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22 pages, 4266 KiB

Open AccessArticle

In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes

by Guillem Santamaria, Paula Ruiz-Rodriguez, Chantal Renau-Mínguez, Francisco R. Pinto and Mireia Coscollá

Biomolecules 2022, 12(3), 376; https://doi.org/10.3390/biom12030376 - 28 Feb 2022

Viewed by 2382

Abstract

Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. [...] Read more.

Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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17 pages, 2849 KiB

Open AccessArticle

How Far Are We from the Completion of the Human Protein Interactome Reconstruction?

by Georgios N. Dimitrakopoulos, Maria I. Klapa and Nicholas K. Moschonas

Biomolecules 2022, 12(1), 140; https://doi.org/10.3390/biom12010140 - 15 Jan 2022

Cited by 11 | Viewed by 2987

Abstract

After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights [...] Read more.

After more than fifteen years from the first high-throughput experiments for human protein–protein interaction (PPI) detection, we are still wondering how close the completion of the genome-scale human PPI network reconstruction is, what needs to be further explored and whether the biological insights gained from the holistic investigation of the current network are valid and useful. The unique structure of PICKLE, a meta-database of the human experimentally determined direct PPI network developed by our group, presently covering ~80% of the UniProtKB/Swiss-Prot reviewed human complete proteome, enables the evaluation of the interactome expansion by comparing the successive PICKLE releases since 2013. We observe a gradual overall increase of 39%, 182%, and 67% in protein nodes, PPIs, and supporting references, respectively. Our results indicate that, in recent years, (a) the PPI addition rate has decreased, (b) the new PPIs are largely determined by high-throughput experiments and mainly concern existing protein nodes and (c), as we had predicted earlier, most of the newly added protein nodes have a low degree. These observations, combined with a largely overlapping k-core between PICKLE releases and a network density increase, imply that an almost complete picture of a structurally defined network has been reached. The comparative unsupervised application of two clustering algorithms indicated that exploring the full interactome topology can reveal the protein neighborhoods involved in closely related biological processes as transcriptional regulation, cell signaling and multiprotein complexes such as the connexon complex associated with cancers. A well-reconstructed human protein interactome is a powerful tool in network biology and medicine research forming the basis for multi-omic and dynamic analyses. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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21 pages, 4406 KiB

Open AccessArticle

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

by Suma L. Sivan and Vinod Chandra S. Sukumara Pillai

Biomolecules 2022, 12(1), 37; https://doi.org/10.3390/biom12010037 - 27 Dec 2021

Viewed by 2631

Abstract

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as [...] Read more.

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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20 pages, 3415 KiB

Open AccessEditor’s ChoiceArticle

Emergence and Enhancement of Ultrasensitivity through Posttranslational Modulation of Protein Stability

by Carla M. Kumbale, Eberhard O. Voit and Qiang Zhang

Biomolecules 2021, 11(11), 1741; https://doi.org/10.3390/biom11111741 - 22 Nov 2021

Cited by 2 | Viewed by 1594

Abstract

Signal amplification in biomolecular networks converts a linear input to a steeply sigmoid output and is central to a number of cellular functions including proliferation, differentiation, homeostasis, adaptation, and biological rhythms. One canonical signal amplifying motif is zero-order ultrasensitivity that is mediated through [...] Read more.

Signal amplification in biomolecular networks converts a linear input to a steeply sigmoid output and is central to a number of cellular functions including proliferation, differentiation, homeostasis, adaptation, and biological rhythms. One canonical signal amplifying motif is zero-order ultrasensitivity that is mediated through the posttranslational modification (PTM) cycle of signaling proteins. The functionality of this signaling motif has been examined conventionally by supposing that the total amount of the protein substrates remains constant, as by the classical Koshland–Goldbeter model. However, covalent modification of signaling proteins often results in changes in their stability, which affects the abundance of the protein substrates. Here, we use mathematical models to explore the signal amplification properties in such scenarios and report some novel aspects. Our analyses indicate that PTM-induced protein stabilization brings the enzymes closer to saturation. As a result, ultrasensitivity may emerge or is greatly enhanced, with a steeper sigmoidal response, higher magnitude, and generally longer response time. In cases where PTM destabilizes the protein, ultrasensitivity can be regained through changes in the activities of the involved enzymes or from increased protein synthesis. Importantly, ultrasensitivity is not limited to modified or unmodified protein substrates—when protein turnover is considered, the total free protein substrate can also exhibit ultrasensitivity under several conditions. When full enzymatic reactions are used instead of Michaelis–Menten kinetics for the modeling, the total free protein substrate can even exhibit nonmonotonic dose–response patterns. It is conceivable that cells use inducible protein stabilization as a strategy in the signaling network to boost signal amplification while saving energy by keeping the protein substrate levels low at basal conditions. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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20 pages, 1782 KiB

Open AccessArticle

Therapeutic Approach of KRAS Mutant Tumours by the Combination of Pharmacologic Ascorbate and Chloroquine

by Orsolya Kapuy, Kinga Makk-Merczel and András Szarka

Biomolecules 2021, 11(5), 652; https://doi.org/10.3390/biom11050652 - 28 Apr 2021

Cited by 7 | Viewed by 2163

Abstract

The Warburg effect has been considered a potential therapeutic target to fight against cancer progression. In KRAS mutant cells, PKM2 (pyruvate kinase isozyme M2) is hyper-activated, and it induces GLUT1 expression; therefore, KRAS has been closely involved in the initiation of Warburg metabolism. [...] Read more.

The Warburg effect has been considered a potential therapeutic target to fight against cancer progression. In KRAS mutant cells, PKM2 (pyruvate kinase isozyme M2) is hyper-activated, and it induces GLUT1 expression; therefore, KRAS has been closely involved in the initiation of Warburg metabolism. Although mTOR (mammalian target of rapamycin), a well-known inhibitor of autophagy-dependent survival in physiological conditions, is also activated in KRAS mutants, many recent studies have revealed that autophagy becomes hyper-active in KRAS mutant cancer cells. In the present study, a mathematical model was built containing the main elements of the regulatory network in KRAS mutant cancer cells to explore the further possible therapeutic strategies. Our dynamical analysis suggests that the downregulation of KRAS, mTOR and autophagy are crucial in anti-cancer therapy. PKM2 has been assumed to be the key switch in the stress response mechanism. We predicted that the addition of both pharmacologic ascorbate and chloroquine is able to block both KRAS and mTOR pathways: in this case, no GLUT1 expression is observed, meanwhile autophagy, essential for KRAS mutant cancer cells, is blocked. Corresponding to our system biological analysis, this combined pharmacologic ascorbate and chloroquine treatment in KRAS mutant cancers might be a therapeutic approach in anti-cancer therapies. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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17 pages, 6330 KiB

Open AccessArticle

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network

by Namgil Lee, Hojin Yoo and Heejung Yang

Biomolecules 2021, 11(4), 546; https://doi.org/10.3390/biom11040546 - 08 Apr 2021

Cited by 7 | Viewed by 2568

Abstract

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions [...] Read more.

Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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24 pages, 10891 KiB

Open AccessArticle

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines

by Ewelina Weglarz-Tomczak, Thierry D. G. A. Mondeel, Diewertje G. E. Piebes and Hans V. Westerhoff

Biomolecules 2021, 11(4), 490; https://doi.org/10.3390/biom11040490 - 24 Mar 2021

Cited by 9 | Viewed by 3357

Abstract

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as [...] Read more.

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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18 pages, 30003 KiB

Open AccessArticle

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

by Daniele Mercatelli, Nicola Balboni, Alessandro Palma, Emanuela Aleo, Pietro Paolo Sanna, Giovanni Perini and Federico Manuel Giorgi

Biomolecules 2021, 11(2), 177; https://doi.org/10.3390/biom11020177 - 28 Jan 2021

Cited by 10 | Viewed by 4954

Abstract

Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. [...] Read more.

Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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14 pages, 3949 KiB

Open AccessArticle

Molecular Dynamics Simulations Predict that rSNP Located in the HNF-1α Gene Promotor Region Linked with MODY3 and Hepatocellular Carcinoma Promotes Stronger Binding of the HNF-4α Transcription Factor

by Eva Španinger, Uroš Potočnik and Urban Bren

Biomolecules 2020, 10(12), 1700; https://doi.org/10.3390/biom10121700 - 21 Dec 2020

Cited by 5 | Viewed by 2376

Abstract

Our study aims to investigate the impact of the Maturity-onset diabetes of the young 3 disease-linked rSNP rs35126805 located in the HNF-1α gene promotor on the binding of the transcription factor HNF-4α and consequently on the regulation of HNF-1α gene expression. Our focus [...] Read more.

Our study aims to investigate the impact of the Maturity-onset diabetes of the young 3 disease-linked rSNP rs35126805 located in the HNF-1α gene promotor on the binding of the transcription factor HNF-4α and consequently on the regulation of HNF-1α gene expression. Our focus is to calculate the change in the binding affinity of the transcription factor HNF-4α to the DNA, caused by the regulatory single nucleotide polymorphism (rSNP) through molecular dynamics simulations and thermodynamic analysis of acquired results. Both root-mean-square difference (RMSD) and the relative binding free energy ΔΔG_bind reveal that the HNF-4α binds slightly more strongly to the DNA containing the mutation (rSNP) making the complex more stable/rigid, and thereby influencing the expression of the HNF-1α gene. The resulting disruption of the HNF-4α/HNF-1α pathway is also linked to hepatocellular carcinoma metastasis and enhanced apoptosis in pancreatic cancer cells. To the best of our knowledge, this represents the first study where thermodynamic analysis of the results obtained from molecular dynamics simulations is performed to uncover the influence of rSNP on the protein binding to DNA. Therefore, our approach can be generally applied for studying the impact of regulatory single nucleotide polymorphisms on the binding of transcription factors to the DNA. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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Review

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12 pages, 833 KiB

Open AccessReview

Network Approaches to Study Endogenous RNA Competition and Its Impact on Tissue-Specific microRNA Functions

by Tânia Monteiro Marques and Margarida Gama-Carvalho

Biomolecules 2022, 12(2), 332; https://doi.org/10.3390/biom12020332 - 19 Feb 2022

Cited by 9 | Viewed by 2346

Abstract

microRNAs are small non-coding RNAs that play a key role in regulating gene expression. These molecules exert their function through sequence complementarity with microRNA responsive elements and are typically located in the 3′ untranslated region of mRNAs, negatively regulating expression. Even though the [...] Read more.

microRNAs are small non-coding RNAs that play a key role in regulating gene expression. These molecules exert their function through sequence complementarity with microRNA responsive elements and are typically located in the 3′ untranslated region of mRNAs, negatively regulating expression. Even though the relevant role of miRNA-dependent regulation is broadly recognized, the principles governing their ability to lead to specific functional outcomes in distinct cell types are still not well understood. In recent years, an intriguing hypothesis proposed that miRNA-responsive elements act as communication links between different RNA species, making the investigation of microRNA function even more complex than previously thought. The competing endogenous RNA hypothesis suggests the presence of a new level of regulation, whereby a specific RNA transcript can indirectly influence the abundance of other transcripts by limiting the availability of a common miRNA, acting as a “molecular sponge”. Since this idea has been proposed, several studies have tried to pinpoint the interaction networks that have been established between different RNA species and whether they contribute to normal cell function and disease. The focus of this review is to highlight recent developments and achievements made towards the process of characterizing competing endogenous RNA networks and their role in cellular function. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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18 pages, 727 KiB

Open AccessEditor’s ChoiceReview

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts

by Prashant Srivastava, Saptarshi Bej, Kristina Yordanova and Olaf Wolkenhauer

Biomolecules 2021, 11(11), 1591; https://doi.org/10.3390/biom11111591 - 27 Oct 2021

Cited by 11 | Viewed by 2762

Abstract

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can [...] Read more.

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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12 pages, 624 KiB

Open AccessReview

Text Mining for Building Biomedical Networks Using Cancer as a Case Study

by Sofia I. R. Conceição and Francisco M. Couto

Biomolecules 2021, 11(10), 1430; https://doi.org/10.3390/biom11101430 - 29 Sep 2021

Cited by 7 | Viewed by 2841

Abstract

In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the [...] Read more.

In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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41 pages, 593 KiB

Open AccessFeature PaperReview

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

by Fotis A. Baltoumas, Sofia Zafeiropoulou, Evangelos Karatzas, Mikaela Koutrouli, Foteini Thanati, Kleanthi Voutsadaki, Maria Gkonta, Joana Hotova, Ioannis Kasionis, Pantelis Hatzis and Georgios A. Pavlopoulos

Biomolecules 2021, 11(8), 1245; https://doi.org/10.3390/biom11081245 - 20 Aug 2021

Cited by 14 | Viewed by 5097

Abstract

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, [...] Read more.

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology. Full article

(This article belongs to the Special Issue Computational Approaches for the Study of Biomolecular Networks)

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