In our group we are aiming at a quantitative understanding of biological systems to an extent that one is able to predict systemic features and with the hope to rational design and modify their behaviour.  This applies to any system comprising biological components that is more than the mere sum of its components, or, in other words, the addition of the individual components results in systemic properties that could not be predicted by considering the components individually.  By achieving this objective we are aiming at new global understanding and treatment of human diseases in which the target will not be a single molecule but a network.  For this purpose in our group we develop on one hand new software and theoretical approximations to understand complex systems and on the other we do experiments to validate our predictions.

Our group is divided in 3 teams:

Synthetic Biology

Synthetic Biology team aims the quantitative understanding of the minimal organism Mycoplasma pneumoniae to later apply this knowledge for doing rational engineering to develop applications. An interdisciplinary team of more than 15 researchers, working together in Systems and Synthetic Biology approaches made possible, after more of one decade, the characterization at molecular level of this model of minimal organism. By combining “-omics” techniques and modeling approaches one of the main goals of the group is to obtain a computational whole-cell model that could be used as tool for rational engineering of this microorganism as bacterial chassis for lung therapy or vaccination.

Systems Biology

In recent years, high-throughput measurements of genomics, transcriptomics and proteomics have played a major role in advancing a more systems-oriented view of the gene expression pathway. In the Systems Biology sub-team, we apply statistics, machine learning and modeling frameworks to better understand and ultimately modify gene expression and cell signaling. This will improve our understanding of cell- and tissue-type-specific expression and signal transduction. Modeling the quantitative effects of these systems may provide a basis for elucidating the molecular mechanisms of disease.

Structural modeling

The modeling team works in the development of two software tools: FoldX and ModelX.

FoldX is an empirical force field developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX is the calculation of the free energy of a macromolecule based on its high-resolution 3D structure. The FoldX tool-suite allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation.

ModelX is a tool-suite for biomolecular modeling. Its based on the usage of fragment libraries as building blocks developed to predict or reconstruct biomolecules and its interactions. The design of ModelX relies on interacting fragments pairs (of protein, DNA, RNA) standing for an interaction that accurately models structural complexes and predicts the biomolecular binding regions of resolved protein structures. The tool includes a fast statistical force field computed from the distances found in the training datasets (one forcefield for each type of interaction) to quickly evaluate and filter 3D docking models. Docked templates are compatible with FoldX protein design tool-suite to identify the crystallized molecule sequence as the most energetically favorable in the majority of the cases. ModelX can be used to predict interface changes upon protein mutagenesis and by predicting binding regions and binding sequences on proteins crystallized in non bounded configuration, opening perspectives for the engineering of protein-biomolecule interfaces.