Networks behind the morphology and structural design of living systems

Gosak, Marko; Milojević, Marko; Duh, Maja; Skok, Kristijan; Perc, Matjaž

doi:10.1016/j.plrev.2022.03.001

Cited by 72 publications

(28 citation statements)

References 241 publications

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“…The research interests have evolved in several directions, from social and economic systems ( 3 , 308 , 309 ), to a wide range of engineered and technological systems ( 86 , 87 , 310 ), and to the more recently developing field of multilayered networks ( 19 , 219 ). Guided by the advances in high-throughput data-collection and imaging techniques, network analyses are also becoming an indispensable tool in biomedical sciences across multiple disciplines and levels of organization ( 15 , 36 , 82 , 85 ), including in studies of intercellular interactions in tissues ( 18 , 59 ). Understanding how heterogeneous populations of interconnected cells in a dynamic and noisy environment operate to ensure proper function is very appealing and challenging to investigate.…”

Section: Discussionmentioning

confidence: 99%

“…In its purest form, a graph or a network is an abstract mathematical object consisting of nodes (or vertices) which are connected by links (or edges or connections). Because of this rather simple definition, complex networks represent a general and very useful framework to describe a large variety of social ( 3 , 8 , 81 ), biological ( 17 , 18 , 82 – 85 ), and technological systems ( 86 – 88 ). Nodes and edges can be regarded as a manifestation of some properties of a system and the corresponding network is a simplified mathematical representation of the relationships (edges) between variables (nodes), that does not necessarily encompass all the details of the underlying system.…”

Section: Types Of Network and Network Metricsmentioning

confidence: 99%

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

Stožer

Šterk²,

Leitgeb³

et al. 2022

Front. Endocrinol.

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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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Section: Discussionmentioning

confidence: 99%

Section: Types Of Network and Network Metricsmentioning

confidence: 99%

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

Stožer

Šterk²,

Leitgeb³

et al. 2022

Front. Endocrinol.

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View full text Add to dashboard Cite

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“…Approaches that characterize properties of interactions form the basis (so called "functional" and "effective" connectivity) of a data-driven construction of functional brain networks (Eguıluz et al, 2005;Bullmore and Sporns, 2009;Bullmore and Sporns, 2012;Stam, 2014;Bassett and Sporns, 2017;Lynn and Bassett, 2019). These approaches are backed up with a variety of imaging techniques (Sporns, 2011;Fornito et al, 2015;Rockland, 2015;Fornito et al, 2019;Sotiropoulos and Zalesky, 2019;Sarwar et al, 2021;Yeh et al, 2021;Gosak et al, 2022) that aim at characterizing the so called "structural" connectivity, which is often considered ground truth and underlying constraint on "functional"/"effective" connectivity. Structural information is mostly derived from magnetic-imaging-based techniques such as diffusion tensor imaging (DTI).…”

Section: Discussionmentioning

confidence: 99%

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Goodfellow

Andrzejak

Masoller

et al. 2022

Front. Netw. Physiol.

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Despite impressive scientific advances in understanding the structure and function of the human brain, big challenges remain. A deep understanding of healthy and aberrant brain activity at a wide range of temporal and spatial scales is needed. Here we discuss, from an interdisciplinary network perspective, the advancements in physical and mathematical modeling as well as in data analysis techniques that, in our opinion, have potential to further advance our understanding of brain structure and function.

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“…In recent years, the models are becoming increasingly comprehensive as they incorporate several types of neuronal populations, different types of interactions, information transmission delays, plasticity in connectivity patterns, etc. Moreover, along with the developments in the field of network science, the scope is shifting to multilayer networks as this novel concept offers a more comprehensive framework to assess the complex interactions across multiple facets of neurophysiological relationships and the resulting dynamical phenomena (Boccaletti et al, 2014 ; Kivelä et al, 2014 ; Gosak et al, 2018 , 2022 ; Battiston et al, 2020 ; Torres et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

Ristič

Gosak

2022

Front. Comput. Neurosci.

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The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

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Networks behind the morphology and structural design of living systems

Cited by 72 publications

References 241 publications

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

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

What Models and Tools can Contribute to a Better Understanding of Brain Activity?

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

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