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Decision Making in an Intracellular Genetic Classifier

Abrego, L; Zaikin, A; (2017) Decision Making in an Intracellular Genetic Classifier. Mathematical Modelling of Natural Phenomena , 12 (4) pp. 30-42. 10.1051/mmnp/201712404. Green open access

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Abstract

A model for an intracellular genetic classifier is introduced and studied to investigate how cellular decision making will function under the stochastic conditions. In particular, this provides a basis to investigate whether a binary classification under the effects of intrinsic noise is still possible. More precisely, a mathematical model of a genetic classifier is derived using a standard approach using Hill functions and its dynamical properties are explored. Classification mechanism is studied considering the effects of low copy number of mRNA and proteins in terms of the degree of cooperativity, inputs and transcription rates. It is shown that the intrinsic noise blurs the separation line between the classification classes, but the influence of stochasticity is qualitatively different for the case of monostable or bistable dynamics. Finally, potential applications are discussed.

Type: Article
Title: Decision Making in an Intracellular Genetic Classifier
Open access status: An open access version is available from UCL Discovery
DOI: 10.1051/mmnp/201712404
Publisher version: https://doi.org/10.1051/mmnp/201712404
Language: English
Additional information: Copyright © EDP Sciences 2017. This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: binary classification, decision making, intrinsic noise, perceptron, intelligence
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
URI: https://discovery.ucl.ac.uk/id/eprint/1568551
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