Effect of Noise in Intelligent Cellular Decision Making

Abstract

Similar to intelligent multicellular neural networks controlling human brains, even single cells, surprisingly, are able to make intelligent decisions to classify several external stimuli or to associate them. This happens because of the fact that gene regulatory networks can perform as perceptrons, simple intelligent schemes known from studies on Artificial Intelligence. We study the role of genetic noise in intelligent decision making at the genetic level and show that noise can play a constructive role helping cells to make a proper decision. We show this using the example of a simple genetic classifier able to classify two external stimuli.

Recurrence-based information processing in gene regulatory networks

“…Non-recurrent gene network architectures have been proposed in the past as mechanisms of information integration and storage, 31,32 associative learning, 33,34 and cellular decision making. 35,36 However, processing of time-dependent information requires recurrent topologies such as the ones investigated in this paper. The nonconventional computation framework proposed here also implies that the integration of information is distributed across the network in large and diffuse structures with well-defined functional roles.…”

Decision Making in an Intracellular Genetic Classifier

“…Approaches from synthetic biology have been used to gain insight into this issue by constructing proof-of-principle systems. Recently, it was shown the constructive role of noise for this decision process in a synthetic genetic classifier circuit [10,11]. The model is motivated to function as a genetic perceptron with the ability of discriminating external stimuli while providing one out of two possible states.…”

“…In this way, the output value will depend on the state of the whole classifier at any time. In the work proposed in [10] the influence of noise in linear classification was studied when the threshold of classification is spoiled. Their results show that additive noise following a normal distribution and discriminated according to a linear classification leads to stochastic resonance in the accuracy of classification.…”

Synthetic biology routes to bio-artificial intelligence

“…As such, a complete description of the relevant models is not necessary to understand the concepts presented here. Readers who do wish to examine the mathematical models further should refer to the cited literature and reviews by Bates et al [11] and Borg et al [12]. Specific technical details can be provided via the corresponding author.…”