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Research on System Biology Methods and Tools for Integrating Omics Data

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (20 June 2026) | Viewed by 5778

Editor

Dr. Yuanyan Xiong

E-Mail Website
Guest Editor
School of Life Sciences, Sun Yat-sen University, Guangzhou, China
Interests: bioinformatics; high-throughput sequencing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of high-throughput omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, has generated increasingly complex and high-dimensional biological datasets. Traditional analytical approaches are often insufficient to uncover the full biological meaning hidden within these layers. Systems biology offers a comprehensive and integrative framework for analyzing multi-omics data, enabling researchers to model biological systems, elucidate regulatory networks, and explore disease mechanisms from a holistic perspective.

This Special Issue focuses on innovative systems biology methodologies and computational tools designed to integrate diverse omics datasets. We will highlight recent advances in integrative data analysis, network-based approaches, dynamic system modeling, and artificial intelligence-driven techniques that facilitate identifying biomarkers, therapeutic targets, and mechanistic understanding in complex biological systems.

We welcome submissions of original research articles, reviews, and software/tool descriptions that contribute to advancing multi-omics data integration, particularly in the contexts of biomedical research, biotechnology, and precision medicine.

Dr. Yuanyan Xiong
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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
  • multi-omics integration
  • network analysis
  • computational biology
  • bioinformatics tools
  • machine learning
  • precision medicine

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Published Papers (3 papers)

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Research

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35 pages, 4283 KB  
Article
Confounder-Adjusted Differentiation of Colorectal Cancer via Dynamic Propagation of Pathway Influence
by Larissa Margareta Batrancea, Ömer Akgüller, Mehmet Ali Balcı, Gizem Çalıbaşı Koçal and Lucian Gaban
Int. J. Mol. Sci. 2025, 26(20), 10023; https://doi.org/10.3390/ijms262010023 - 15 Oct 2025
Cited by 1 | Viewed by 975
Abstract
Colorectal cancer (CRC) exhibits profound molecular heterogeneity between left-sided and right-sided tumors with distinct therapeutic responses that current static genomic analyses incompletely explain. We developed Dynamic Functional Influence Computation (DynaFIC), a computational framework modeling time-resolved signal propagation through biological networks to quantify functional [...] Read more.
Colorectal cancer (CRC) exhibits profound molecular heterogeneity between left-sided and right-sided tumors with distinct therapeutic responses that current static genomic analyses incompletely explain. We developed Dynamic Functional Influence Computation (DynaFIC), a computational framework modeling time-resolved signal propagation through biological networks to quantify functional influence beyond static expression levels. Using the GSE39582 dataset comprising 583 primary CRC samples, we performed confounder-adjusted differential expression analysis controlling for microsatellite instability status, BRAF mutations, Tumor Node Metastasis (TNM) stage, age, and sex, identifying 105 laterality-associated genes that underwent DynaFIC temporal network analysis. Right-sided tumors exhibited dramatically higher network connectivity density despite fewer nodes, creating distributed vulnerability patterns with HOXC6 as the dominant regulator, achieving 200-fold influence through network amplification. Left-sided tumors showed compartmentalized, hierarchical organization with PRAC1 as the primary regulator and predictable expression-influence scaling. Temporal clustering revealed distinct propagation kinetics: right-sided tumors demonstrated rapid signal saturation requiring early intervention, while left-sided tumors exhibited sustained propagation permitting sequential approaches. Stability Volatility Index analysis showed right-sided tumors maintain significantly higher systemic vulnerability. These findings establish anatomical location as a fundamental network organizational principle, suggesting that incorporating temporal dynamics into cancer analysis reveals therapeutically relevant differences for precision medicine applications in colorectal cancer. Full article
(This article belongs to the Special Issue Research on System Biology Methods and Tools for Integrating Omics Data)
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Review

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28 pages, 1918 KB  
Review
Fifteen Years of the Genome Analysis Toolkit as the De Facto Standard in Short-Read Variant Calling
by Asta Blazyte, Long Le, Jaesuk Lee, Delger Bayarsaikhan and Bonghee Lee
Int. J. Mol. Sci. 2026, 27(9), 3754; https://doi.org/10.3390/ijms27093754 - 23 Apr 2026
Viewed by 329
Abstract
Genome Analysis Toolkit (GATK) is a rigorously maintained collection of 430 analysis tools and a core bioinformatics engine. First released in 2010 as a toolkit for next-generation sequencing (NGS) data analysis, GATK remains one of the least celebrated yet foundational tools of the [...] Read more.
Genome Analysis Toolkit (GATK) is a rigorously maintained collection of 430 analysis tools and a core bioinformatics engine. First released in 2010 as a toolkit for next-generation sequencing (NGS) data analysis, GATK remains one of the least celebrated yet foundational tools of the NGS era. By employing state-of-the-art approaches and continuously adapting to the evolving demands of NGS analysis, it has effectively unified the variant calling process worldwide. In a field as rapidly evolving as genomics, it is remarkable that, over a decade later, the same toolkit remains the gold standard. This critical review explores the pre-history of GATK, the reasons for its broad and enduring adoption by the scientific community, its developmental evolution, contributions to science, and future prospects. Full article
(This article belongs to the Special Issue Research on System Biology Methods and Tools for Integrating Omics Data)
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52 pages, 1709 KB  
Review
The Endocannabinoid–Microbiota–Neuroimmune Super-System: A Unifying Feedback Architecture for Systems Resilience, Collapse Trajectories, and Precision Feedback Medicine
by Cătălin Aliuș, Alexandru Breazu, Cosmin Pantu, Corneliu Toader, Matei Șerban, Răzvan-Adrian Covache-Busuioc, Octavian Munteanu and Adrian Vasile Dumitru
Int. J. Mol. Sci. 2025, 26(22), 10959; https://doi.org/10.3390/ijms262210959 - 12 Nov 2025
Cited by 6 | Viewed by 3957
Abstract
Modern biomedicine frequently contextualizes disease around isolated molecular or organ-specific mechanisms, but numerous chronic diseases, including Alzheimer’s disease, multiple sclerosis, depression, diabetes, and sepsis, share common trajectories of systemic destabilization. An increasing body of evidence indicates that health is not a property of [...] Read more.
Modern biomedicine frequently contextualizes disease around isolated molecular or organ-specific mechanisms, but numerous chronic diseases, including Alzheimer’s disease, multiple sclerosis, depression, diabetes, and sepsis, share common trajectories of systemic destabilization. An increasing body of evidence indicates that health is not a property of single organs but the emergent property of interdependent feedback networks linking the microbiome, endocannabinoidome, neuroimmune system, and metabolic regulators. We propose the Endocannabinoid–Microbiota–Neuroimmune Super-System (EMN-S) as an evolutionarily conserved conceptual model that describes how these fields of influence reciprocally interact through feedback control. The microbial communities constituting the EMN-S encode environmental and dietary inputs, endocannabinoid signaling serves as an integrative regulator that synchronizes neural and immune activity, and neuroimmune circuits effectuate adaptive behaviors that alter microbiotal and lipid ecosystems. This review formalizes the EMN-S, contending that it is a unitary and cohesive model of physiological resilience, as well as offering a framework for precision feedback therapeutics. We describe how three mechanisms—encoder drift, integrator detuning, and executor overutilization—convert stabilizing negative feedback into runaway feedback cascades that underlie chronic, recurrent, and multisystemic disease. We then specify the EMN-S signature—integrated microbiome, lipidomic, and immune readouts—as an early indicator of resilience collapse and prospective preclinical state. Finally, we recapitulate the potential of AI-driven digital twins to illuminate feedback collapse, predict tipping points, and direct closed-loop intervention and treatments to restore dynamic equilibrium. By anchoring complexity in concrete and measurable feedback principles, the EMN-S shifts focus to investigate pathophysiology as opposed to reductionist lesion models of systemic derangements and embraces a systemic, empirically testable theory of stability. Full article
(This article belongs to the Special Issue Research on System Biology Methods and Tools for Integrating Omics Data)
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