Cerebral palsy (CP) is the most common motor disorder among children, affecting around 1 per 500 newborns. Due to brain damage before the first birthday, children with CP often develop neural, muscular, and skeletal impairments during growth. Common impairments are spasticity, weakness, contractures, and bony torsions. These impairments result in gait deviations, but the underlying mechanisms are not entirely clear, whereas these are relevant for adequate treatment selection. Neuromusculoskeletal modelling is a type of biocomputational simulations that can enhance our mechanistic understanding of how impairments cause gait deviations. It allows us to estimate variables that are difficult or impossible to measure in an experimental setting. Examples of variables that can be estimated are musculotendon lengths, musculotendon forces, and joint contact forces. Additionally, modelling through predictive simulations allows the direct evaluation of cause-effect mechanisms. Ultimately, modelling outcomes may inform treatment selection to improve the management of CP. However, current common modelling approaches lack personalisation, which is particularly relevant for the aberrant neuromusculoskeletal system in CP. Moreover, predictive simulation approaches with modelled impairments currently lack comparisons with pathology-specific experimental data, limiting the evaluations of the effects of these impairments on gait. Therefore, the overall aim of this thesis was to develop, personalise and apply biocomputational simulations to elucidate how neuromusculoskeletal impairments affect gait in children with CP.