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Modeling cell populations metabolism and competition under maximum power constraints

Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to mode...

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Autores principales: Conte, Luigi, Gonella, Francesco, Giansanti, Andrea, Kleidon, Axel, Romano, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659174/
https://www.ncbi.nlm.nih.gov/pubmed/37939139
http://dx.doi.org/10.1371/journal.pcbi.1011607
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author Conte, Luigi
Gonella, Francesco
Giansanti, Andrea
Kleidon, Axel
Romano, Alessandra
author_facet Conte, Luigi
Gonella, Francesco
Giansanti, Andrea
Kleidon, Axel
Romano, Alessandra
author_sort Conte, Luigi
collection PubMed
description Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to model cell metabolisms and competition under energetic constraints. As a proof-of-concept, we apply this general formulation to study the dynamics of the onset of a specific blood cancer—called Multiple Myeloma. We show that a minimal model describing antagonist cell populations competing for limited resources, as regulated by microenvironmental factors and internal cellular structures, reproduces patterns of Multiple Myeloma evolution, due to the uncontrolled proliferation of cancerous plasma cells within the bone marrow. The model is characterized by a class of regime shifts to more dissipative states for selectively advantaged malignant plasma cells, reflecting a breakdown of self-regulation in the bone marrow. The transition times obtained from the simulations range from years to decades consistently with clinical observations of survival times of patients. This irreversible dynamical behavior represents a possible description of the incurable nature of myelomas based on the ecological interactions between plasma cells and the microenvironment, embedded in a larger complex system. The use of ATP equivalent energy units in defining stocks and flows is a key to constructing an ecological model which reproduces the onset of myelomas as transitions between states of a system which reflects the energetics of plasma cells. This work provides a basis to construct more complex models representing myelomas, which can be compared with model ecosystems.
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spelling pubmed-106591742023-11-08 Modeling cell populations metabolism and competition under maximum power constraints Conte, Luigi Gonella, Francesco Giansanti, Andrea Kleidon, Axel Romano, Alessandra PLoS Comput Biol Research Article Ecological interactions are fundamental at the cellular scale, addressing the possibility of a description of cellular systems that uses language and principles of ecology. In this work, we use a minimal ecological approach that encompasses growth, adaptation and survival of cell populations to model cell metabolisms and competition under energetic constraints. As a proof-of-concept, we apply this general formulation to study the dynamics of the onset of a specific blood cancer—called Multiple Myeloma. We show that a minimal model describing antagonist cell populations competing for limited resources, as regulated by microenvironmental factors and internal cellular structures, reproduces patterns of Multiple Myeloma evolution, due to the uncontrolled proliferation of cancerous plasma cells within the bone marrow. The model is characterized by a class of regime shifts to more dissipative states for selectively advantaged malignant plasma cells, reflecting a breakdown of self-regulation in the bone marrow. The transition times obtained from the simulations range from years to decades consistently with clinical observations of survival times of patients. This irreversible dynamical behavior represents a possible description of the incurable nature of myelomas based on the ecological interactions between plasma cells and the microenvironment, embedded in a larger complex system. The use of ATP equivalent energy units in defining stocks and flows is a key to constructing an ecological model which reproduces the onset of myelomas as transitions between states of a system which reflects the energetics of plasma cells. This work provides a basis to construct more complex models representing myelomas, which can be compared with model ecosystems. Public Library of Science 2023-11-08 /pmc/articles/PMC10659174/ /pubmed/37939139 http://dx.doi.org/10.1371/journal.pcbi.1011607 Text en © 2023 Conte et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Conte, Luigi
Gonella, Francesco
Giansanti, Andrea
Kleidon, Axel
Romano, Alessandra
Modeling cell populations metabolism and competition under maximum power constraints
title Modeling cell populations metabolism and competition under maximum power constraints
title_full Modeling cell populations metabolism and competition under maximum power constraints
title_fullStr Modeling cell populations metabolism and competition under maximum power constraints
title_full_unstemmed Modeling cell populations metabolism and competition under maximum power constraints
title_short Modeling cell populations metabolism and competition under maximum power constraints
title_sort modeling cell populations metabolism and competition under maximum power constraints
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659174/
https://www.ncbi.nlm.nih.gov/pubmed/37939139
http://dx.doi.org/10.1371/journal.pcbi.1011607
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