Fractal rules in brain networks: Signatures of self-organization

Singh, Soibam Shyamchand; Haobijam, Dineshchandra; Ishrat, Romana

doi:10.1016/j.jtbi.2017.09.014

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…Brain functional networks are reported to be the fractal small world [190,297,298], and Gallos et al [194,299] revealed that the weak ties play a very important role in the optimal global integration of self-similar modules to generate the small world property in the functional brain networks according to renormalization group theory [300]. The fractal properties in the brain network would be an important signature of self-organization [301]. Singh et al [302] investigated the fractal properties of brain networks in different species, and the fractal dimensions in the high-level species (e.g., human and monkey) are much smaller than that in the low-level species (e.g., C. elegans), indicating a more ordered and systematically self-organized topography in higher-level species [303].…”

Section: Fractal and Self-similaritymentioning

confidence: 99%

Computational network biology: Data, models, and applications

et al. 2020

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Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing

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Section: Fractal and Self-similaritymentioning

confidence: 99%

Computational network biology: Data, models, and applications

et al. 2020

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“…Finally, after eliminating isolated nodes and duplicate edges, a PPI network comprising 1099 upregulated and 815 downregulated genes was constructed in Cytoscape. The networks topological properties were analysed using Network Analyser 46 and Cytohubba 47 plugin of Cytoscape.…”

Section: Methodsmentioning

confidence: 99%

Conserved Cardiovascular Network: Bioinformatics Insights into Genes and Pathways for EstablishingCaenorhabditis elegansas an Animal Model for Cardiovascular Diseases

Ray,

Priya,

Malik

et al. 2023

Preprint

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Cardiovascular disease (CVD) is a collective term for disorders of the heart and blood vessels. The molecular events and biochemical pathways associated with CVD are difficult to study in clinical settings on patients andin vitroconditions. Animal models play a pivotal and indispensable role in cardiovascular disease (CVD) research.Caenorhabditis elegans, a nematode species, has emerged as a prominent experimental organism widely utilised in various biomedical research fields. However, the specific number of CVD-related genes and pathways within theC. elegansgenome remains undisclosed to date, limiting its in-depth utilisation for investigations. In the present study, we conducted a comprehensive analysis of genes and pathways related to CVD within the genomes of humans andC. elegansthrough a systematic bioinformatic approach.A total of 1113 genes inC. elegansorthologous to the most significant CVD-related genes in humans were identified, and the GO terms and pathways were compared to study the pathways that are conserved between the two species. In order to infer the functions of CVD-related orthologous genes inC. elegans, aPPI network was constructed. Orthologous gene PPI network analysis results reveal the hubs and important KRs:pmk-1, daf-21, gpb-1, crh-1, enpl-1, eef-1G, acdh-8, hif-1, pmk-2,andaha-1 in C. elegans.Modules were identified for determining the role of the orthologous genes at various levels in the created network. We also identified 9 commonly enriched pathways between humans andC. eleganslinked with CVDs that include autophagy (animal), the ErbB signalling pathway, the FoxO signalling pathway, the MAPK signalling pathway, ABC transporters, the biosynthesis of unsaturated fatty acids, fatty acid metabolism, glutathione metabolism, and metabolic pathways. This study provides the first systematic genomic approach to explore the CVD-associated genes and pathways that are present inC. elegans,supporting the use ofC. elegansas a prominent animal model organism for cardiovascular diseases.

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“…However, numerical solutions for the master Equation 2 can be derived using stochastic simulation algorithm (SSA) ( Gillespie, 1977 ; Gillespie, 2000 ) that is based on the theoretical foundations developed by Doob (1942) and initially proposed by Kendall (1950) . The stochastic simulation implements a Monte Carlo algorithm that provides the exact numerical solution by considering every possible interaction in the system ( Gillespie, 1977 ; Singh et al, 2018 ). This algorithm is in deed a non-spatial, individual-based analog of the master Equation 2 , which is constructed on the physical basis of molecular collision in each reaction channel at a certain constant temperature.…”

Section: Methodsmentioning

confidence: 99%

“…These two processes are implemented in this algorithm by generating two statistically independent random numbers, namely

and

such that the reaction time is computed using

, where

is the

propensity function given by

, where

is the number of possible molecular combinations of

reaction, and the

reaction will fire when it satisfies

. Intrinsic noise (

) associated with the clock protein dynamics in the system is inversely proportional to the square root of the systems size

(i.e.,

( Gillespie, 2000 ; Singh et al, 2018 ; Sharma et al, 2019 ; Singh et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Disruption in the regulation of casein kinase 2 in circadian rhythm leads to pathological states: cancer, diabetes and neurodegenerative disorders

Malik

Dashti

Fatima

et al. 2023

Front. Mol. Neurosci.

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IntroductionCircadian rhythm maintains the sleep–wake cycle in biological systems. Various biological activities are regulated and modulated by the circadian rhythm, disruption of which can result in onset of diseases. Robust rhythms of phosphorylation profiles and abundances of PERIOD (PER) proteins are thought to be the master keys that drive circadian clock functions. The role of casein kinase 2 (CK2) in circadian rhythm via its direct interactions with the PER protein has been extensively studied; however, the exact mechanism by which it affects circadian rhythms at the molecular level is not known.MethodsHere, we propose an extended circadian rhythm model in Drosophila that incorporates the crosstalk between the PER protein and CK2. We studied the regulatory role of CK2 in the dynamics of PER proteins involved in circadian rhythm using the stochastic simulation algorithm.ResultsWe observed that variations in the concentration of CK2 in the circadian rhythm model modulates the PER protein dynamics at different cellular states, namely, active, weakly active, and rhythmic death. These oscillatory states may correspond to distinct pathological cellular states of the living system. We find molecular noise at the expression level of CK2 to switch normal circadian rhythm to any of the three above-mentioned circadian oscillatory states. Our results suggest that the concentration levels of CK2 in the system has a strong impact on its dynamics, which is reflected in the time evolution of PER protein.DiscussionWe believe that our findings can contribute towards understanding the molecular mechanisms of circadian dysregulation in pathways driven by the PER mutant genes and their pathological states, including cancer, obesity, diabetes, neurodegenerative disorders, and socio-psychological disease.

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Fractal rules in brain networks: Signatures of self-organization

Cited by 9 publications

References 46 publications

Computational network biology: Data, models, and applications

Computational network biology: Data, models, and applications

Conserved Cardiovascular Network: Bioinformatics Insights into Genes and Pathways for EstablishingCaenorhabditis elegansas an Animal Model for Cardiovascular Diseases

Disruption in the regulation of casein kinase 2 in circadian rhythm leads to pathological states: cancer, diabetes and neurodegenerative disorders

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