In recent years, intrinsically disordered proteins have emerged as pivotal actors in biology. Their high prevalence in eukaryotic organisms and their direct and indirect association with a number of human diseases have spurred a considerable high interest in elucidating structural, dynamical and functional behavior. However, their conformational heterogeneity makes investigation difficult for most conventional experimental techniques. On the other hand, molecular dynamics simulations can complement experimental molecular biology investigations by providing a highly detailed picture of the system. Nevertheless, the ability of molecular dynamics simulations to provide accurate models of intrinsically disordered proteins are hindered by force-field inaccuracies, and time-consuming simulations required to obtain converged ensembles. In this thesis, an integrative approach was applied in order to obtain consistency between experimental and back-calculated observables of intrinsically disordered proteins. This approach allowed to directly integrate experimental data with molecular dynamics simulations and as result provide a better agreement with experimental observables. The work done in this thesis can be condensed into two main research projects that are summarized in two research papers. The first project gives a practical introduction to integration by reweighting. By using computationally-generated conformational ensembles of an intrinsically disordered protein as a model, we illustrate how integration with experimental SAXS data and NMR diffusion experiments can be used to derive conformational ensembles in agreement with those experiments. The second project describes the results of extensive research performed on the intrinsically disordered alpha-synuclein protein. Here, two methods that rely on the well-established Bayesian inference principle was used to integrate experimenix tal SAXS data . A combination of NMR diffusion and paramagnetic relaxation enhancement data was used to validate the refined ensembles.