nonlinear pattern formation in bone growth and architecture
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2015
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Abstract
The 3D morphology of bone arises through adaptation to its required engineering performance. Genetically and adaptively bone travels along a complex spatio-temporal trajectory to acquire optimal architecture. On a cellular, micro-anatomical scale, what mechanisms coordinate the activity of osteoblasts and osteoclasts to produce complex and efficient bone architectures? One mechanism is examined here – chaotic nonlinear pattern formation (NPF) – which underlies in a unifying way natural structures as disparate as trabecular bone, swarms of birds flying, island formation, fluid turbulence and others. At the heart of NPF is the fact that simple rules operating between interacting elements, and Turing-like interaction between global and local signals, lead to complex and structured patterns. The study of group intelligence exhibited by swarming birds or shoaling fish has led to an embodiment of NPF called particle swarm optimization (PSO). This theoretical model could be applicable to the behavior of osteoblasts osteoclasts and osteocytes, seeing them operating socially in response simultaneously to both global and local signals (endocrine, cytokine, mechanical) resulting in their clustered activity at formation and resorption sites. This represents problem-solving by social intelligence, and could potentially add further realism to in-silico simulation of bone modeling.What insights has NPF provided to bone biology? One example concerns the genetic disorder Juvenile Pagets Disease (JPD) or Idiopathic Hyperphosphatasia, where the anomalous parallel trabecular architecture characteristic of this pathology is consistent with an NPF paradigm by analogy with known experimental NPF systems. Here coupling or feedback between osteoblasts and osteoclasts is the critical element.This NPF paradigm implies a profound link between bone regulation and its architecture: in bone the architecture is the regulation. The former is the emergent consequence of the latter.
| Reference Key |
esalmon2015frontiersnonlinear
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| Authors | ;Phil eSalmon |
| Journal | aip advances |
| Year | 2015 |
| DOI |
10.3389/fendo.2014.00239
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