The musculoskeletal system in the human and mammalian bodies is characterized by a rich redundancy: A given movement such as level walking through recruitment of muscles can be effectuated in infinitely many ways, as the number of muscles exceeds the number of degrees of freedom, in practice, the joint angles, needed to characterize the motion. The uncontrolled manifold hypothesis states that in typical repetitive tasks, the central nervous system controls only few groups of muscles that are organized in a synergetic way, leaving a significant freedom to adjust the movements if needed, e.g., to secure the joints by co-contractions if the external conditions change; Think about walking on a slippery surface. The muscle recruitment problem in biomechanics is to characterize all possible muscle activation configurations corresponding to an observed movement, and to identify the synergies during the task. Among other things, this helps to get insight into the changed role of muscles in musculo/neurodegenerative states such as Parkinson's disease, and may be useful tool for functional electrical stimulus (FES) applications as well as in treatment of injuries. In this talk, we give an overview of a computational package ""Myobolica"" developed for Bayesian sampling of the muscle activation states, as well as on applications of it.