CC BY
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Section COMPUTATIONAL NEUROSCIENCE
COMPUTATIONAL NEUROSCIENCE
Intelligence in Intracellular Gene-Regulatory Networks
A.Zaikin*
Department of Mathematics and Institute for Women's Health, University College London, United Kingdom. * Presenting e-mail: alexey.zaikin@ucl.ac.uk
I discuss results of theoretical modeling in very multi-disciplinary area between Systems Medicine, Synthetic Biology, Artificial Intelligence and Applied Mathematics. Multicellular systems, e.g. neural networks of a living brain, can learn and be intelligent. Some of the principles of this intelligence have been mathematically formulated in the study of Artificial Intelligence (AI), starting from the basic Rosenblatt's and associative Hebbian perceptrons and resulting in modern artificial neural networks with multilayer structure and recurrence. In some sense AI has mimicked the function of natural neural networks. However, relatively simple systems as cells are also able to perform tasks such as decision making and learning by utilizing their genetic regulatory frameworks. Intracellular genetic networks can be more intelligent than it could be assumed due to their ability to learn. Hence, one can speculate that each neuron probably has an intracellular network on a genetic level, based and functioning on the principle of artificial intelligence [1]. Such learning includes classification of several inputs or intracellular intelligence can manifest itself in the ability to learn association between two stimuli within gene regulating circuitry. However, gene expression is an intrinsically noisy process, hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We show that counter-intuitively genetic noise can improve learning inside the cell [2-4]. We discuss several designs of genetic networks illustrating the fact that intelligence, as it is understood in the science of artificial intelligence, can be built inside the cell, on the gene-regulating scale. Without any doubt, neurons or astrocytes, being a very sophisticated cells, use this possible functionality in one or another form. It is an intriguing question, how learning and changes of weighting is executed in the real genome of the neuron. We put forward the hypothesis that weights are implemented in the form of DNA methylation pattern, as a kind of long time memory. During the talk I will also include brief introductions/tutorials about Synthetic Biology, modelling of genetic networks and noise-induced ordering.
Acknowledgements
This work was supported by the Russian Science Foundation (grant 16-12-00077). References
1. V. Samborska, S. Gordleeva, E. Ullner, A. Lebedeva, V. Kazanteev, M. Ivanchenko, and A. Zaikin, "Mammalian brain as network of networks", Opera Medica & Physiologica 1, 23-38 (2016).
2. S.Yu. Filicheva, A. Zaikin, O.I. Kanakov, "Dynamical decision making in a genetic perceptron", Physica D, Vol. 318-319, 112-115 (2016).
3. R. Bates, O. Blyuss, A. Alsaedi, and A. Zaikin, "Effect of noise in intelligent cellular decision making", PLOS ONE 10(5), e0125079 (2015).
4. R. Bates, O. Blyuss, and A. Zaikin," Stochastic resonance in an intracellular genetic perceptron", Phys. Rev. E, 89, 032716 (2014).
Coherence Enhancement in Coupled Chaotic Neurons
A. N. Pisarchik1 *, R. Jaimes-Reátegui2, and M. A. García-Hernandez1
1 Center for Biomedical Technology, Technical University of Madrid, Spain;
2 Centro Universitario de los Lagos, Universidad de Guadalajara, Mexico. * Presenting e-mail: alexander.pisarchik@ctb.upm.es
The emergence of order from chaos is one of the greatest mysteries of the universe. In his famous book "Order Out of Chaos" Ilya Prigogine argued that systems being far from equilibrium, with a high flow-through of energy could proOpera Med Physiol 2016 Vol. 2 (S1) 51
OM&P
