Special Issue "Computational Approaches for the Study of Biomolecular Networks"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Dr. Francisco Rodrigues Pinto
Website
Guest Editor
BioISI (Biosystems and Integrative Sciences Institute), Faculty of Sciences, University of Lisbon, Lisbon, Portugal
Interests: computational biology; network biology; disease gene prediction; data science; mathematical modeling; gene expression regulation; systems biology
Dr. Javier De Las Rivas
Website
Guest Editor
Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) University of Salamanca (USAL), and Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
Interests: bioinformatics; computational biology; functional genomics; cancer; human gene; cancer gene; genomic medicine; transcriptomics; proteomics; protein interactions; interactome; network biology; data science; artificial intelligence
Special Issues and Collections in MDPI journals

Special Issue Information

Living organisms are complex systems composed of large numbers of interacting biomolecules. Although there is vast accumulated knowledge about the properties and functions of most of these biomolecules, it is still challenging to predict the behavior of complete organisms or cells in response to environmental or genetic perturbations. To improve our ability to predict and understand how biological systems function, we need to better understand the patterns of interaction between biomolecules.

To fill this knowledge gap, there have been substantial efforts to discover, characterize, and share information about biomolecular interactions, such as protein–protein, protein–DNA, protein–RNA, or miRNA–mRNA interactions, many of them with signaling and regulatory roles. Enumerating these interactions is not enough to gain new insights into the complex behavior of biological systems. Due to the large number of biomolecules and their interactions, computational methods are needed to build, analyze, and explore these biomolecular networks.

This Special Issue welcomes reports that develop or evaluate computational approaches for the study of biomolecular networks. These approaches can be focused on the analysis or exploration of these networks per se, aim to facilitate the analysis of large-scale omics datasets, or use the knowledge encoded in these networks to gain insights into the molecular determinants of complex phenotypes such as human diseases.

Dr. Francisco Rodrigues Pinto
Dr. Javier De Las Rivas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomolecular networks
  • computational biology
  • network biology
  • network medicine
  • network algorithms
  • network analysis
  • network miming

Published Papers (2 papers)

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Research

Open AccessArticle
Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines
Biomolecules 2021, 11(2), 177; https://doi.org/10.3390/biom11020177 - 28 Jan 2021
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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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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
Biomolecules 2020, 10(12), 1700; https://doi.org/10.3390/biom10121700 - 21 Dec 2020
Viewed by 505
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 ΔΔGbind 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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