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A noble extended stochastic logistic model for cell proliferation with density-dependent parameters

Cell proliferation often experiences a density-dependent intrinsic proliferation rate (IPR) and negative feedback from growth-inhibiting molecules in culture media. The lack of flexible models with explanatory parameters fails to capture such a proliferation mechanism. We propose an extended logisti...

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Autores principales: Roy, Trina, Ghosh, Sinchan, Saha, Bapi, Bhattacharya, Sabyasachi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151920/
https://www.ncbi.nlm.nih.gov/pubmed/35637247
http://dx.doi.org/10.1038/s41598-022-12719-y
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author Roy, Trina
Ghosh, Sinchan
Saha, Bapi
Bhattacharya, Sabyasachi
author_facet Roy, Trina
Ghosh, Sinchan
Saha, Bapi
Bhattacharya, Sabyasachi
author_sort Roy, Trina
collection PubMed
description Cell proliferation often experiences a density-dependent intrinsic proliferation rate (IPR) and negative feedback from growth-inhibiting molecules in culture media. The lack of flexible models with explanatory parameters fails to capture such a proliferation mechanism. We propose an extended logistic growth law with the density-dependent IPR and additional negative feedback. The extended parameters of the proposed model can be interpreted as density-dependent cell-cell cooperation and negative feedback on cell proliferation. Moreover, we incorporate further density regulation for flexibility in the model through environmental resistance on cells. The proposed growth law has similarities with the strong Allee model and harvesting phenomenon. We also develop the stochastic analog of the deterministic model by representing possible heterogeneity in growth-inhibiting molecules and environmental perturbation of the culture setup as correlated multiplicative and additive noises. The model provides a conditional maximum sustainable stable cell density (MSSCD) and a new fitness measure for proliferative cells. The proposed model shows superiority to the logistic law after fitting to real cell culture datasets. We illustrate both conditional MSSCD and the new cell fitness for a range of parameters. The cell density distributions reveal the chance of overproliferation, underproliferation, or decay for different parameter sets under the deterministic and stochastic setups.
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spelling pubmed-91519202022-06-01 A noble extended stochastic logistic model for cell proliferation with density-dependent parameters Roy, Trina Ghosh, Sinchan Saha, Bapi Bhattacharya, Sabyasachi Sci Rep Article Cell proliferation often experiences a density-dependent intrinsic proliferation rate (IPR) and negative feedback from growth-inhibiting molecules in culture media. The lack of flexible models with explanatory parameters fails to capture such a proliferation mechanism. We propose an extended logistic growth law with the density-dependent IPR and additional negative feedback. The extended parameters of the proposed model can be interpreted as density-dependent cell-cell cooperation and negative feedback on cell proliferation. Moreover, we incorporate further density regulation for flexibility in the model through environmental resistance on cells. The proposed growth law has similarities with the strong Allee model and harvesting phenomenon. We also develop the stochastic analog of the deterministic model by representing possible heterogeneity in growth-inhibiting molecules and environmental perturbation of the culture setup as correlated multiplicative and additive noises. The model provides a conditional maximum sustainable stable cell density (MSSCD) and a new fitness measure for proliferative cells. The proposed model shows superiority to the logistic law after fitting to real cell culture datasets. We illustrate both conditional MSSCD and the new cell fitness for a range of parameters. The cell density distributions reveal the chance of overproliferation, underproliferation, or decay for different parameter sets under the deterministic and stochastic setups. Nature Publishing Group UK 2022-05-30 /pmc/articles/PMC9151920/ /pubmed/35637247 http://dx.doi.org/10.1038/s41598-022-12719-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Roy, Trina
Ghosh, Sinchan
Saha, Bapi
Bhattacharya, Sabyasachi
A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title_full A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title_fullStr A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title_full_unstemmed A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title_short A noble extended stochastic logistic model for cell proliferation with density-dependent parameters
title_sort noble extended stochastic logistic model for cell proliferation with density-dependent parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151920/
https://www.ncbi.nlm.nih.gov/pubmed/35637247
http://dx.doi.org/10.1038/s41598-022-12719-y
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