“…Our results are interesting in view of the frequently posed question which advantages oscillations may have in biology (Heinrich and Schuster 1996;Dupont and Goldbeter 1998;Gall et al 2000). It has been suggested that lowering some concentrations could be an advantage of oscillations.…”
Section: Discussionmentioning
confidence: 84%
“…As alluded to in the Introduction, the equality property has implications for the decoding of calcium oscillations (for modelling the decoding, see Gall et al 2000;Dupont et al 2003;Schuster et al 2005;Marhl et al 2006;Marhl and Grubelnik 2007). As outlined above, there is a tendency to keep the Ca 2+ concentration low.…”
Nonlinear oscillatory systems, playing a major role in biology, do not exhibit harmonic oscillations. Therefore, one might assume that the average value of any of their oscillating variables is unequal to the steady-state value. For a number of mathematical models of calcium oscillations (e.g. the Somogyi-Stucki model and several models developed by Goldbeter and co-workers), the average value of the cytosolic calcium concentration (not, however, of the concentration in the intracellular store) does equal its value at the corresponding unstable steady state at the same parameter values. The average value for parameter values in the unstable region is even equal to the level at the stable steady state for other parameter values, which allow stability. This holds for all parameters except those involved in the net flux across the cell membrane. We compare these properties with a similar property of the Higgins-Selkov model of glycolytic oscillations and two-dimensional Lotka-Volterra equations. Here, we show that this equality property is critically dependent on the following conditions: There must exist a net flux across the model boundaries that is linearly dependent on the concentration variable for which the equality property holds plus an additive constant, while being independent of all others. A number of models satisfy these conditions or can be transformed such that they do so. We discuss our results in view of the question which advantages oscillations may have in biology. For example, the implications of the findings for the decoding of calcium oscillations are outlined. Moreover, we elucidate interrelations with metabolic control analysis.
“…Our results are interesting in view of the frequently posed question which advantages oscillations may have in biology (Heinrich and Schuster 1996;Dupont and Goldbeter 1998;Gall et al 2000). It has been suggested that lowering some concentrations could be an advantage of oscillations.…”
Section: Discussionmentioning
confidence: 84%
“…As alluded to in the Introduction, the equality property has implications for the decoding of calcium oscillations (for modelling the decoding, see Gall et al 2000;Dupont et al 2003;Schuster et al 2005;Marhl et al 2006;Marhl and Grubelnik 2007). As outlined above, there is a tendency to keep the Ca 2+ concentration low.…”
Nonlinear oscillatory systems, playing a major role in biology, do not exhibit harmonic oscillations. Therefore, one might assume that the average value of any of their oscillating variables is unequal to the steady-state value. For a number of mathematical models of calcium oscillations (e.g. the Somogyi-Stucki model and several models developed by Goldbeter and co-workers), the average value of the cytosolic calcium concentration (not, however, of the concentration in the intracellular store) does equal its value at the corresponding unstable steady state at the same parameter values. The average value for parameter values in the unstable region is even equal to the level at the stable steady state for other parameter values, which allow stability. This holds for all parameters except those involved in the net flux across the cell membrane. We compare these properties with a similar property of the Higgins-Selkov model of glycolytic oscillations and two-dimensional Lotka-Volterra equations. Here, we show that this equality property is critically dependent on the following conditions: There must exist a net flux across the model boundaries that is linearly dependent on the concentration variable for which the equality property holds plus an additive constant, while being independent of all others. A number of models satisfy these conditions or can be transformed such that they do so. We discuss our results in view of the question which advantages oscillations may have in biology. For example, the implications of the findings for the decoding of calcium oscillations are outlined. Moreover, we elucidate interrelations with metabolic control analysis.
“…Distributed control would not allow the activation of some targets to suppress other ones one in a frequency sensitive manner, because they would not -regulated activities should be switched on at elevated Ca 2+ concentration, as has indeed been found experimentally 62−64 and theoretically. 65 Number of Targets. Increasing the number of target types increases the range of temporal dynamics the network can transduce into differential regulation.…”
Diverse physiological processes are regulated differentially by Ca 2+ oscillations through the common regulatory hub calmodulin. The capacity of calmodulin to combine specificity with promiscuity remains to be resolved. Here we propose a mechanism based on the molecular properties of calmodulin, its two domains with separate Ca 2+ binding affinities, and target exchange rates that depend on both target identity and Ca 2+ occupancy. The binding dynamics among Ca 2+ , Mg 2+ , calmodulin, and its targets were modeled with mass-action differential equations based on experimentally determined protein concentrations and rate constants. The model predicts that the activation of calcineurin and nitric oxide synthase depends nonmonotonically on Ca 2+ -oscillation frequency. Preferential activation reaches a maximum at a target-specific frequency. Differential activation arises from the accumulation of inactive calmodulin-target intermediate complexes between Ca 2+ transients. Their accumulation provides the system with hysteresis and favors activation of some targets at the expense of others. The generality of this result was tested by simulating 60 000 networks with two, four, or eight targets with concentrations and rate constants from experimentally determined ranges. Most networks exhibit differential activation that increases in magnitude with the number of targets. Moreover, differential activation increases with decreasing calmodulin concentration due to competition among targets. The results rationalize calmodulin signaling in terms of the network topology and the molecular properties of calmodulin.
“…(11) represents phosphoylation and dephosphorylation process of SNAP-23 catalyzed by PKC and phosphatase. In the previous models of protein phosphorylation driven by Ca 2+ dynamics, the kinase activity is given by the quasi-steady-state form about cytosolic Ca 2+ concentration, and the dynamical characteristics of kinase are neglected [41,42] . In our model, the PKC activity is defined as a variable in Eq.…”
Section: Model Of Mast Cell Degranulation Driven By Ca 2+ Dynamicsmentioning
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