Quantitative analysis in systems biology often deals with noisy and complex high-dimensional problems. In genomics, for instance, measurements of gene expression changes are usually obtained through various experimental conditions, and when these conditions correspond to time points, only a few of them are usually available. This is an unfortunate fact, as with small sample sizes it becomes hard to capture any form of dependence structure in the data. Thus, key information about gene co-expression and co-regulation dynamics may be missed preventing from a reliable reconstruction of the underlying gene-gene interaction network. It is often an advantage to be able to exploit the sparsity and achieve the intrinsic dimensionality properties of biological systems under exam. Such noisy high-dimensional systems depend on complex latent dynamics that may be viewed as mixtures of informative sources with unknown statistical distribution and subject to unknown mixing mechanism. Blind source separation techniques, fuzzy rules, embedding principles and entropie measures represent useful methodological tools for disentanglement of the dynamics. We report results from data obtained by perturbation experiments and gene network reconstruction and inference.