Determining the most important cellular characteristics for fracture healing using design of experiments methods.

Computational models are employed as tools to investigate possible mechanoregulation pathways for tissue differentiation and bone healing. However, current models do not account for the uncertainty in input parameters, and often include assumptions about parameter values that are not yet established. The objective of this study was to determine the most important cellular characteristics of a mechanoregulatory model describing both cell phenotype-specific and mechanobiological processes that are active during bone healing using a statistical approach. The computational model included an adaptive two-dimensional finite element model of a fractured long bone. Three different outcome criteria were quantified: (1) ability to predict sequential healing events, (2) amount of bone formation at early, mid and late stages of healing and (3) the total time until complete healing. For the statistical analysis, first a resolution IV fractional factorial design (L(64)) was used to identify the most significant factors. Thereafter, a three-level Taguchi orthogonal array (L(27)) was employed to study the curvature (non-linearity) of the 10 identified most important parameters. The results show that the ability of the model to predict the sequences of normal fracture healing was predominantly influenced by the rate of matrix production of bone, followed by cartilage degradation (replacement). The amount of bone formation at early stages was solely dependent on matrix production of bone and the proliferation rate of osteoblasts. However, the amount of bone formation at mid and late phases had the rate of matrix production of cartilage as the most influential parameter. The time to complete healing was primarily dependent on the rate of cartilage degradation during endochondral ossification, followed by the rate of cartilage formation. The analyses of the curvature revealed a linear response for parameters related to bone, where higher rates of formation were more beneficial to healing. In contrast, parameters related to fibrous tissue and cartilage showed optimum levels. Some fibrous connective tissue- and cartilage formation was beneficial to bone healing, but too much of either tissue delayed bone formation. The identified significant parameters and processes are further confirmed by in vivo animal experiments in the literature. This study illustrates the potential of design of experiments methods for evaluating computational mechanobiological model parameters and suggests that further experiments should preferably focus at establishing values of parameters related to cartilage formation and degradation.