The biologists now face with the masses of high dimensional datasets generated from various high-throughput technologies, which are outputs of complex inter-connected biological networks at different levels driven by a number of hidden regulatory signals. So far, many computational and statistical methods such as PCA and ICA have been employed for computing low-dimensional or hidden representations of these datasets, but in most cases the results are inconsistent with underlying real network. In this paper we have employed and compared three linear (PCA and ICA) and non-linear (MLP neural network) dimensionality reduction techniques to uncover these regulatory signals, from outputs of such networks. The three approaches were verified experimentally using the absorbance spectra of a network of seven hemoglobin solutions, and the results revealed the superiority of the MLP NN to PCA and ICA. This study shows the capability of the MLP NN approach to efficiently determine the regulatory components in biological networked systems.