4.6 Review

Self-Organization and Information Processing: From Basic Enzymatic Activities to Complex Adaptive Cellular Behavior

Journal

FRONTIERS IN GENETICS
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2021.644615

Keywords

entropy; dissipative structures; self-organization; Hopfield dynamics; information processing

Funding

  1. University of Basque Country UPV/EHU [US18/21]

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The main sources of biomolecular order and complexity in cellular life include dissipative self-organization and molecular information processing, leading to the emergence of systemic functional structures in the cell. Through studies on enzymatic activity, functional coordination, metabolic networks, and molecular rhythms, it has been demonstrated that self-organization is central to understanding cellular processes. The quantification of biomolecular information flows in dissipative metabolic networks has enabled efficient self-regulatory control of metabolism, and the emergence of Hopfield-like dynamics characterized by associative memory has been observed in experiments with individual amoeba cells, indicating a fundamental evolutionary mechanism for cell adaptation.
One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation.

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