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      From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science

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          Abstract

          Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.

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          Most cited references309

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Complex brain networks: graph theoretical analysis of structural and functional systems.

            Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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              Emergence of Scaling in Random Networks

              Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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                Author and article information

                Contributors
                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrinol.
                Frontiers in Endocrinology
                Frontiers Media S.A.
                1664-2392
                15 June 2022
                2022
                : 13
                : 922640
                Affiliations
                [1] 1 Institute of Physiology, Faculty of Medicine, University of Maribor , Maribor, Slovenia
                [2] 2 Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor , Maribor, Slovenia
                [3] 3 Institute of Mathematics and Physics, Faculty of Electrical Engineering and Computer Science, University of Maribor , Maribor, Slovenia
                [4] 4 Department of Pharmacology and Alberta Diabetes Institute, University of Alberta , Edmonton, AB, Canada
                Author notes

                Edited by: Quan Zhang, University of Oxford, United Kingdom

                Reviewed by: Kazuki Harada, The University of Tokyo, Japan; Xuelin Lou, Medical College of Wisconsin, United States

                *Correspondence: Marko Gosak, marko.gosak@ 123456um.si

                This article was submitted to Diabetes: Molecular Mechanisms, a section of the journal Frontiers in Endocrinology

                Article
                10.3389/fendo.2022.922640
                9240343
                35784543
                ae05b5a3-5af3-4cba-b5d6-b767dd830b56
                Copyright © 2022 Stožer, Šterk, Paradiž Leitgeb, Markovič, Skelin Klemen, Ellis, Križančić Bombek, Dolenšek, MacDonald and Gosak

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 April 2022
                : 16 May 2022
                Page count
                Figures: 10, Tables: 0, Equations: 0, References: 310, Pages: 28, Words: 14327
                Funding
                Funded by: Javna Agencija za Raziskovalno Dejavnost RS , doi 10.13039/501100004329;
                Award ID: P3-0396, P1-0055, I0-0029, J3-3077, J1-2457, J3-9289, N3-0048, N3-0170, N3-0133
                Categories
                Endocrinology
                Review

                Endocrinology & Diabetes
                pancreatic islets,beta cells,calcium imaging,intercellular communication,functional networks,multilayer networks

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