The power law approach results in small parameter spaces. For example, the model in contains only ten parameters fitted for a single BMU using experimental data from . A small parameter space requires fewer data for model calibration and validation and enables quick exploration of cell population balances through parameter sweeps. The lower computational complexity also allows researchers to connect the power law model to other biological system models, particularly for physiological homeostasis conditions. However, the empirical nature of power law models leads to ambiguity about which signaling factors control the bone remodeling cycle and how they interact mechanistically. The power law models cannot be easily extended for situations like diseases or treatments when these signals are perturbed outside the conditions used to fit the power law parameters.