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Multiscale Mathematical Modeling for Cell Decision-Making in Multicellular Systems

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: closed (16 August 2021) | Viewed by 15217

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Special Issue Editors

Dr. Haralampos Hatzikirou

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Guest Editor

1. Centre for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Straße 46, 01062 Dresden, Germany
2. Mathematics Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: mathematical biology; systems medicine/biology; cell decision-making; multi-scale modeling; non-equilibrium dynamics
Special Issues, Collections and Topics in MDPI journals

Mr. Arnab Barua

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Assistant Guest Editor

Helmholtz Centre for Infection Research, Inhoffenstraße 7, 38124 Braunschweig, Germany
Interests: mathematical biology; cell decision-making; statistical mechanics of biological systems; systems biology

Special Issue Information

Dear Colleagues,

Cell decision-making is the process of cells responding to their microenvironmental cues. Cell decision-making phenomena have been well-studied in the context of single cells. However, how cells make decisions in their multi-cellular environment still remains elusive. Statistical physics and information theory offer a unique toolbox to study cell decisions in their multicellular environments since it allows for (1) low-dimensional description of relevant dynamics and (2) coupling between single cell decisions and the corresponding collective behavior at the multicellular level. Eukaryotic or prokaryotic (bacterial) phenotypic plasticity and cell fate determination are prime paradigms of such cell decision-making impacting all aspects of multicellular behavior, such collective migration, tissue development, and tumor growth. In this Special Issue, we intend to shed light on the latest developments on these aspects.

Prof. Haralampos Hatzikirou
Mr. Arnab Barua
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 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. Entropy 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 2600 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

cell decision-making
multicellular systems
statistical physics
information theory
collective behavior
non-equilibrium dynamics

Published Papers (4 papers)

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Research

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19 pages, 2958 KiB

Open AccessArticle

Model-Based Prediction of an Effective Adhesion Parameter Guiding Multi-Type Cell Segregation

by Philipp Rossbach, Hans-Joachim Böhme, Steffen Lange and Anja Voss-Böhme

Entropy 2021, 23(11), 1378; https://doi.org/10.3390/e23111378 - 21 Oct 2021

Cited by 1 | Viewed by 1443

Abstract

The process of cell-sorting is essential for development and maintenance of tissues. Mathematical modeling can provide the means to analyze the consequences of different hypotheses about the underlying mechanisms. With the Differential Adhesion Hypothesis, Steinberg proposed that cell-sorting is determined by quantitative differences in cell-type-specific intercellular adhesion strengths. An implementation of the Differential Adhesion Hypothesis is the Differential Migration Model by Voss-Böhme and Deutsch. There, an effective adhesion parameter was derived analytically for systems with two cell types, which predicts the asymptotic sorting pattern. However, the existence and form of such a parameter for more than two cell types is unclear. Here, we generalize analytically the concept of an effective adhesion parameter to three and more cell types and demonstrate its existence numerically for three cell types based on in silico time-series data that is produced by a cellular-automaton implementation of the Differential Migration Model. Additionally, we classify the segregation behavior using statistical learning methods and show that the estimated effective adhesion parameter for three cell types matches our analytical prediction. Finally, we demonstrate that the effective adhesion parameter can resolve a recent dispute about the impact of interfacial adhesion, cortical tension and heterotypic repulsion on cell segregation. Full article

(This article belongs to the Special Issue Multiscale Mathematical Modeling for Cell Decision-Making in Multicellular Systems)

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25 pages, 1732 KiB

Open AccessArticle

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

by Arnab Barua, Alireza Beygi and Haralampos Hatzikirou

Entropy 2021, 23(7), 867; https://doi.org/10.3390/e23070867 - 7 Jul 2021

Cited by 4 | Viewed by 2433

Abstract

The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic. Full article

(This article belongs to the Special Issue Multiscale Mathematical Modeling for Cell Decision-Making in Multicellular Systems)

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16 pages, 2948 KiB

Open AccessArticle

Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-Genetic Heterogeneity in Prostate Cancer Cells

by Divyoj Singh, Federico Bocci, Prakash Kulkarni and Mohit Kumar Jolly

Entropy 2021, 23(3), 288; https://doi.org/10.3390/e23030288 - 26 Feb 2021

Cited by 4 | Viewed by 2691

Abstract

Non-genetic heterogeneity is emerging as a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving (a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, (b) multistability in epithelial–mesenchymal transition (EMT), and (c) Notch–Delta–Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to monostability, bistability, or oscillations in the levels of AR, as well as propagation of oscillations to EMT dynamics. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients. Full article

(This article belongs to the Special Issue Multiscale Mathematical Modeling for Cell Decision-Making in Multicellular Systems)

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Review

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36 pages, 3132 KiB

Open AccessReview

Modeling the Dynamics of T-Cell Development in the Thymus

by Philippe A. Robert, Heike Kunze-Schumacher, Victor Greiff and Andreas Krueger

Entropy 2021, 23(4), 437; https://doi.org/10.3390/e23040437 - 8 Apr 2021

Cited by 15 | Viewed by 8003

Abstract

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multi-scale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review experimental strategies aimed at revealing quantitative and dynamic properties of T-cell development and how they have been implemented in mathematical modeling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multi-scale picture of T-cell development. Full article

(This article belongs to the Special Issue Multiscale Mathematical Modeling for Cell Decision-Making in Multicellular Systems)

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