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From Molecular to Systems Biology through Data Integration

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 April 2025) | Viewed by 600

Special Issue Editor


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Guest Editor
1. Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
2. Department of Laboratory Medicine, ASST "Grande Ospedale Metropolitano" Niguarda, 20162 Milan, Italy
Interests: computational modeling of biological networks; data science; omics; personalized drug responses; artificial intelligence; systems biology; ethics of AI

Special Issue Information

Dear Colleagues,

Omics-based approaches used to identify disease mechanisms and drug responses have produced massive amounts of data, now accessible through indexed journals, reviews, and public and navigable repositories, thanks to technological advancements.

In this context, systems biology has developed into a discipline that uses complex computational and mathematical models to investigate the interactions between various biological entities (proteins, metabolites, genes, cells, organs, tissues, and the environment). In this scenario, we can reveal phenotype as a reflection of the several simultaneous molecular interactions occurring at any one time from various levels, combined holistically.

Current research is limited to studying the molecules themselves while ignoring the effects of their interactions with the environment. Therefore, systems biology contrasts the standard concept of the reductionist approach, using the study of dysregulation in isolated molecular components as a starting point to identify distinct changes in the patterns of intermolecular relationships.

The time is ripe, and thus we must reap the benefits of integrating molecular data with the repositories present in the literature (es interactome, pathways or network analysis) and in clinical practice data. This Special Issue is an exciting opportunity to shed light on the importance of data integration in systems biology to better support the discipline of precision medicine. Original papers, review articles, and protocols from experts in the field are welcome.

Dr. Elvira Inglese
Guest Editor

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Keywords

  • bioinformatic methods in systems biology

  • data-driven and dynamic modeling approaches
  • network analysis
  • data integration methods
  • digital twin
  • omics-based research
  • interactome analysis
  • network pharmacology

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Published Papers (1 paper)

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Research

15 pages, 1027 KiB  
Article
Enhanced Outer Membrane Vesicle Production in Escherichia coli: From Metabolic Network Model to Designed Strain Lipidomic Profile
by Héctor Alejandro Ruiz-Moreno, Juan D. Valderrama-Rincon, Mónica P. Cala, Miguel Fernández-Niño, Mateo Valderruten Cajiao, María Francisca Villegas-Torres and Andrés Fernando González Barrios
Int. J. Mol. Sci. 2025, 26(14), 6714; https://doi.org/10.3390/ijms26146714 - 13 Jul 2025
Viewed by 184
Abstract
Bacterial structures formed from the outer membrane and the periplasm components carry biomolecules to expel cellular material and interact with other cells. These outer membrane vesicles (OMVs) can encapsulate bioactive content, which confers OMVs with high potential as alternative drug delivery vehicles or [...] Read more.
Bacterial structures formed from the outer membrane and the periplasm components carry biomolecules to expel cellular material and interact with other cells. These outer membrane vesicles (OMVs) can encapsulate bioactive content, which confers OMVs with high potential as alternative drug delivery vehicles or as a platform for novel vaccine development. Single-gene mutants derived from Escherichia coli JC8031 were engineered to further enhance OMV production based on metabolic network modelling and in silico gene knockout design (ΔpoxB, ΔsgbE, ΔgmhA, and ΔallD). Mutants were experimentally obtained by genome editing using CRISPR-Cas9 and tested for OMVs recovery observing an enhanced OMV production in all of them. Lipidomic analysis through LC-ESI-QTOF-MS was performed for OMVs obtained from each engineered strain and compared to the wild-type E. coli JC8031 strain. The lipid profile of OMVs from the wild-type E. coli JC8031 did not change significantly confirmed by multivariate statistical analysis when compared to the mutant strains. The obtained results suggest that the vesicle production can be further improved while the obtained vesicles are not altered in their composition, allowing further study for stability and integrity for use in therapeutic settings. Full article
(This article belongs to the Special Issue From Molecular to Systems Biology through Data Integration)
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