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BIO-LGCA: A cellular automaton modelling class for analysing collective cell migration. PLoS Comput Biol 2021; 17:e1009066. [PMID: 34129639 PMCID: PMC8232544 DOI: 10.1371/journal.pcbi.1009066] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 06/25/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022] Open
Abstract
Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics—understood as population behaviour arising from the interplay of the constituting discrete cells—can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments. Pattern formation during embryonic development and pathological tissue dynamics, such as cancer invasion, emerge from individual intercellular interactions. In order to study the impact of single cell dynamics and cell-cell interactions on tissue behaviour, one needs to develop space-time-dependent on- or off-lattice agent-based models (ABMs), which consider the behaviour of individual cells. However, classical on-lattice agent-based models also known as cellular automata fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. Here, we present the rule- and lattice-based BIO-LGCA modelling class which allows for (i) rigorous derivation of rules from biophysical laws and/or experimental data, (ii) mathematical analysis of collective migration, and (iii) computationally efficient simulations.
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Roy U, Mugler A. Intermediate adhesion maximizes migration velocity of multicellular clusters. Phys Rev E 2021; 103:032410. [PMID: 33862697 DOI: 10.1103/physreve.103.032410] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 03/02/2021] [Indexed: 06/12/2023]
Abstract
Collections of cells exhibit coherent migration during morphogenesis, cancer metastasis, and wound healing. In many cases, bigger clusters split, smaller subclusters collide and reassemble, and gaps continually emerge. The connections between cell-level adhesion and cluster-level dynamics, as well as the resulting consequences for cluster properties such as migration velocity, remain poorly understood. Here we investigate collective migration of one- and two-dimensional cell clusters that collectively track chemical gradients using a mechanism based on contact inhibition of locomotion. We develop both a minimal description based on the lattice gas model of statistical physics and a more realistic framework based on the cellular Potts model which captures cell shape changes and cluster rearrangement. In both cases, we find that cells have an optimal adhesion strength that maximizes cluster migration speed. The optimum negotiates a tradeoff between maintaining cell-cell contact and maintaining configurational freedom, and we identify maximal variability in the cluster aspect ratio as a revealing signature. Our results suggest a collective benefit for intermediate cell-cell adhesion.
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Affiliation(s)
- Ushasi Roy
- Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana 47907, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Andrew Mugler
- Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana 47907, USA
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
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Salavati H, Soltani M, Amanpour S. The pivotal role of angiogenesis in a multi-scale modeling of tumor growth exhibiting the avascular and vascular phases. Microvasc Res 2018; 119:105-116. [PMID: 29742454 DOI: 10.1016/j.mvr.2018.05.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2017] [Revised: 04/28/2018] [Accepted: 05/03/2018] [Indexed: 12/28/2022]
Abstract
The mechanisms involved in tumor growth mainly occur at the microenvironment, where the interactions between the intracellular, intercellular and extracellular scales mediate the dynamics of tumor. In this work, we present a multi-scale model of solid tumor dynamics to simulate the avascular and vascular growth as well as tumor-induced angiogenesis. The extracellular and intercellular scales are modeled using partial differential equations and cellular Potts model, respectively. Also, few biochemical and biophysical rules control the dynamics of intracellular level. On the other hand, the growth of melanoma tumors is modeled in an animal in-vivo study to evaluate the simulation. The simulation shows that the model successfully reproduces a completed image of processes involved in tumor growth such as avascular and vascular growth as well as angiogenesis. The model incorporates the phenotypes of cancerous cells including proliferating, quiescent and necrotic cells, as well as endothelial cells during angiogenesis. The results clearly demonstrate the pivotal effect of angiogenesis on the progression of cancerous cells. Also, the model exhibits important events in tumor-induced angiogenesis like anastomosis. Moreover, the computational trend of tumor growth closely follows the observations in the experimental study.
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Affiliation(s)
- Hooman Salavati
- Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis, Iran
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Computational Medicine Center, Tehran, Iran; Division of Nuclear Medicine, Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, MD, USA; Department of Earth & Environmental Sciences, University of Waterloo, Ontario, Canada; Cancer Biology Research Centre, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
| | - Saeid Amanpour
- Cancer Biology Research Centre, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
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Nava-Sedeño JM, Hatzikirou H, Klages R, Deutsch A. Cellular automaton models for time-correlated random walks: derivation and analysis. Sci Rep 2017; 7:16952. [PMID: 29209065 PMCID: PMC5717221 DOI: 10.1038/s41598-017-17317-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 11/22/2017] [Indexed: 11/09/2022] Open
Abstract
Many diffusion processes in nature and society were found to be anomalous, in the sense of being fundamentally different from conventional Brownian motion. An important example is the migration of biological cells, which exhibits non-trivial temporal decay of velocity autocorrelation functions. This means that the corresponding dynamics is characterized by memory effects that slowly decay in time. Motivated by this we construct non-Markovian lattice-gas cellular automata models for moving agents with memory. For this purpose the reorientation probabilities are derived from velocity autocorrelation functions that are given a priori; in that respect our approach is “data-driven”. Particular examples we consider are velocity correlations that decay exponentially or as power laws, where the latter functions generate anomalous diffusion. The computational efficiency of cellular automata combined with our analytical results paves the way to explore the relevance of memory and anomalous diffusion for the dynamics of interacting cell populations, like confluent cell monolayers and cell clustering.
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Affiliation(s)
- J M Nava-Sedeño
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany.
| | - H Hatzikirou
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany.,Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Center for Infection Research, Inhoffenstraße 7, 38124, Braunschweig, Germany
| | - R Klages
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom
| | - A Deutsch
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany
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Reher D, Klink B, Deutsch A, Voss-Böhme A. Cell adhesion heterogeneity reinforces tumour cell dissemination: novel insights from a mathematical model. Biol Direct 2017; 12:18. [PMID: 28800767 PMCID: PMC5553611 DOI: 10.1186/s13062-017-0188-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 07/17/2017] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Cancer cell invasion, dissemination, and metastasis have been linked to an epithelial-mesenchymal transition (EMT) of individual tumour cells. During EMT, adhesion molecules like E-cadherin are downregulated and the decrease of cell-cell adhesion allows tumour cells to dissociate from the primary tumour mass. This complex process depends on intracellular cues that are subject to genetic and epigenetic variability, as well as extrinsic cues from the local environment resulting in a spatial heterogeneity in the adhesive phenotype of individual tumour cells. Here, we use a novel mathematical model to study how adhesion heterogeneity, influenced by intrinsic and extrinsic factors, affects the dissemination of tumour cells from an epithelial cell population. The model is a multiscale cellular automaton that couples intracellular adhesion receptor regulation with cell-cell adhesion. RESULTS Simulations of our mathematical model indicate profound effects of adhesion heterogeneity on tumour cell dissemination. In particular, we show that a large variation of intracellular adhesion receptor concentrations in a cell population reinforces cell dissemination, regardless of extrinsic cues mediated through the local cell density. However, additional control of adhesion receptor concentration through the local cell density, which can be assumed in healthy cells, weakens the effect. Furthermore, we provide evidence that adhesion heterogeneity can explain the remarkable differences in adhesion receptor concentrations of epithelial and mesenchymal phenotypes observed during EMT and might drive early dissemination of tumour cells. CONCLUSIONS Our results suggest that adhesion heterogeneity may be a universal trigger to reinforce cell dissemination in epithelial cell populations. This effect can be at least partially compensated by a control of adhesion receptor regulation through neighbouring cells. Accordingly, our findings explain how both an increase in intra-tumour adhesion heterogeneity and the loss of control through the local environment can promote tumour cell dissemination. REVIEWERS This article was reviewed by Hanspeter Herzel, Thomas Dandekar and Marek Kimmel.
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Affiliation(s)
- David Reher
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig, 04103, Germany.
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany.
| | - Barbara Klink
- Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstr. 74, Dresden, 01307, Germany
- German Cancer Consortium (DKTK), Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Center for Molecular Tumor Diagnostics (CMTD), National Center for Tumor Diseases (NCT), Dresden, Germany
| | - Andreas Deutsch
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany
| | - Anja Voss-Böhme
- Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Str. 46, Dresden, 01062, Germany
- Hochschule für Technik und Wirtschaft Dresden, Fakultät Informatik/Mathematik, Friedrich-List-Platz 1, Dresden, 01069, Germany
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Varennes J, Han B, Mugler A. Collective Chemotaxis through Noisy Multicellular Gradient Sensing. Biophys J 2017; 111:640-649. [PMID: 27508447 DOI: 10.1016/j.bpj.2016.06.040] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 06/17/2016] [Accepted: 06/29/2016] [Indexed: 12/11/2022] Open
Abstract
Collective cell migration in response to a chemical cue occurs in many biological processes such as morphogenesis and cancer metastasis. Clusters of migratory cells in these systems are capable of responding to gradients of <1% difference in chemical concentration across a cell length. Multicellular systems are extremely sensitive to their environment, and although the limits to multicellular sensing are becoming known, how this information leads to coherent migration remains poorly understood. We develop a computational model of multicellular sensing and migration in which groups of cells collectively measure noisy chemical gradients. The output of the sensing process is coupled to the polarization of individual cells to model migratory behavior. Through the use of numerical simulations, we find that larger clusters of cells detect the gradient direction with higher precision and thus achieve stronger polarization bias, but larger clusters also induce more drag on collective motion. The trade-off between these two effects leads to an optimal cluster size for most efficient migration. We discuss how our model could be validated using simple, phenomenological experiments.
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Affiliation(s)
- Julien Varennes
- Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana
| | - Bumsoo Han
- Schools of Mechanical Engineering and Biomedical Engineering, Purdue University, West Lafayette, Indiana; Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Andrew Mugler
- Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana.
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Simpson MJ, Baker RE. Special issue on spatial moment techniques for modelling biological processes. Bull Math Biol 2016; 77:581-5. [PMID: 25894919 DOI: 10.1007/s11538-015-0066-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Matthew J Simpson
- Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia,
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