title: Modelling the anabolic response of bone using a cell population model.
abstract: To maintain bone mass during bone remodelling, coupling is required between bone resorption and bone formation. This coordination is achieved by a network of autocrine and paracrine signalling molecules between cells of the osteoclastic lineage and cells of the osteoblastic lineage. Mathematical modelling of signalling between cells of both lineages can assist in the interpretation of experimental data, clarify signalling interactions and help develop a deeper understanding of complex bone diseases. Several mathematical models of bone cell interactions have been developed, some including RANK-RANKL-OPG signalling between cells and systemic parathyroid hormone PTH. However, to our knowledge these models do not currently include key aspects of some more recent biological evidence for anabolic responses. In this paper, we further develop a mathematical model of bone cell interactions by Pivonka et al. (2008) to include the proliferation of precursor osteoblasts into the model. This inclusion is important to be able to account for Wnt signalling, believed to play an important role in the anabolic responses of bone. We show that an increased rate of differentiation to precursor cells or an increased rate of proliferation of precursor osteoblasts themselves both result in increased bone mass. However, modelling these different processes separately enables the new model to represent recent experimental discoveries such as the role of Wnt signalling in bone biology and the recruitment of osteoblast progenitor cells by transforming growth factor β. Finally, we illustrate the power of the new model's capabilities by applying the model to prostate cancer metastasis to bone. In the bone microenvironment, prostate cancer cells are believed to release some of the same signalling molecules used to coordinate bone remodelling (i.e.,Wnt and PTHrP), enabling the cancer cells to disrupt normal signalling and coordination between bone cells. This disruption can lead to either bone gain or bone loss. We demonstrate that the new computational model developed here is capable of capturing some key observations made on the evolution of the bone mass due to metastasis of prostate cancer to the bone microenvironment.
authors.affiliation: Faculty of Engineering, Computing & Mathematics, The University of Western Australia, WA 6009, Australia. pascal.buenzli@uwa.edu.au
authors.forename: Pascal R
authors.initials: PR
authors.lastname: Buenzli
authors.forename: Peter
authors.initials: P
authors.lastname: Pivonka
authors.forename: Bruce S
authors.initials: BS
authors.lastname: Gardiner
authors.forename: David W
authors.initials: DW
authors.lastname: Smith
mesh_terms: D001862:Bone Resorption / Q000378:metabolism / Q000473:pathology; D001842:Bone and Bones / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism*; D003198:Computer Simulation; D006801:Humans; D008297:Male; D008954:Models, Biological; D009362:Neoplasm Metastasis; D009929:Organ Size / Q000187:drug effects; D010006:Osteoblasts / Q000187:drug effects / Q000378:metabolism / Q000473:pathology; D010012:Osteogenesis / Q000187:drug effects; D010281:Parathyroid Hormone / Q000008:administration & dosage / Q000494:pharmacology; D017430:Prostate-Specific Antigen / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000473:pathology; D060449:Wnt Signaling Pathway / Q000187:drug effects
chemical_list: D010281:Parathyroid Hormone; D017430:Prostate-Specific Antigen