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Biomolecules
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Applications of Systems Biology Approaches in Biomedicine
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Applications of Systems Biology Approaches in Biomedicine

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 11447

Special Issue Editors

Dr. Xin Lai

E-Mail Website
Guest Editor
Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
Interests: medical systems biology; bioinformatics; biomathematics; network biology; noncoding RNA
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Le Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Sichuan University, Chengdu, China
Interests: bioinformatics; numerical analysis; high performance computing and data mining

Special Issue Information

Dear Colleagues,

Human diseases such as cancer can be viewed as a system failure, and studying the complexity of a disease requires us to understand its system structure, function, and dynamics. Disease complexity rises from enormous amounts of information on the components (such as genes, RNAs, and proteins) and interactions that comprise a biological system. Advanced mathematical and computational models have played a significant role in improving our understanding of biological systems. Furthermore, translational modeling approaches to a full understanding of the underlying molecular mechanisms of human diseases and their clinical relevance are currently ongoing. However, the systematic integration of multi-omics datasets (such as genomics, transcriptomics, proteomics, and metabolomics) into computational modeling brings about numerous challenges in the field, including utilizing a suitable modeling framework while considering the balance between model complexity and accuracy, developing scalable and reproducible workflows to integrate and analyze biomedical data, inventing efficient methods to calibrate data-driven models, evaluation of models’ robustness and uncertainties, and designing experiments to validate model-driven hypotheses that contribute the knowledge of molecular and cell biology as well as pathophysiology. Therefore, we call for contributions from researchers with diverse backgrounds (e.g., biomathematics and bioinformatics) and practices in computational biology that aim to address some of the mentioned challenges and difficulties in this field, as well as tools, techniques, and application studies.

Dr. Xin Lai
Prof. Dr. Le Zhang
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 submissions that pass pre-check are 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 2700 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

  • systems biology
  • systems medicine
  • network biology
  • mechanistic modeling
  • computational biology
  • bioinformatics
  • biomathematics
  • machine learning

Published Papers (6 papers)

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Research

22 pages, 2751 KiB  
Article
The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations
by Hongzhu Cui, Suhas Srinivasan, Ziyang Gao and Dmitry Korkin
Biomolecules 2024, 14(1), 40; https://doi.org/10.3390/biom14010040 - 27 Dec 2023
Viewed by 910
Abstract
Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can [...] Read more.
Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new “edgotype” concept has been proposed, which emphasizes the interaction-specific, “edgetic”, perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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18 pages, 2936 KiB  
Article
SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes
by Kevin Meng-Lin, Choong-Yong Ung, Cheng Zhang, Taylor M. Weiskittel, Philip Wisniewski, Zhuofei Zhang, Shyang-Hong Tan, Kok-Siong Yeo, Shizhen Zhu, Cristina Correia and Hu Li
Biomolecules 2023, 13(6), 895; https://doi.org/10.3390/biom13060895 - 27 May 2023
Cited by 2 | Viewed by 2849
Abstract
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of [...] Read more.
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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16 pages, 3641 KiB  
Article
A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis
by Ming Xiao, Fubo Ma, Jun Yu, Jianghang Xie, Qiaozhen Zhang, Peng Liu, Fei Yu, Yuming Jiang and Le Zhang
Biomolecules 2023, 13(1), 63; https://doi.org/10.3390/biom13010063 - 28 Dec 2022
Cited by 1 | Viewed by 1274
Abstract
It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation [...] Read more.
It is very important to compute the mutation spectra, and simulate the intra-host mutation processes by sequencing data, which is not only for the understanding of SARS-CoV-2 genetic mechanism, but also for epidemic prediction, vaccine, and drug design. However, the current intra-host mutation analysis algorithms are not only inaccurate, but also the simulation methods are unable to quickly and precisely predict new SARS-CoV-2 variants generated from the accumulation of mutations. Therefore, this study proposes a novel accurate strand-specific SARS-CoV-2 intra-host mutation spectra computation method, develops an efficient and fast SARS-CoV-2 intra-host mutation simulation method based on mutation spectra, and establishes an online analysis and visualization platform. Our main results include: (1) There is a significant variability in the SARS-CoV-2 intra-host mutation spectra across different lineages, with the major mutations from G- > A, G- > C, G- > U on the positive-sense strand and C- > U, C- > G, C- > A on the negative-sense strand; (2) our mutation simulation reveals the simulation sequence starts to deviate from the base content percentage of Alpha-CoV/Delta-CoV after approximately 620 mutation steps; (3) 2019-NCSS provides an easy-to-use and visualized online platform for SARS-Cov-2 online analysis and mutation simulation. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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19 pages, 2969 KiB  
Article
A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay
by Dhruba Tara Maharjan, Weichen Song, Zhe Liu, Weidi Wang, Wenxiang Cai, Jue Chen, Fei Xu, Weihai Ying and Guan Ning Lin
Biomolecules 2022, 12(11), 1643; https://doi.org/10.3390/biom12111643 - 06 Nov 2022
Viewed by 1743
Abstract
Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not [...] Read more.
Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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11 pages, 1870 KiB  
Article
COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data
by Seungbyn Baek, Sunmo Yang and Insuk Lee
Biomolecules 2022, 12(10), 1446; https://doi.org/10.3390/biom12101446 - 09 Oct 2022
Viewed by 1517
Abstract
Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from [...] Read more.
Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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18 pages, 2826 KiB  
Article
Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
by Guohua Huang, Wei Luo, Guiyang Zhang, Peijie Zheng, Yuhua Yao, Jianyi Lyu, Yuewu Liu and Dong-Qing Wei
Biomolecules 2022, 12(7), 995; https://doi.org/10.3390/biom12070995 - 17 Jul 2022
Cited by 5 | Viewed by 2266
Abstract
Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory [...] Read more.
Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers. Full article
(This article belongs to the Special Issue Applications of Systems Biology Approaches in Biomedicine)
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