Dataset Information

Komarova2003_BoneRemodeling

ABSTRACT: This a model from the article: Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling. Komarova SV, Smith RJ, Dixon SJ, Sims SM, Wahl LM Bone2003 Aug;33(2):206-15 14499354, Abstract: Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorderssuch as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among osteoblasts and osteoclasts that allowed us to calculate cell population dynamics and changes in bone mass at a discrete site of bone remodeling. Themodel predicted different modes of dynamic behavior: a single remodeling cycle in response to an external stimulus, a series of internally regulated cycles of bone remodeling, or unstable behavior similar to pathological bone remodeling in Paget's disease. Parametric analysis demonstrated that the mode of dynamic behaviorin the system depends strongly on the regulation of osteoclasts by autocrine factors, such as transforming growth factor beta. Moreover, simulations demonstratedthat nonlinear dynamics of the system may explain the differing effects of immunosuppressants on bone remodeling in vitro and in vivo. In conclusion, the mathematical model revealed that interactions among osteoblasts and osteoclasts result in complex, nonlinear system behavior, which cannot be deduced from studies of each cell type alone. The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process ofremodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease. The model reproduces Fig 2A and Fig 2B of the paper. Note that the Y-axis scale is not right, the osteoblast steadystate is approximatley 212 and not 0 as depicted in the figure. Also, there is atypo in the equation for x2_bar which has been corrected here. Model successfully tested on MathSBML. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

DISEASE(S): Osteoporosis,Paget's Disease Of Bone,Osteoarthritis

SUBMITTER: Harish Dharuri

PROVIDER: BIOMD0000000148 | BioModels | 2007-07-30

REPOSITORIES: BioModels

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Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.

Komarova Svetlana V SV Smith Robert J RJ Dixon S Jeffrey SJ Sims Stephen M SM Wahl Lindi M LM

Bone 20030801 2

Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorders such as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among os ...[more]

PMID: 14499354

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Project description:This a model from the article: Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone. Komarova SV. Endocrinology.2005 Aug;146(8):3589-95. 15860557, Abstract: To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis. The model reproduces Figures 1B and 2A of the reference publication. To obtain the figures 1B, the parameter g21 needs changes. To obtain the figures 1A, the parameters g21, g12 and k2 need to changed. For details look at the curation tab. The initial concentration of Osteoclasts (x1) is corrected to 1.06066 from 10.06066. This model was taken from the CellML repository and automatically converted to SBML. The original model was: CellMLdetails The original CellML model was created by: Lloyd, Catherine, May c.lloyd@auckland.ac.nz The University of Auckland The Bioengineering Institute This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449, Abstract: BACKGROUND: Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease. RESULTS: Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined. CONCLUSIONS: The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches. Note: The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour. Typos in the publication: Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ) Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ) Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ) All these corrections has been implemented in the model, with the authors agreement. Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

Project description:This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449, Abstract: BACKGROUND: Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease. RESULTS: Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined. CONCLUSIONS: The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches. Note: The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour. Typos in the publication: Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ) Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ) Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ) All these corrections has been implemented in the model, with the authors agreement. Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

Project description:This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449, Abstract: BACKGROUND: Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease. RESULTS: Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined. CONCLUSIONS: The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches. Note: The paper describes three models 1) Zero-dimensional Bone Model without Tumou r, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bo ne Model with Tumour and Drug Treatment. Typos in the publication: Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ) Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ) Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ) All these corrections has been implemented in the model, with the authors agreement. Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

Project description:This a model from the article: Modeling the interactions between osteoblast and osteoclast activities in bone remodeling. Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ. J Theor Biol.2004 Aug 7;229(3):293-309. 15234198, Abstract: We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling. This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model. To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model. The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:This a model from the article: Parathyroid hormone temporal effects on bone formation and resorption. Kroll MH. Bull Math Biol 2000 Jan;62(1):163-88 10824426 , Abstract: Parathyroid hormone (PTH) paradoxically causes net bone loss (resorption) when administered in a continuous fashion, and net bone formation (deposition) when administered intermittently. Currently no pharmacological formulations are available to promote bone formation, as needed for the treatment of osteoporosis. The paradoxical behavior of PTH confuses endocrinologists, thus, a model bone resorption or deposition dependent on the timing of PTH administration would de-mystify this behavior and provide the basis for logical drug formulation. We developed a mathematical model that accounts for net bone loss with continuous PTH administration and net bone formation with intermittent PTH administration, based on the differential effects of PTH on the osteoblastic and osteoclastic populations of cells. Bone, being a major reservoir of body calcium, is under the hormonal control of PTH. The overall effect of PTH is to raise plasma levels of calcium, partly through bone resorption. Osteoclasts resorb bone and liberate calcium, but they lack receptors for PTH. The preosteoblastic precursors and preosteoblasts possess receptors for PTH, upon which the hormone induces differentiation from the precursor to preosteoblast and from the preosteoblast to the osteoblast. The osteoblasts generate IL-6; IL-6 stimulates preosteoclasts to differentiate into osteoclasts. We developed a mathematical model for the differentiation of osteoblastic and osteoclastic populations in bone, using a delay time of 1 hour for differentiation of preosteoblastic precursors into preosteoblasts and 2 hours for the differentiation of preosteoblasts into osteoblasts. The ratio of the number of osteoblasts to osteoclasts indicates the net effect of PTH on bone resorption and deposition; the timing of events producing the maximum ratio would induce net bone deposition. When PTH is pulsed with a frequency of every hour, the preosteoblastic population rises and decreases in nearly a symmetric pattern, with 3.9 peaks every 24 hours, and 4.0 peaks every 24 hours when PTH is administered every 6 hours. Thus, the preosteoblast and osteoblast frequency depends more on the nearly constant value of the PTH, rather than on the frequency of the PTH pulsations. Increasing the time delay gradually increases the mean value for the number of osteoblasts. The osteoblastic population oscillates for all intermittent administrations of PTH and even when the PTH infusion is constant. The maximum ratio of osteoblasts to osteoclasts occurs when PTH is administered in pulses of every 6 hours. The delay features in the model bear most of the responsibility for the occurrence of these oscillations, because without the delay and in the presence of constant PTH infusions, no oscillations occur. However, with a delay, under constant PTH infusions, the model generates oscillations. The osteoblast oscillations express limit cycle behavior. Phase plane analysis show simple and complex attractors. Subsequent to a disturbance in the number of osteoblasts, the osteoblasts quickly regain their oscillatory behavior and cycle back to the original attractor, typical of limit cycle behavior. Further, because the model was constructed with dissipative and nonlinear features, one would expect ensuing oscillations to show limit cycle behavior. The results from our model, increased bone deposition with intermittent PTH administration and increased bone resorption with constant PTH administration, conforms with experimental observations and with an accepted explanation for osteoporosis. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Kroll MH. (2000) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Dataset Information

Komarova2003_BoneRemodeling

Publications

Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.

Similar Datasets

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