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Cancers
Special Issues
Experimental and Modeling Efforts to Target Metabolism in Cancer
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Experimental and Modeling Efforts to Target Metabolism in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 4607

Special Issue Editors

Dr. Prashant Dogra

E-Mail Website
Guest Editor
Mathematics in Medicine Program, The Houston Methodist Research Institute, HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030, USA
Interests: multiscale modeling; computational medicine; cancer; nanomedicine; immunology
Prof. Dr. Vittorio Cristini

E-Mail Website
Guest Editor
Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
Interests: mathematical oncology; precision medicine; mathematical medicine
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sergio Caserta

E-Mail Website
Guest Editor
1. DICMaPI, Università di Napoli Federico II, P.le V. Tecchio 80, 80125 Napoli, Italy
2. CEINGE Advanced Biotechnologies, Via Gaetano Salvatore, 486, 80131 Napoli, Italy
3. INSTM, Consorzio Interuniversitario Nazionale per la Scienza e Tecnologia dei Materiali, UdR INSTM Napoli Federico II, P. le Tecchio, 80, 80125 Napoli, Italy
Interests: thermodynamics; transport phenomena; chemical engineering; rheology; microstructured systems; multiphase fluids; soft matter; intefaces; active matter; time lapse microscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest advancements and breakthroughs in understanding and targeting the metabolic reprogramming of cancer cells. We welcome contributions on various relevant aspects, including experimental studies elucidating the role of specific metabolic pathways in cancer progression, novel therapeutics and drug delivery strategies targeting cancer metabolism, and mathematical modeling (spanning across various spatiotemporal scales) or artificial intelligence efforts to study metabolic changes in tumors and their effect on treatment outcomes. Additionally, studies that focus on the dynamics of metabolic changes during cancer cell invasion and metastasis are encouraged as well. By bringing together experimental and modeling approaches, this Special Issue aims to provide a comprehensive overview of the current state of the art in cancer metabolism research. Researchers are encouraged to submit their high-quality original research articles or reviews to contribute to this exciting and rapidly evolving area of cancer biology and therapy.

Dr. Prashant Dogra
Prof. Dr. Vittorio Cristini
Dr. Sergio Caserta
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • cancer
  • metabolism
  • drug delivery
  • mathematical modeling
  • artificial intelligence
  • metastasis

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

22 pages, 2905 KiB  
Article
Hybrid Cellular Automata Modeling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth
by Luca Messina, Rosalia Ferraro, Maria J. Peláez, Zhihui Wang, Vittorio Cristini, Prashant Dogra and Sergio Caserta
Cancers 2023, 15(23), 5660; https://doi.org/10.3390/cancers15235660 - 30 Nov 2023
Cited by 4 | Viewed by 1804
Abstract
Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision [...] Read more.
Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses. Methods: Motivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness. The automated hybrid cell emulates critical tumor cell characteristics, including rapid proliferation, heightened motility, reduced cell adhesion, and increased responsiveness to chemotactic signals. This model explores the potential evolution of 3D tumor spheroids by manipulating biological parameters and microenvironment factors, focusing on nutrient availability. Results: Our comprehensive global and local sensitivity analysis reveals that tumor growth primarily depends on cell duplication speed and cell-to-cell adhesion, rather than external chemical gradients. Conversely, tumor invasiveness is predominantly driven by chemotaxis. These insights illuminate tumor development mechanisms, providing vital guidance for effective strategies against tumor progression. Our proposed model is a valuable tool for advancing cancer biology research and exploring potential therapeutic interventions. Full article
(This article belongs to the Special Issue Experimental and Modeling Efforts to Target Metabolism in Cancer)
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14 pages, 2787 KiB  
Article
Exploring Cell Migration Mechanisms in Cancer: From Wound Healing Assays to Cellular Automata Models
by Giorgia Migliaccio, Rosalia Ferraro, Zhihui Wang, Vittorio Cristini, Prashant Dogra and Sergio Caserta
Cancers 2023, 15(21), 5284; https://doi.org/10.3390/cancers15215284 - 3 Nov 2023
Cited by 2 | Viewed by 2431
Abstract
Purpose: Cell migration is a critical driver of metastatic tumor spread, contributing significantly to cancer-related mortality. Yet, our understanding of the underlying mechanisms remains incomplete. Methods: In this study, a wound healing assay was employed to investigate cancer cell migratory behavior, with the [...] Read more.
Purpose: Cell migration is a critical driver of metastatic tumor spread, contributing significantly to cancer-related mortality. Yet, our understanding of the underlying mechanisms remains incomplete. Methods: In this study, a wound healing assay was employed to investigate cancer cell migratory behavior, with the aim of utilizing migration as a biomarker for invasiveness. To gain a comprehensive understanding of this complex system, we developed a computational model based on cellular automata (CA) and rigorously calibrated and validated it using in vitro data, including both tumoral and non-tumoral cell lines. Harnessing this CA-based framework, extensive numerical experiments were conducted and supported by local and global sensitivity analyses in order to identify the key biological parameters governing this process. Results: Our analyses led to the formulation of a power law equation derived from just a few input parameters that accurately describes the governing mechanism of wound healing. This groundbreaking research provides a powerful tool for the pharmaceutical industry. In fact, this approach proves invaluable for the discovery of novel compounds aimed at disrupting cell migration, assessing the efficacy of prospective drugs designed to impede cancer invasion, and evaluating the immune system’s responses. Full article
(This article belongs to the Special Issue Experimental and Modeling Efforts to Target Metabolism in Cancer)
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