Decision Making in an Intracellular Genetic Classifier

作者：Abrego L; Zaikin A^*

来源：Mathematical Modelling of Natural Phenomena, 2017, 12(4): 30-42.

DOI：10.1051/mmnp/201712404

• binary classification
• decision making
• intrinsic noise
• perceptron
• intelligence

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.

• 出版日期2017