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Special Issue "In Silico Models of Cell–Microenvironment Interactions in Healthy and Tumor Tissues"

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A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Microenvironment".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4548

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Special Issue Editors

Dr. Katarzyna A. Rejniak

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Guest Editor

Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
Interests: systems biology of tumor microenvironment; in silico modeling of extracellular matrices; interstitial transport; tissue metabolic landscape

Dr. Magdalena Stolarska
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Guest Editor

Department of Mathematics, University of St. Thomas, Saint Paul, MN, USA
Interests: applied mathematics; mathematical biology; scientific computing numerical analysis

Dr. Gibin Powathil

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Guest Editor

Gibin Powathil, Department of Mathematics, Centre for Biomathematics, Swansea University, Swansea, UK
Interests: applied mathematics; mathematical oncology; mathematical biology

Special Issue Information

Dear Colleagues,

The complexity and heterogeneity of tissue microenvironment, its interactions with multiple healthy and tumorous cells, as well as dynamic changes in the microenvironment as a result of various treatments cannot be fully recreated in laboratory experiments but can be addressed by in silico modeling. Mathematical models, by their nature, are well suited to deal with multiple interdependent players and provide quantitative method to analyze such complex systems.

In this Special Issue, we welcome papers that use in silico modeling to address interactions between healthy or tumor cells and the surrounding microenvironment, including one of more components of the tissue microenvironment: physical, such as extracellular matrix fiber structure; chemical, such as gradients of metabolites, nutrients, and waste products; cellular, such as stromal, endothelial, or immune cells; and the interstitial fluid interpenetrating the tissue structure. This Special Issue will showcase recent advances in mathematical models of cell–environment interactions.

Dr. Katarzyna A. Rejniak
Dr. Magdalena Stolarska
Dr. Gibin Powathil
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. Cells 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 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

mathematical modeling of tissue microenvironments
mechanobiology
tissue metabolic landscape
cell–microenvironment crosstalk

Published Papers (3 papers)

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16 pages, 4580 KiB

Open AccessArticle

Dynamics of Fibril Collagen Remodeling by Tumor Cells: A Model of Tumor-Associated Collagen Signatures

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Cells 2023, 12(23), 2688; https://doi.org/10.3390/cells12232688 - 22 Nov 2023

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Abstract

Many solid tumors are characterized by a dense extracellular matrix (ECM) composed of various ECM fibril proteins. These proteins provide structural support and a biological context for the residing cells. The reciprocal interactions between growing and migrating tumor cells and the surrounding stroma result in dynamic changes in the ECM architecture and its properties. With the use of advanced imaging techniques, several specific patterns in the collagen surrounding the breast tumor have been identified in both tumor murine models and clinical histology images. These tumor-associated collagen signatures (TACS) include loosely organized fibrils far from the tumor and fibrils aligned either parallel or perpendicular to tumor colonies. They are correlated with tumor behavior, such as benign growth or invasive migration. However, it is not fully understood how one specific fibril pattern can be dynamically remodeled to form another alignment. Here, we present a novel multi-cellular lattice-free (MultiCell-LF) agent-based model of ECM that, in contrast to static histology images, can simulate dynamic changes between TACSs. This model allowed us to identify the rules of cell–ECM physical interplay and feedback that guided the emergence and transition among various TACSs. Full article

(This article belongs to the Special Issue In Silico Models of Cell–Microenvironment Interactions in Healthy and Tumor Tissues)

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19 pages, 4761 KiB

Open AccessArticle

Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy

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Cells 2022, 11(19), 3165; https://doi.org/10.3390/cells11193165 - 09 Oct 2022

Cited by 2 | Viewed by 1552

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy. Full article

(This article belongs to the Special Issue In Silico Models of Cell–Microenvironment Interactions in Healthy and Tumor Tissues)

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21 pages, 3767 KiB

Open AccessBrief Report

Mathematical Modeling of Clonal Interference by Density-Dependent Selection in Heterogeneous Cancer Cell Lines

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Cells 2023, 12(14), 1849; https://doi.org/10.3390/cells12141849 - 14 Jul 2023

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Abstract

Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, thus affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically would give insights into cellular phenotypes before they unfold temporally. Here, we re-analyzed single cell RNA (scRNA) and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. Using live cell imaging, we quantified differences in the growth rate and contact inhibition between the eight cell lines and used these differences to prioritize the transcriptomic biomarkers of the growth rate and carrying capacity. Using these biomarkers, we found significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We used these predictions to simulate how the clonal composition of a cell line would change depending on density conditions during in-vitro experiments. Once validated, these models can aid in the design of experiments that steer evolution with density-dependent selection. Full article

(This article belongs to the Special Issue In Silico Models of Cell–Microenvironment Interactions in Healthy and Tumor Tissues)

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