scholarly journals Dissecting molecular network structures using a network subgraph approach

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9556
Author(s):  
Chien-Hung Huang ◽  
Efendi Zaenudin ◽  
Jeffrey J.P. Tsai ◽  
Nilubon Kurubanjerdjit ◽  
Eskezeia Y. Dessie ◽  
...  
Keyword(s):  
Biological Networks ◽  
Spectral Graph Theory ◽  
Molecular Networks ◽  
Driver Genes ◽  
Complexity Measures ◽  
Cellular Processes ◽  
Graph Energy ◽  
Energy Cutoff ◽  
Cancer Networks ◽  
Irreducible Graphs

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

scholarly journals Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory

2019 ◽  
Author(s):  
Chien-Hung Huang ◽  
Jeffrey J. P. Tsai ◽  
Nilubon Kurubanjerdjit ◽  
Ka-Lok Ng
Keyword(s):  
Graph Theory ◽  
Frequency Distribution ◽  
Shannon Entropy ◽  
Biological Networks ◽  
Spectral Graph Theory ◽  
Molecular Networks ◽  
Network Motifs ◽  
Complexity Measures ◽  
Spectral Graph ◽  
Graph Energy

AbstractMolecular networks are described in terms of directed multigraphs, so-called network motifs. Spectral graph theory, reciprocal link and complexity measures were utilized to quantify network motifs. It was found that graph energy, reciprocal link and cyclomatic complexity can optimally specify network motifs with some degree of degeneracy. Biological networks are built up from a finite number of motif patterns; hence, a graph energy cutoff exists and the Shannon entropy of the motif frequency distribution is not maximal. Also, frequently found motifs are irreducible graphs. Network similarity was quantified by gauging their motif frequency distribution functions using Jensen-Shannon entropy. This method allows us to determine the distance between two networks regardless of their nodes’ identities and network sizes.This study provides a systematic approach to dissect the complex nature of biological networks. Our novel method different from any other approach. The findings support the view that there are organizational principles underlying molecular networks.

scholarly journals Competing Endogenous RNAs in Cervical Carcinogenesis: A New Layer of Complexity

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 991
Author(s):  
Fernanda Costa Brandão Berti ◽  
Sara Cristina Lobo-Alves ◽  
Camila de Freitas Oliveira-Toré ◽  
Amanda Salviano-Silva ◽  
Karen Brajão de Oliveira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fine Tuning ◽  
Molecular Networks ◽  
Cervical Carcinogenesis ◽  
Molecular Changes ◽  
Cellular Processes ◽  
Target Mrnas ◽  
Competing Endogenous Rnas ◽  
Cervical Cells ◽  
Post Transcriptional Regulation

MicroRNAs (miRNAs) regulate gene expression by binding to complementary sequences within target mRNAs. Apart from working ‘solo’, miRNAs may interact in important molecular networks such as competing endogenous RNA (ceRNA) axes. By competing for a limited pool of miRNAs, transcripts such as long noncoding RNAs (lncRNAs) and mRNAs can regulate each other, fine-tuning gene expression. Several ceRNA networks led by different lncRNAs—described here as lncRNA-mediated ceRNAs—seem to play essential roles in cervical cancer (CC). By conducting an extensive search, we summarized networks involved in CC, highlighting the major impacts of such dynamic molecular changes over multiple cellular processes. Through the sponging of distinct miRNAs, some lncRNAs as HOTAIR, MALAT1, NEAT1, OIP5-AS1, and XIST trigger crucial molecular changes, ultimately increasing cell proliferation, migration, invasion, and inhibiting apoptosis. Likewise, several lncRNAs seem to be a sponge for important tumor-suppressive miRNAs (as miR-140-5p, miR-143-3p, miR-148a-3p, and miR-206), impairing such molecules from exerting a negative post-transcriptional regulation over target mRNAs. Curiously, some of the involved mRNAs code for important proteins such as PTEN, ROCK1, and MAPK1, known to modulate cell growth, proliferation, apoptosis, and adhesion in CC. Overall, we highlight important lncRNA-mediated functional interactions occurring in cervical cells and their closely related impact on cervical carcinogenesis.

scholarly journals Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 318 ◽  
Author(s):  
Jiang Xie ◽  
Jiamin Sun ◽  
Jiatai Feng ◽  
Fuzhang Yang ◽  
Jiao Wang ◽  
...  
Keyword(s):  
Biological Networks ◽  
Cortical Neurons ◽  
Neural Differentiation ◽  
Primary Brain Tumor ◽  
Regulatory Genes ◽  
Functional Enrichment ◽  
Steiner Tree Problem ◽  
Literature Mining ◽  
Driver Genes ◽  
Kegg Pathways

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

CAN COMPLEX CELLULAR PROCESSES BE GOVERNED BY SIMPLE LINEAR RULES?

2009 ◽  
Vol 07 (01) ◽  
pp. 243-268 ◽  
Author(s):  
KUMAR SELVARAJOO ◽  
MASARU TOMITA ◽  
MASA TSUCHIYA
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Living Systems ◽  
Regulatory Motifs ◽  
Cellular Processes ◽  
Self Organized ◽  
Wide Range ◽  
Activation Response ◽  
Physical Reasons

Complex living systems have shown remarkably well-orchestrated, self-organized, robust, and stable behavior under a wide range of perturbations. However, despite the recent generation of high-throughput experimental datasets, basic cellular processes such as division, differentiation, and apoptosis still remain elusive. One of the key reasons is the lack of understanding of the governing principles of complex living systems. Here, we have reviewed the success of perturbation–response approaches, where without the requirement of detailed in vivo physiological parameters, the analysis of temporal concentration or activation response unravels biological network features such as causal relationships of reactant species, regulatory motifs, etc. Our review shows that simple linear rules govern the response behavior of biological networks in an ensemble of cells. It is daunting to know why such simplicity could hold in a complex heterogeneous environment. Provided physical reasons can be explained for these phenomena, major advancement in the understanding of basic cellular processes could be achieved.

scholarly journals Copy number networks to guide combinatorial therapy for cancer and other disorders

2014 ◽  
Author(s):  
Andy Lin ◽  
Desmond James Smith
Keyword(s):  
Drug Treatment ◽  
Protein Interactions ◽  
Copy Number ◽  
Genetic Interaction ◽  
Single Gene ◽  
Growth Factor Receptor ◽  
Molecular Networks ◽  
Protein Protein Interactions ◽  
Gene Pairs ◽  
Cancer Networks

The dwindling drug pipeline is driving increased interest in the use of genome datasets to inform drug treatment. In particular, networks based on transcript data and protein-protein interactions have been used to design therapies that employ drug combinations. But there has been less focus on employing human genetic interaction networks constructed from copy number alterations (CNAs). These networks can be charted with sensitivity and precision by seeking gene pairs that tend to be amplified and/or deleted in tandem, even when they are located at a distance on the genome. Our experience with radiation hybrid (RH) panels, a library of cell clones that have been used for genetic mapping, have shown this tool can pinpoint statistically significant patterns of co-inherited gene pairs. In fact, we were able to identify gene pairs specifically associated with the mechanism of cell survival at single gene resolution. The strategy of seeking correlated CNAs can also be used to map survival networks for cancer. Although the cancer networks have lower resolution, the RH network can be leveraged to provide single gene specificity in the tumor networks. In a survival network for glioblastoma possessing single gene resolution, we found that the epidermal growth factor receptor (EGFR) oncogene interacted with 46 genes. Of these genes, ten (22%) happened to be targets for existing drugs. Here, we briefly review the previous use of molecular networks to design novel therapies. We then highlight the potential of using correlated CNAs to guide combinatorial drug treatment in common medical conditions. We focus on therapeutic opportunities in cancer, but also offer examples from autoimmune disorders and atherosclerosis.

scholarly journals Robustness and lethality in multilayer biological molecular networks

2019 ◽  
Author(s):  
Xueming Liu ◽  
Enrico Maiorino ◽  
Arda Halu ◽  
Joseph Loscalzo ◽  
Jianxi Gao ◽  
...  
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Metabolic Diseases ◽  
Biological Systems ◽  
Interaction Network ◽  
Network Models ◽  
Global Network ◽  
Molecular Networks ◽  
Cancer Genes ◽  
Gene Regulatory

AbstractRobustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cell’s functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers. These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

scholarly journals Generalizing RNA velocity to transient cell states through dynamical modeling

2019 ◽  
Author(s):  
Volker Bergen ◽  
Marius Lange ◽  
Stefan Peidli ◽  
F. Alexander Wolf ◽  
Fabian J. Theis
Keyword(s):  
Steady State ◽  
Cell Fate ◽  
Single Cells ◽  
Internal Clock ◽  
Mrna Levels ◽  
Driver Genes ◽  
Cellular Processes ◽  
Regulatory Changes ◽  
Transcriptional Dynamics ◽  
Latent Time

AbstractThe introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cell’s internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification.

Network Analysis as a Grand Unifier in Biomedical Data Science

2018 ◽  
Vol 1 (1) ◽  
pp. 153-180 ◽  
Author(s):  
Patrick McGillivray ◽  
Declan Clarke ◽  
William Meyerson ◽  
Jing Zhang ◽  
Donghoon Lee ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Data Science ◽  
Molecular Networks ◽  
Biomedical Data ◽  
Molecular Context ◽  
Gene Regulatory ◽  
Global Statistics ◽  
Key Aspects

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

scholarly journals MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome–Host Interactions

Cells ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1278
Author(s):  
Tahila Andrighetti ◽  
Balazs Bohar ◽  
Ney Lemke ◽  
Padhmanand Sudhakar ◽  
Tamas Korcsmaros
Keyword(s):  
Biological Significance ◽  
Molecular Networks ◽  
Computational Pipeline ◽  
Host Proteins ◽  
Disease Pathogenesis ◽  
Host Interactions ◽  
Cellular Processes ◽  
Downstream Effects ◽  
Host Genes

Microbiome–host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe–host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions—as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn’s disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

scholarly journals Evidence of Rentian Scaling of Functional Modules in Diverse Biological Networks

2018 ◽  
Vol 30 (8) ◽  
pp. 2210-2244
Author(s):  
Javier J. How ◽  
Saket Navlakha
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Broad Class ◽  
Graph Model ◽  
Cell Types ◽  
Mouse Cell ◽  
Molecular Networks ◽  
Functional Modules ◽  
Gene Regulatory ◽  
External Connections

Biological networks have long been known to be modular, containing sets of nodes that are highly connected internally. Less emphasis, however, has been placed on understanding how intermodule connections are distributed within a network. Here, we borrow ideas from engineered circuit design and study Rentian scaling, which states that the number of external connections between nodes in different modules is related to the number of nodes inside the modules by a power-law relationship. We tested this property in a broad class of molecular networks, including protein interaction networks for six species and gene regulatory networks for 41 human and 25 mouse cell types. Using evolutionarily defined modules corresponding to known biological processes in the cell, we found that all networks displayed Rentian scaling with a broad range of exponents. We also found evidence for Rentian scaling in functional modules in the Caenorhabditis elegans neural network, but, interestingly, not in three different social networks, suggesting that this property does not inevitably emerge. To understand how such scaling may have arisen evolutionarily, we derived a new graph model that can generate Rentian networks given a target Rent exponent and a module decomposition as inputs. Overall, our work uncovers a new principle shared by engineered circuits and biological networks.

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