Computational modeling of interactions between multiple myeloma and the bone microenvironment.

Multiple Myeloma (MM) is a B-cell malignancy that is characterized by osteolytic bone lesions. It has been postulated that positive feedback loops in the interactions between MM cells and the bone microenvironment form reinforcing 'vicious cycles', resulting in more bone resorption and MM...

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Autores principales:	Yan Wang, Peter Pivonka, Pascal R Buenzli, David W Smith, Colin R Dunstan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b22ee497c3de41179f4f5ba667341407
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spelling	oai:doaj.org-article:b22ee497c3de41179f4f5ba6673414072021-11-18T07:34:42ZComputational modeling of interactions between multiple myeloma and the bone microenvironment.1932-620310.1371/journal.pone.0027494https://doaj.org/article/b22ee497c3de41179f4f5ba6673414072011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110661/?tool=EBIhttps://doaj.org/toc/1932-6203Multiple Myeloma (MM) is a B-cell malignancy that is characterized by osteolytic bone lesions. It has been postulated that positive feedback loops in the interactions between MM cells and the bone microenvironment form reinforcing 'vicious cycles', resulting in more bone resorption and MM cell population growth in the bone microenvironment. Despite many identified MM-bone interactions, the combined effect of these interactions and their relative importance are unknown. In this paper, we develop a computational model of MM-bone interactions and clarify whether the intercellular signaling mechanisms implemented in this model appropriately drive MM disease progression. This new computational model is based on the previous bone remodeling model of Pivonka et al., and explicitly considers IL-6 and MM-BMSC (bone marrow stromal cell) adhesion related pathways, leading to formation of two positive feedback cycles in this model. The progression of MM disease is simulated numerically, from normal bone physiology to a well established MM disease state. Our simulations are consistent with known behaviors and data reported for both normal bone physiology and for MM disease. The model results suggest that the two positive feedback cycles identified for this model are sufficient to jointly drive the MM disease progression. Furthermore, quantitative analysis performed on the two positive feedback cycles clarifies the relative importance of the two positive feedback cycles, and identifies the dominant processes that govern the behavior of the two positive feedback cycles. Using our proposed quantitative criteria, we identify which of the positive feedback cycles in this model may be considered to be 'vicious cycles'. Finally, key points at which to block the positive feedback cycles in MM-bone interactions are identified, suggesting potential drug targets.Yan WangPeter PivonkaPascal R BuenzliDavid W SmithColin R DunstanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 11, p e27494 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Wang Peter Pivonka Pascal R Buenzli David W Smith Colin R Dunstan Computational modeling of interactions between multiple myeloma and the bone microenvironment.
description	Multiple Myeloma (MM) is a B-cell malignancy that is characterized by osteolytic bone lesions. It has been postulated that positive feedback loops in the interactions between MM cells and the bone microenvironment form reinforcing 'vicious cycles', resulting in more bone resorption and MM cell population growth in the bone microenvironment. Despite many identified MM-bone interactions, the combined effect of these interactions and their relative importance are unknown. In this paper, we develop a computational model of MM-bone interactions and clarify whether the intercellular signaling mechanisms implemented in this model appropriately drive MM disease progression. This new computational model is based on the previous bone remodeling model of Pivonka et al., and explicitly considers IL-6 and MM-BMSC (bone marrow stromal cell) adhesion related pathways, leading to formation of two positive feedback cycles in this model. The progression of MM disease is simulated numerically, from normal bone physiology to a well established MM disease state. Our simulations are consistent with known behaviors and data reported for both normal bone physiology and for MM disease. The model results suggest that the two positive feedback cycles identified for this model are sufficient to jointly drive the MM disease progression. Furthermore, quantitative analysis performed on the two positive feedback cycles clarifies the relative importance of the two positive feedback cycles, and identifies the dominant processes that govern the behavior of the two positive feedback cycles. Using our proposed quantitative criteria, we identify which of the positive feedback cycles in this model may be considered to be 'vicious cycles'. Finally, key points at which to block the positive feedback cycles in MM-bone interactions are identified, suggesting potential drug targets.
format	article
author	Yan Wang Peter Pivonka Pascal R Buenzli David W Smith Colin R Dunstan
author_facet	Yan Wang Peter Pivonka Pascal R Buenzli David W Smith Colin R Dunstan
author_sort	Yan Wang
title	Computational modeling of interactions between multiple myeloma and the bone microenvironment.
title_short	Computational modeling of interactions between multiple myeloma and the bone microenvironment.
title_full	Computational modeling of interactions between multiple myeloma and the bone microenvironment.
title_fullStr	Computational modeling of interactions between multiple myeloma and the bone microenvironment.
title_full_unstemmed	Computational modeling of interactions between multiple myeloma and the bone microenvironment.
title_sort	computational modeling of interactions between multiple myeloma and the bone microenvironment.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/b22ee497c3de41179f4f5ba667341407
work_keys_str_mv	AT yanwang computationalmodelingofinteractionsbetweenmultiplemyelomaandthebonemicroenvironment AT peterpivonka computationalmodelingofinteractionsbetweenmultiplemyelomaandthebonemicroenvironment AT pascalrbuenzli computationalmodelingofinteractionsbetweenmultiplemyelomaandthebonemicroenvironment AT davidwsmith computationalmodelingofinteractionsbetweenmultiplemyelomaandthebonemicroenvironment AT colinrdunstan computationalmodelingofinteractionsbetweenmultiplemyelomaandthebonemicroenvironment
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Computational modeling of interactions between multiple myeloma and the bone microenvironment.

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