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Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach

We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie’s celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of s...

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Detalles Bibliográficos
Autor principal: Engblom, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677715/
https://www.ncbi.nlm.nih.gov/pubmed/29926381
http://dx.doi.org/10.1007/s11538-018-0454-y
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author Engblom, Stefan
author_facet Engblom, Stefan
author_sort Engblom, Stefan
collection PubMed
description We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie’s celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single-cell computational modeling, here taken to be spatial stochastic reaction–diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch–Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites and via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.
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spelling pubmed-66777152019-08-16 Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach Engblom, Stefan Bull Math Biol Special Issue: Gillespie and His Algorithms We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie’s celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single-cell computational modeling, here taken to be spatial stochastic reaction–diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch–Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites and via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods. Springer US 2018-06-20 2019 /pmc/articles/PMC6677715/ /pubmed/29926381 http://dx.doi.org/10.1007/s11538-018-0454-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Special Issue: Gillespie and His Algorithms
Engblom, Stefan
Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title_full Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title_fullStr Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title_full_unstemmed Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title_short Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach
title_sort stochastic simulation of pattern formation in growing tissue: a multilevel approach
topic Special Issue: Gillespie and His Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677715/
https://www.ncbi.nlm.nih.gov/pubmed/29926381
http://dx.doi.org/10.1007/s11538-018-0454-y
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