In Silico Bone Health Prognosis

Several large-scale computational models for biological processes and mechanical properties of bone health and disease already exist, but to model the effects of treatments on bone in more detail, they need to be merged, extended, and compared to in vivo patient measurements. Additionally, current approaches for the modelling of biological pathways rely on many parameters and variables that are not known and cannot be measured, not even in animal models and certainly not in a patient. Even more so, simulation methods that integrate varied clinical data are still missing although they would be required to understand and predict the development of complex disease.

Specifically, one of the aims is thus to approach this problem and model bone biology in a discrete manner with so called Boolean network models that allow modelling pathways and simulate alterations within them without the knowledge of missing parameters and variables, and combine them with existing models for mechanical calculations in a unifying in silico bone framework. This involves new theoretical developments and computational implementations followed by a further aim, which will be the comparison of in silico simulation results with in vivo longitudinal measurements in patients. Such comparisons with clinical data are still lacking at this level but are essential for in silico simulations to be accepted for clinical prognosis.