Figure 1.
Interactions between bone cell populations.
Figure 2.
System of ordinary differential equations constructed, using the biochemical systems analysis formalism [32]–[36], to model osteocyte-induced targeted bone remodeling.
Table 1.
Definitions of Symbols Used in the Paper.
Table 2.
Parameter Values.
Figure 3.
Dynamics of bone cells during a single event of targeted bone remodeling.
The dynamics of osteocyte (a), pre-osteoblast (b), osteoblast (c), and osteoclast (d) populations during an event of targeted bone remodeling.
Figure 4.
Dynamics of bone volume during a single event of targeted bone remodeling.
Table 3.
Effectiveness of RANKL Expression.
Figure 5.
The steady state bone volume, , as a simultaneous function of the effectiveness of osteocyte paracrine signaling on stromal cell differentiation,
, and pre-osteoblast autocrine signaling for pre-osteoblast proliferation,
.
Figure 6.
The steady state bone volume, , computed as a function of the effectiveness of osteocyte paracrine signaling on stromal cell differentiation,
, with all other parameters held at baseline values.
Figure 7.
The steady state bone volume, , computed as a function of the effectiveness of pre-osteoblast autocrine signaling for pre-osteoblast proliferation,
, with all other parameters held at baseline values.
Figure 8.
We simulate the loss of bone mass associated with over resorption in conjunction with a bone degenerative disease, modeled by a slight increase in the value of with all other parameters set to the baseline values listed in table 2.
Figure 9.
This figure shows simulation results of treating pathological bone remodeling, as simulated in figure 5, via the addition of an anti-sclerostin drug.
This results in a dose-dependent increase in bone mass. Treatment with an anti-sclerostin drug is modeled by modifying the appropriate signaling mechanisms, that is, by modifying the appropriate exponents and
in the power law approximation.