Computational Biology
Research Lines
The main objective of our research group is the development of new strategies to combat infections caused by multidrug-resistant bacteria, in particular gram-negative bacteria. The increase in the emergence and spread of multidrug-resistant pathogens currently constitutes one of the main threats to public health. The shortage of effective antimicrobials for the treatment of infections by multidrug-resistant gram-negative bacteria is particularly alarming, since cases of resistance to practically all antibiotics are not uncommon. Therefore, the discovery of new therapeutic targets and mechanisms of antimicrobial action less prone to the induction of bacterial resistance has become an urgent need. In parallel, the development of effective vaccines can offer an alternative or complementary solution for at-risk population groups. Our research team combines a series of computational and experimental techniques for the identification of antimicrobial targets with novel modes of action, as well as antigens for the development of vaccines that generate the desired type of immune response. Much of this research is carried out in collaboration with the Institute's Bacterial Molecular Genetics group.
More recently, we have initiated a line of research that uses a network biology approach and machine learning models to analyze the mechanisms of action of drugs and identify biomarkers, side effects and new indications. In addition, through the use of real-world data (RWD) we are developing methodologies for conducting in silico clinical trials, for direct drug comparison, patient cohort simulation or optimization of clinical study design.
Specifically, we have the following lines of research:
- Development of new antimicrobial strategies and identification and validation of therapeutic targets
- Identification and validation of T and B cell antigens in bacterial pathogens and rational vaccine design
- Development of databases, methodologies and machine learning models for the analysis of drug mechanisms of action using a network biology approach
- Development of databases, methodologies and machine learning models for the performance of in silico clinical trials
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