How microcracks help to remodel the human bone microstructure

1. Modeling Bone-Remodeling
21.04.2015 1UNIVERSITÄT ROSTOCK | Institut für Informatik | Dept. Of Systems Biology and Bioinformatics | www.sbi.uni-rostock.de21.04.2015
Modeling Bone-Remodeling
The Influence of Microcracks
Tom Theile

2. Modeling Bone-Remodeling
21.04.2015 2UNIVERSITÄT ROSTOCK | Institut für Informatik | Dept. Of Systems Biology and Bioinformatics | www.sbi.uni-rostock.de21.04.2015 2
Bone Remodeling
Introduction – The Bone Remodeling Process
The human skeleton serves many functions:
For all these functions, the remodeling-process plays an important role
Remodeling repairs damages and microdamages and lets the bone adapt to changing loads
 ~10% turnaround every year in adults (Heaney et al. 1978)
 Pathologic changes may lead to osteoporosis and fractures
Mechanical functions Chemical functions Biological functions
• Carry loads
• Protect the body
• Allow movements
• Store calcium and iron
• Produce blood cells
• Release hormons

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Bone Remodeling – How is it done?
Introduction – The Bone Remodeling Process
Osteoclasts resorp mineralized bone
Osteoblasts synthesize mineralized bone
Osteocytes are former osteoblasts, build into the mineralized matrix.
Osteocytes
Image-source: york.ac.uk

4. 21.04.2015 UNIVERSITÄT ROSTOCK | Institut für Informatik | Dept. Of Systems Biology and Bioinformatics | www.sbi.uni-rostock.de 4
Bone Remodeling – How is it done?
Introduction – The Bone Remodeling Process
Osteoclasts resorp mineralized bone
Osteoblasts synthesize mineralized bone
Osteocytes are former osteoblasts, build into the mineralized matrix.
Osteocytes
Image-source: york.ac.uk

5. Modeling Bone-Remodeling
21.04.2015 5UNIVERSITÄT ROSTOCK | Institut für Informatik | Dept. Of Systems Biology and Bioinformatics | www.sbi.uni-rostock.de
Osteocytes
 Osteocytes form a network to exchange
nutrients and waste (Aarden et al. 1994)
 This Network may also be used for
intercellular communication
 Osteocytes secrete RANKL
 Osteocytes initiate bone remodeling events
 Osteocytes control the bone remodeling
process (Nakashima et al. 2011)

6. Modeling Bone-Remodeling
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Microcracks
• Microcracks are tiny fractures
• Microcracks arise after many cycles of high (but not too
high) loads
• Accumulation and growth of microcracks leads to material
failure
• Bone remodeling repairs microcracks
• First evidence for higher remodeling rates at locations with
more microcracks (Mori et. al)

7. Modeling Bone-Remodeling
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Microcracks – Our Objective
(afaik) no direct evidence, that osteocytes can really sense mechanical
or electrical fields
Our hypothesis:
• Osteocytes can (only) sense microcracks
• Local bone remodeling is only steered by microcracks
• Adaption to changing loads only by excessive repair of
microcracks
Our model
• We build a model, that tests this hypothesis quantitatively

8. Modeling Bone-Remodeling
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Methods – Microcrack Simulation
Spatio-temporal, agent-based Monte Carlo simulation
Input:
• Micro-computer comography data of bone microstructure ( Fazzalari 2012)
• Parameters from the literature
• Remodeling volume fraction per year (usually 10% in adults)
• Remodeling volume per event
• New cracks per volume per year (hard to obtain)
Model
Output
• Number of microcracks per volume
• Mean length of microcracks
• Length of the biggest microcrack

9. Modeling Bone-Remodeling
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Methods – Microcrack Simulation –The Model
Cracks
• Add microcracks to the microstructure
Signals
• Send out signals at the positions of microcracks
• Spread signals by diffusion simulation
Remodel
• Find a remodel position
• Remove all microcracks and signals around the
position
1 time step
x 100 years

10. Modeling Bone-Remodeling
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Mineralized bone (trabecula)
Marrow tissue

11. Modeling Bone-Remodeling
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Cracks
• Add microcracks to the microstructure

12. Modeling Bone-Remodeling
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Signals
• Send out signals at the positions of microcracks
• Spread signals by diffusion simulation

13. Modeling Bone-Remodeling
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Remodel
• Find a remodel position

14. Modeling Bone-Remodeling
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Remodel
• Remove all microcracks and signals around the
remodel position

15. Modeling Bone-Remodeling
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Remodel
• Remove all microcracks and signals around the
remodel position

16. Modeling Bone-Remodeling
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repeat

17. Modeling Bone-Remodeling
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Methods – Microcrack Simulation – Variants
With osteocytes and signal-steered remodeling
 Remodeling events with higher probability near
microcracks
Without osteocytes and signal-steered remodeling
 Random positions of remodeling events
• Two different versions of the simulation – to be able to compare the results

18. Modeling Bone-Remodeling
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Results

19. Modeling Bone-Remodeling
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Results

20. Modeling Bone-Remodeling
1
10
100
1000
10000
100000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
cracklengthsuminmm/mm^3
remodeling volume fraction per year
mean cracklength over remodeling volume fraction
Random
Steered
21.04.2015 20UNIVERSITÄT ROSTOCK | Institut für Informatik | Systems Biology and Bioinformatics Rostock | www.sbi.uni-rostock.de
Results
~3 times more remodeling events are necessary to keep the bone on the same health without directed remodeling

21. Modeling Bone-Remodeling
1
1001
2001
3001
4001
5001
6001
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
meanmaximumcracklengthin
mm/mm^3
remodeling volume fraction per year
mean maximum cracklength over remodeling volume fraction
Random
Steered
21.04.2015 21UNIVERSITÄT ROSTOCK | Institut für Informatik | Systems Biology and Bioinformatics Rostock | www.sbi.uni-rostock.de
Results
The effect is even more extreme on the maximum crack-length ~6 times more efficient

22. Modeling Bone-Remodeling
21.04.2015 23UNIVERSITÄT ROSTOCK | Institut für Informatik | Dept. Of Systems Biology and Bioinformatics | www.sbi.uni-rostock.de
Discussion 1
1
10
100
1000
10000
100000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
remodeling volume fraction per year
mean cracklength over remodeling volume fraction
Random
Steered
Strange artifact in the steered simulation:
Especially good bone health at remodeling rates
of 10% per year!
Why?
• Random result?
• No. Simulation was repeated often
• Error in simulation?
• …probably not
• Negative influence of remodeling events on signal distribution – 10% rate may be an
optimum value?
10%/year is assumed as the actual turnover rate in adult humans – but that is probably a
coincidence
Needs further investigation

23. Modeling Bone-Remodeling
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Discussion 2 & Conclusion
We showed: Guided bone remodeling is much more efficient than random bone remodeling!
Widely believed: The role of osteocytes is to guide the remodelling process. Our simulation shows why guidance is
needed.
It remains unclear wether osteocytes can sense mechanical strain
We showed: Microcrack-sensing is a sufficient mechanism to steer the remodeling locations

24. Modeling Bone-Remodeling
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Outlook – What to do next
Combine with FE-Analysis
• Do osteocytes sense mechanical strain?
• Will the remodeling be even more efficient
compared to microcrack-sensing?

25. Modeling Bone-Remodeling
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Outlook
• Investigate strange behaviour at 10% turnover rate
• Include variable parameters over lifetime
• Compare results to FE-coupled modell
• Include spatial remodeling into the model
• Compare spatial structure to experimental data

26. Modeling Bone-Remodeling
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Thank you
for your attention and useful ideas!

27. Bibliography
 Aarden, E.M., Nijweide, P.J. & Burger, E.H., 1994. Function of osteocytes in bone. Journal of
cellular biochemistry, 55(3), pp.287–299.
 Heaney, R.P., Recker, R. & Saville, P., 1978. Menopausal changes in bone remodeling. The
Journal of laboratory and clinical medicine, 92(6), pp.964–970.
 Lanyon, L., 1993. Osteocytes, strain detection, bone modeling and remodeling. Calcified tissue
international, 53(1), pp.S102–S107.
 Nakashima, T. et al., 2011. Evidence for osteocyte regulation of bone homeostasis through
RANKL expression. Nature medicine, 17(10), pp.1231–1234.
 Verborgt, O., Gibson, G.J. & Schaffler, M.B., 2000. Loss of osteocyte integrity in association with
microdamage and bone remodeling after fatigue in vivo. Journal of Bone and Mineral Research,
15(1), pp.60–67.
 Nicola L. Fazzalari 2012, , Brianna L. Martin, Murk J. Bottema, Tammy M. Cleek, Arash Badiei. A
model for the change of cancellous bone volume and structure over time. Mathematical
Biosciences. 240 132–140
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