The main research lines are:
- 1. Reverse-engineering of gene co-expression netwoks. The pattern of co-expression among genes is a snapshot of the transcription regulatory program that is being executed throughout the experimental conditions measured in a microarray experiment. A way to exploit such information consists of reverse-engineering the network of co-expression associations between the genes. However, most of the available computational methods for such purpose infer pairwise relationships which often cannot distinguish between direct and indirect co-expression associations. Multivariate statistical methods would be the natural choice to overcome such a limitation but the standard available techniques cannot be applied because of the particular dimension of microarray data, where the number of probed genes p is much larger than the number of experimental conditions n. We are developing accurate and robust multivariate methods that work in this setting with p >> n and that will allow us to exploit further the current wealth of microarray experiments to approach the underlying complex combinatorial control on transcriptional and post-transcriptional regulation.
- 2. Identification of functional binding sites. The identification of functional binding sites in DNA/RNA sequences is a fundamental step in order to build a detailed mechanistic model of any particular transcriptional or post-transcriptional regulatory event. We have developed a method for a more accurate computational prediction of binding sites and we are working now on the integration of microarray expression information with such computational procedures, through the reverse-engineering of co-expression networks, in order to approach a more realistic model of the combinatorial control exerted by the regulatory mechanisms.
- 3. Evolution of co-expression networks. Two of the factors that explain a larger portion of the evolutionary rate variation on genes is gene expression breadth and gene expression level. However, it remains open the question on what are the selective forces acting on the gene expression throughout evolution. Likewise, it is not yet well understood how regulatory programs evolve and clues to approach that question would shed light in other important, and controversial, questions like what makes us humans. We believe we can contribute to these questions by studying the evolution of co-expression networks, particularly by exploiting our new approach to reverse-engineer these networks from microarray data.