reinforcement; dissociation; nondimensionalisation |
UCL classification: | UCL 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 > 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 |
URI: | https://discovery.ucl.ac.uk/id/eprint/10154673 |
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