Learning of Signaling Networks: Molecular Mechanisms

• Published in: Trends in biochemical sciences (Amsterdam. Regular ed.) Vol. 45; no. 4; pp. 284 - 294
• Main Authors: Csermely, Péter, Kunsic, Nina, Mendik, Péter, Kerestély, Márk, Faragó, Teodóra, Veres, Dániel V., Tompa, Péter
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2020
• Subjects: epigenetic memory; epithelial–mesenchymal transition; histone modification; molecular memory; signaling pathways; system-level memory; epithelial–mesenchymal transition; molecular memory; signaling pathways; histone modification; epigenetic memory; system-level memory
• ISSN: 0968-0004; 1362-4326
• DOI: 10.1016/j.tibs.2019.12.005

Molecular processes of neuronal learning have been well described. However, learning mechanisms of non-neuronal cells are not yet fully understood at the molecular level. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins (IDPs) and prions, signaling cascades, protein translocation, RNAs [miRNA and long noncoding RNA (lncRNA)], and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells, and we discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. Besides the well-known learning processes of neurons, non-neuronal, single cells are able to learn and show a more robust (and often faster) adaptive response when the same stimulus is repeated.Known examples of cellular learning are sensitization- or habituation-type responses.Several molecular mechanisms of neuronal learning, such as conformational memory, protein translocation, signaling cascades, miRNAs, lncRNAs, and chromatin memory, also participate in learning of non-neuronal, single cells.We propose that these molecular mechanisms form the integrative memory of signaling networks and display a generalized Hebbian learning process by increasing those edge weights through which the signal has been propagated.