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Biology
Special Issues
Network Analysis for Biology and Precision Medicine
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Special Issue "Network Analysis for Biology and Precision Medicine"

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 5427

Special Issue Editors

Prof. Dr. Lorenzo Farina

E-Mail Website
Guest Editor
Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: bioinformatics; systems biology; computational biology; network biology; network medicine
Dr. Manuela Petti

E-Mail Website
Guest Editor
Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: network medicine; computational biology; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Concetta Palumbo

E-Mail Website
Guest Editor
Institute for Applied Mathematics, National Research Council, Rome, Italy
Interests: mathematical modelling; computational modelling; system biology; simulation; metabolomics; data science

Special Issue Information

Dear Colleagues,

Until recently, the investigation of disease etiology, diagnosis, and treatment was based on a conventional reductionist approach. This tenet argues that critical biological factors work in a simple linear mechanism to control disease pathobiology. Rather, they are nearly always the result of multiple pathobiological pathways that interact through an interconnected network: a disease is rarely a direct consequence of an abnormality in a single gene or molecular component. A large body of evidence that is now emerging from new genomic technologies points out directly to the cause of disease as “perturbations” of biological networks. There is an urgent need for integrated network-based algorithms and machine learning methods to reconcile biological network representation and large-scale data integration.

Prof. Dr. Lorenzo Farina
Dr. Manuela Petti
Dr. Maria Concetta Palumbo
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. Biology 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 2200 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

  • network biology
  • network medicine
  • network analysis
  • omics data analysis
  • precision medicine
  • complex system

Published Papers (3 papers)

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Article
Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach
by
Pilar Ortiz-Vilchis
,
Jazmin-Susana De-la-Cruz-García
and
Aldo Ramirez-Arellano
Biology 2023, 12(1), 140; https://doi.org/10.3390/biology12010140 - 16 Jan 2023
Viewed by 897
Abstract
Protein–protein interactions (PPIs) are the basis for understanding most cellular events in biological systems. Several experimental methods, e.g., biochemical, molecular, and genetic methods, have been used to identify protein–protein associations. However, some of them, such as mass spectrometry, are time-consuming and expensive. Machine [...] Read more.
Protein–protein interactions (PPIs) are the basis for understanding most cellular events in biological systems. Several experimental methods, e.g., biochemical, molecular, and genetic methods, have been used to identify protein–protein associations. However, some of them, such as mass spectrometry, are time-consuming and expensive. Machine learning (ML) techniques have been widely used to characterize PPIs, increasing the number of proteins analyzed simultaneously and optimizing time and resources for identifying and predicting protein–protein functional linkages. Previous ML approaches have focused on well-known networks or specific targets but not on identifying relevant proteins with partial or null knowledge of the interaction networks. The proposed approach aims to generate a relevant protein sequence based on bidirectional Long-Short Term Memory (LSTM) with partial knowledge of interactions. The general framework comprises conducting a scale-free and fractal complex network analysis. The outcome of these analyses is then used to fine-tune the fractal method for the vital protein extraction of PPI networks. The results show that several PPI networks are self-similar or fractal, but that both features cannot coexist. The generated protein sequences (by the bidirectional LSTM) also contain an average of 39.5% of proteins in the original sequence. The average length of the generated sequences was 17% of the original one. Finally, 95% of the generated sequences were true. Full article
(This article belongs to the Special Issue Network Analysis for Biology and Precision Medicine)
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Article
A Boolean Model of the Proliferative Role of the lncRNA XIST in Non-Small Cell Lung Cancer Cells
by
Shantanu Gupta
,
Daner A. Silveira
,
Ronaldo F. Hashimoto
and
Jose Carlos M. Mombach
Biology 2022, 11(4), 480; https://doi.org/10.3390/biology11040480 - 22 Mar 2022
Cited by 3 | Viewed by 1793
Abstract
The long non-coding RNA X inactivate-specific transcript (lncRNA XIST) has been verified as an oncogenic gene in non-small cell lung cancer (NSCLC) whose regulatory role is largely unknown. The important tumor suppressors, microRNAs: miR-449a and miR-16 are regulated by lncRNA XIST in NSCLC, [...] Read more.
The long non-coding RNA X inactivate-specific transcript (lncRNA XIST) has been verified as an oncogenic gene in non-small cell lung cancer (NSCLC) whose regulatory role is largely unknown. The important tumor suppressors, microRNAs: miR-449a and miR-16 are regulated by lncRNA XIST in NSCLC, these miRNAs share numerous common targets and experimental evidence suggests that they synergistically regulate the cell-fate regulation of NSCLC. LncRNA XIST is known to sponge miR-449a and miR-34a, however, the regulatory network connecting all these non-coding RNAs is still unknown. Here we propose a Boolean regulatory network for the G1/S cell cycle checkpoint in NSCLC contemplating the involvement of these non-coding RNAs. Model verification was conducted by comparison with experimental knowledge from NSCLC showing good agreement. The results suggest that miR-449a regulates miR-16 and p21 activity by targeting HDAC1, c-Myc, and the lncRNA XIST. Furthermore, our circuit perturbation simulations show that five circuits are involved in cell fate determination between senescence and apoptosis. The model thus allows pinpointing the direct cell fate mechanisms of NSCLC. Therefore, our results support that lncRNA XIST is an attractive target of drug development in tumor growth and aggressive proliferation of NSCLC, and promising results can be achieved through tumor suppressor miRNAs. Full article
(This article belongs to the Special Issue Network Analysis for Biology and Precision Medicine)
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Article
Control of Cholesterol Metabolism Using a Systems Approach
by
Dorota Formanowicz
,
Marcin Radom
,
Agnieszka Rybarczyk
,
Krzysztof Tanaś
and
Piotr Formanowicz
Biology 2022, 11(3), 430; https://doi.org/10.3390/biology11030430 - 11 Mar 2022
Cited by 4 | Viewed by 1964
Abstract
Cholesterol is an essential component of mammalian cells and is involved in many fundamental physiological processes; hence, its homeostasis in the body is tightly controlled, and any disturbance has serious consequences. Disruption of the cellular metabolism of cholesterol, accompanied by inflammation and oxidative [...] Read more.
Cholesterol is an essential component of mammalian cells and is involved in many fundamental physiological processes; hence, its homeostasis in the body is tightly controlled, and any disturbance has serious consequences. Disruption of the cellular metabolism of cholesterol, accompanied by inflammation and oxidative stress, promotes the formation of atherosclerotic plaques and, consequently, is one of the leading causes of death in the Western world. Therefore, new drugs to regulate disturbed cholesterol metabolism are used and developed, which help to control cholesterol homeostasis but still do not entirely cure atherosclerosis. In this study, a Petri net-based model of human cholesterol metabolism affected by a local inflammation and oxidative stress, has been created and analyzed. The use of knockout of selected pathways allowed us to observe and study the effect of various combinations of commonly used drugs on atherosclerosis. The analysis results led to the conclusion that combination therapy, targeting multiple pathways, may be a fundamental concept in the development of more effective strategies for the treatment and prevention of atherosclerosis. Full article
(This article belongs to the Special Issue Network Analysis for Biology and Precision Medicine)
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