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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Main Authors: Yan Wang, Peter Pivonka, Pascal R Buenzli, David W Smith, Colin R Dunstan
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2011-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110661/?tool=EBI
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spelling doaj-b22ee497c3de41179f4f5ba6673414072021-03-04T01:23:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01611e2749410.1371/journal.pone.0027494Computational modeling of interactions between multiple myeloma and the bone microenvironment.Yan WangPeter PivonkaPascal R BuenzliDavid W SmithColin R DunstanMultiple 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110661/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Yan Wang
Peter Pivonka
Pascal R Buenzli
David W Smith
Colin R Dunstan
spellingShingle Yan Wang
Peter Pivonka
Pascal R Buenzli
David W Smith
Colin R Dunstan
Computational modeling of interactions between multiple myeloma and the bone microenvironment.
PLoS ONE
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)
series PLoS ONE
issn 1932-6203
publishDate 2011-01-01
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.
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22110661/?tool=EBI
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