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Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth
The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a ke...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019571/ https://www.ncbi.nlm.nih.gov/pubmed/35463947 http://dx.doi.org/10.3389/fmolb.2022.836794 |
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author | Ponce-de-Leon, Miguel Montagud, Arnau Akasiadis, Charilaos Schreiber, Janina Ntiniakou, Thaleia Valencia, Alfonso |
author_facet | Ponce-de-Leon, Miguel Montagud, Arnau Akasiadis, Charilaos Schreiber, Janina Ntiniakou, Thaleia Valencia, Alfonso |
author_sort | Ponce-de-Leon, Miguel |
collection | PubMed |
description | The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets. |
format | Online Article Text |
id | pubmed-9019571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90195712022-04-21 Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth Ponce-de-Leon, Miguel Montagud, Arnau Akasiadis, Charilaos Schreiber, Janina Ntiniakou, Thaleia Valencia, Alfonso Front Mol Biosci Molecular Biosciences The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets. Frontiers Media S.A. 2022-04-06 /pmc/articles/PMC9019571/ /pubmed/35463947 http://dx.doi.org/10.3389/fmolb.2022.836794 Text en Copyright © 2022 Ponce-de-Leon, Montagud, Akasiadis, Schreiber, Ntiniakou and Valencia. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Ponce-de-Leon, Miguel Montagud, Arnau Akasiadis, Charilaos Schreiber, Janina Ntiniakou, Thaleia Valencia, Alfonso Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title | Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title_full | Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title_fullStr | Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title_full_unstemmed | Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title_short | Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth |
title_sort | optimizing dosage-specific treatments in a multi-scale model of a tumor growth |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019571/ https://www.ncbi.nlm.nih.gov/pubmed/35463947 http://dx.doi.org/10.3389/fmolb.2022.836794 |
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