Does a Self-Similarity Logic Shape the Organization of the Nervous System?

Guidolin, Diego; Tortorella, Cinzia; De, Raffaele; Agnati, Luigi F.

doi:10.1007/978-1-4939-3995-4_9

Cited by 6 publications

(6 citation statements)

References 98 publications

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“…A hierarchical or nested architecture has been suggested as a suitable model providing a unified view of the different spatial scales characterizing the brain network organization (Sporns et al, 2005;Sporns, 2013;Guidolin et al, 2016). In this respect, an almost general consensus exists (see Sporns et al, 2005;Sporns, 2013) in targeting at least three levels of organization: the macroscale of brain areas and regions, the mesoscale involving nerve cells networks, and the microscale where single cells and synaptic clusters (Cutsuridis et al, 2009) can be found.…”

Section: Discussionmentioning

confidence: 99%

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

Guidolin

Marcoli

Tortorella

et al. 2018

Reviews in the Neurosciences

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Abstract:The proposal of receptor-receptor interactions (RRIs) in the early 1980s broadened the view on the role of G protein-coupled receptors (GPCR) in the dynamics of the intercellular communication. RRIs, indeed, allow GPCR to operate not only as monomers but also as receptor complexes, in which the integration of the incoming signals depends on the number, spatial arrangement, and order of activation of the protomers forming the complex. The main biochemical mechanisms controlling the functional interplay of GPCR in the receptor complexes are direct allosteric interactions between protomer domains. The formation of these macromolecular assemblies has several physiologic implications in terms of the modulation of the signaling pathways and interaction with other membrane proteins. It also impacts on the emerging field of connectomics, as it contributes to set and tune the synaptic strength. Furthermore, recent evidence suggests that the transfer of GPCR and GPCR complexes between cells via the exosome pathway could enable the target cells to recognize/decode transmitters and/or modulators for which they did not express the pertinent receptors. Thus, this process may also open the possibility of a new type of redeployment of neural circuits. The fundamental aspects of GPCR complex formation and function are the focus of the present review article.

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Section: Discussionmentioning

confidence: 99%

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

Guidolin

Marcoli

Tortorella

et al. 2018

Reviews in the Neurosciences

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“…A network may be realized physically in space, and may be geometrically self-similar, but this does not necessarily imply that it is a scale-free “network.” In the brain, e.g. we can see a geometrically self-similar structure from the micro-level up to the complex cellular networks connecting different areas ( Guidolin et al. 2016 ), but the network (graph) itself is not scale-free.…”

Section: Scale-free Systems In General and In Naturementioning

confidence: 99%

“…From a geometric perspective, complex networks on the macro-scale show high resemblance to micro-scale neuronal connections ( Guidolin et al. 2016 ).…”

Section: Scale-free Structure In the Brain At Different Levels Of Org...mentioning

confidence: 99%

The fractal brain: scale-invariance in structure and dynamics

et al. 2022

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The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

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“…This effect is typical in genomics, but it is also common in the theory of nonlinear dynamical systems, signal processing, brain tissue morphology, etc. [52]–[59].…”

Section: Resultsmentioning

confidence: 99%

“…The large-size genomic data are often patterned, and each pattern can have its fractal dimension, i.e., the sequences can be multifractals [31]. This effect is typical in genomics, but it is also common in the theory of nonlinear dynamical systems, signal processing and brain tissue morphology, among others [51]-[63].…”

Section: Methodsmentioning

confidence: 99%

Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

Belinsky¹,

Kouzaev

2021

Preprint

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The worldwide spread of SARS-CoV-2 virus increases interest in the research of virus genomics and the creation of more advanced study methods. This work aims to develop a new fast DNA walk algorithm for one-dimensional visualization of RNAs based on a big-data method and comparative examination of several viruses and their lines and strains. In this work, a new metric-based algorithm for quantitative and visual analyses of RNAs is proposed and considered. It allows finding any fragments of genomic sequences using the Hamming distance between the binary-expressed RNA characters and symbols of a fragment under the search and building one-dimensional trajectories of genomic walks convenient for quantitative and qualitative analyses of RNAs and DNAs. Similarly, human-language texts can be processed and compared with genomic sequences.This algorithm is used to investigate the complete genomic sequences of SARS CoV-2, MERS, Dengue, and Ebola viruses available from Genbank and GISAID databases. The distributions of atg codon-starting triplets along with these sequences are built and considered as their atg-schemes. Additionally to the atg-walks, single-symbols distributions are calculated to detect the codon-content mutations, which do not change the atg-triplet coordinates along with genomic sequences. The visual analyses of distributions consisting of several hundred triplets enable us to define the level of stability of RNAs towards essential mutations and perform their classing. Statistical studies are applied to distributions of the inter-atg and inter-symbol distances along with genomic sequences. The fractal dimension values of these distributions are calculated, enabling them to correspond to the mutations discovered by Hamming walks and fractal-dimension values of several ten virus samples investigated here. The developed metric-based-based algorithm allows building one-dimensional RNA schemes of different scale levels and effectively analyzing the virus mutations with their classing.

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Does a Self-Similarity Logic Shape the Organization of the Nervous System?

Cited by 6 publications

References 98 publications

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

The fractal brain: scale-invariance in structure and dynamics

Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

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