Living organisms are complex, curious and "philosophical". Complex because the number of components and variables that make up life is extremely huge. Curious because no one completely understands how the entire "system" works so perfectly. "Philosophical" because it is very difficult to define "LIFE".

Systems Biology

The area of Systems Biology is an approach to understanding “life” (biological system) by integrating isolated components of a cell or organism.

Figure 1. Systems Biology integrates information about genes, proteins, cells, tissues, etc by using mathematical models and methods.

Systems Biology tries to integrate the biological knowledge and to understand how the genes, gene products, cells, tissues etc act together in order to make up life. Here, mathematical/computational/statistical models promise to be the key in advancing this comprehension (Figure 1). The huge and still increasing amount of biological data, and the complexity of a living organism that cannot be understood only by studying each part explain the demand for a systematic approach.

Cancer Systems Biology

Cancer is an extremely complex, heterogeneous disease and is the cause of about 13% of deaths around the world. A Systems Biology approach is essential to the understanding of the set of genes and pathways that are active and essential for cancer development, and to create effective therapeutic drugs.

The main aim of Cancer Systems Biology consists on providing a systems-level understanding of cancer via: (1) the generation of multidimensional data such as genomic, epigenetic, transcriptomic, proteomic, metabolomic and clinical information; (2) sophisticated mathematical/computational/statistical methods that can provide insights into the multidimensional data (identification of key genes and/or pathways) by constructing, analyzing and modeling realistic network models of tumors and (3) experimental techniques to confirm and validate the hypothesis generated by the in silico analysis in cancer cell lines and in vivo models (Figure 2).

Figure 2. Cancer Systems Biology. (1) The multidimensional data composed of genomic, epigenetic, transcriptomic, proteomic, metabolomic and clinical information. (2) Mathematical, computational and statistical methods that model and integrate the biological information. (3) Confirmation of the hypothesis generated by the in silico anaylsis by sophisticated biological experiments.

Ultimately, it is expected to achieve what is called as personalized medicine, i.e., the development of individualized treatment protocols that take into account each patient’s unique genetic profile (SNPs and mutations, for instance), allowing the treatment that best fits to the specific needs of both an unique individual (different genetic profile) or a subpopulation of patients with common genetic backgrounds.

In this context, our research focuses on developing cutting-edge mathematical/statistical techniques that combined to genome-scale measurements (genomic, transcriptomic, proteomic, and metabolomic) allow the construction of computer-aided models of cellular process and disease.

In particular, we are interested in (but not limited to) identifying and understanding Granger causality and information flow (time series analysis) between gene regulatory process; machine learning approaches (eg. Support Vector Machines, Linear Discriminant Analysis) that are able to identify potential biomarkers and interpret them; and graph theory to understand the topology of gene networks.

By integrating knowledge of diverse fields of Science such as Mathematics, Computer Science, Statistics, Medicine and Biology, our ultimate goal consists on understanding the cancer as a "mathematical-biological system" and provide breakthroughs to this hard path to the complete cure of cancer.

If you are highly motivated and interested in joining in multidisciplinary and challenging projects do not hesitate to contact me :-)