Coupling computational modeling and information processing in biology and medicine is a major challenge for better comprehending structures and functions of living systems. Signal processing should extract the relevant information required to explore complex organization levels, at all space and time scales. Advances coming from applied physics and mathematics are challenged by extremely hot topics in biology and medicine. The biomedical scene has proven to be the most difficult to address due to the fact that biomedical processes involve nonGaussian, nonlinear, and nonstationary components. This paper provides some clues on processing schemes such as time and frequency transforms, blind signal separation, independent component analysis, empirical mode decomposition, particle methods and Kernel methods that may help in lessening the ambiguity about the observed components of the mixtures to be handled and, this way, facilitating their matching with models.