Literature DB >> 25770313

LBIBCell: a cell-based simulation environment for morphogenetic problems.

Simon Tanaka1, David Sichau2, Dagmar Iber1.   

Abstract

MOTIVATION: The simulation of morphogenetic problems requires the simultaneous and coupled simulation of signalling and tissue dynamics. A cellular resolution of the tissue domain is important to adequately describe the impact of cell-based events, such as cell division, cell-cell interactions and spatially restricted signalling events. A tightly coupled cell-based mechano-regulatory simulation tool is therefore required.
RESULTS: We developed an open-source software framework for morphogenetic problems. The environment offers core functionalities for the tissue and signalling models. In addition, the software offers great flexibility to add custom extensions and biologically motivated processes. Cells are represented as highly resolved, massless elastic polygons; the viscous properties of the tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used to model the interaction between the viscous and elastic properties of the cells, thus extending on the IBCell model. The fluid and signalling processes are solved using the Lattice Boltzmann method. As application examples we simulate signalling-dependent tissue dynamics.
AVAILABILITY AND IMPLEMENTATION: The documentation and source code are available on http://tanakas.bitbucket.org/lbibcell/index.html
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2015        PMID: 25770313     DOI: 10.1093/bioinformatics/btv147

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

Review 1.  Mechanocellular models of epithelial morphogenesis.

Authors:  Alexander G Fletcher; Fergus Cooper; Ruth E Baker
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2017-05-19       Impact factor: 6.237

2.  A quantitative high-resolution computational mechanics cell model for growing and regenerating tissues.

Authors:  Paul Van Liedekerke; Johannes Neitsch; Tim Johann; Enrico Warmt; Ismael Gonzàlez-Valverde; Stefan Hoehme; Steffen Grosser; Josef Kaes; Dirk Drasdo
Journal:  Biomech Model Mechanobiol       Date:  2019-11-20

3.  Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry.

Authors:  M E Johnson; A Chen; J R Faeder; P Henning; I I Moraru; M Meier-Schellersheim; R F Murphy; T Prüstel; J A Theriot; A M Uhrmacher
Journal:  Mol Biol Cell       Date:  2020-11-25       Impact factor: 4.138

4.  Comparing individual-based approaches to modelling the self-organization of multicellular tissues.

Authors:  James M Osborne; Alexander G Fletcher; Joe M Pitt-Francis; Philip K Maini; David J Gavaghan
Journal:  PLoS Comput Biol       Date:  2017-02-13       Impact factor: 4.475

5.  A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation.

Authors:  Julien Delile; Matthieu Herrmann; Nadine Peyriéras; René Doursat
Journal:  Nat Commun       Date:  2017-01-23       Impact factor: 14.919

6.  Modeling of Wnt-mediated tissue patterning in vertebrate embryogenesis.

Authors:  Jakob Rosenbauer; Chengting Zhang; Benjamin Mattes; Ines Reinartz; Kyle Wedgwood; Simone Schindler; Claude Sinner; Steffen Scholpp; Alexander Schug
Journal:  PLoS Comput Biol       Date:  2020-06-24       Impact factor: 4.475

7.  Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines.

Authors:  Paul Van Liedekerke; Johannes Neitsch; Tim Johann; Kevin Alessandri; Pierre Nassoy; Dirk Drasdo
Journal:  PLoS Comput Biol       Date:  2019-03-08       Impact factor: 4.475

8.  Adapting a Plant Tissue Model to Animal Development: Introducing Cell Sliding into VirtualLeaf.

Authors:  Henri B Wolff; Lance A Davidson; Roeland M H Merks
Journal:  Bull Math Biol       Date:  2019-03-29       Impact factor: 1.758

Review 9.  A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis.

Authors:  Aleksandr Bobrovskikh; Alexey Doroshkov; Stefano Mazzoleni; Fabrizio Cartenì; Francesco Giannino; Ulyana Zubairova
Journal:  Front Genet       Date:  2021-05-21       Impact factor: 4.599

10.  Scalable population-level modelling of biological cells incorporating mechanics and kinetics in continuous time.

Authors:  Stefan Engblom; Daniel B Wilson; Ruth E Baker
Journal:  R Soc Open Sci       Date:  2018-08-01       Impact factor: 2.963

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