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Deciphering the generating rules and functionalities of complex networks.

ABSTRACT: Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

SUBMITTER: Xiao X

PROVIDER: S-EPMC8616909 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Deciphering the generating rules and functionalities of complex networks.

Xiao Xiongye X Chen Hanlong H Bogdan Paul P

Scientific reports 20211125 1

Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towa ...[more]

PMID: 34824290

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Project description:BackgroundRecent advances in the development of small chemical compounds that can modulate RNA splicing brought excitement to the field of splicing-targeting therapy. Splicing-targeting therapy tries to ameliorate the disease by altering the exon combination of transcripts to reduce the undesired effect of genetic mutations. However, the knowledge and tools to understand factors contributing to splicing modulator compound sensitivity have been lacking. Our goal was to establish a method to characterize sequence features found in compound sensitive exons.ResultsHere we developed a comparative transcriptomic approach to explore features that make an exon sensitive to a chemical compound. In this study, we chose TG003, a potential drug for Duchenne muscular dystrophy, and performed RNA-sequencing on samples from human and mouse skeletal muscle cells, with and without TG003 treatments. We compared TG003 responsiveness between homologous exon pairs and identified 21 pairs in which human exons were skip-enhanced but not mouse exons. We compared the sequence features; splice site scores, number of splicing factor binding sites, and properties of branch sequence and polypyrimidine tracts, and found that polypyrimidine tracts were stronger (longer stretches and richer content of consecutive polypyrimidine) in the mouse TG003 insensitive exons. We also compared the features between TG003 skip-enhanced and insensitive exons within the species, and discovered that human TG003 skip-enhanced exons were shorter and had less splicing factor binding sites than the group of human TG003 insensitive exons. Mouse insensitive exons homologous to human TG003 skip-enhanced exons shared these properties. Our results suggested that these features are prerequisites for TG003 skip-enhanced exons and weak polypyrimidine tracts are defining features, which were supported by a decision tree analysis on all cassette exons in human.ConclusionsIn this study we established a comparative transcriptomic approach, which shed lights on how small chemical compounds modulate RNA splicing. The results described here was the first attempt to decipher the targeting rules of a splicing modulator compound. We expect that this approach would contribute to the precise understanding of the mechanism of TG003-induced splicing modulation, expand target diseases of splicing modulators in general, as well as the development of new splicing modulators.

Dataset Information

Deciphering the generating rules and functionalities of complex networks.

Publications

Deciphering the generating rules and functionalities of complex networks.

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