Modeling non-genetic information dynamics in cells using reservoir computing

iScience(2024)

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摘要
Virtually all cells use energy-driven, ion-specific membrane pumps to maintain large transmembrane gradients of Na+, K+, Cl-, Mg++, and Ca++, but the corresponding evolutionary benefit remains unclear. We propose these gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes of cytoplasmic ion concentration can generate a local response or orchestrate global/regional cellular dynamics through wire-like ion fluxes along pre-existing and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus.We frame our hypothesis through a quasi-physical (Cell-Reservoir) model of intra-cellular ion-based information dynamics that permit spatiotemporally resolved cellular response that includes learning complex nonlinear cellular behaviors. We demonstrate that the proposed ion dynamics permit rapid dissemination of information and response to extrinsic perturbations consistent with experimental observations.
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关键词
cellular information dynamics,microtubules,microfilaments,transmembrane potential,reservoir computing,grid graph,machine learning
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