The research group works on the modelling and multiscale simulation of the activation process of cardiac tissue, in order to characterise and predict different pathologies. The group has high-resolution simulation software that allows fully synthetic electrocardiograms to be reproduced, with the possibility of simulating different pathologies. Another active line of work in this field is the estimation of the cardiac conduction system using analysis of data acquired during surgical practice.
We associate transcriptomic and metabolic data in plant organs or cell cultures to isolate unknown secondary metabolic enzymes and their transcriptional regulators.
Modeling of multi-scale biological and medical processes from microscopy and medical imaging data by means of ICT tools to increase the understanding of the patho-physiology and improve the diagnosis and treatment of diseases.
We attempted to characterise the MYB family in different plant species by combining genome-wide and plant functional characterisation studies.
Developments in high-performance computing, using advanced computer architectures, for the processing and analysis of methylation in genomic data produced by Next-Generation Sequencing (NGS).
Development of molecular biointeraction models. Computational simulation methods (“in silico” via “Molecular Docking”). Pharmacophores. QSAR.
Functional genomics of lung cancer. Identification of new markers and molecular targets for the treatment of lung cancer using omics technologies (transcriptomics, proteomics, metabolomics). Study of the mechanisms of innate and acquired resistance to tyrosine kinase inhibitors based on the epithelial-mesenchymal transition (EMT) and the cancer stem cell (CSC) phenotype. Regulation of tumour metabolism by oncogenes and suppressor genes in lung cancer.
Multiple sources of medical images are routinely used in clinical practise (MRI, TAC, PET, ECO...) Methods are needed to isolate the organs, analyse their deviation from normality and matching them with other cases or with explorations of the same patient to help doctors in the diagnosis of diseases.
We model dynamic processes using spatial point processes and random closed sets. Descriptors for univariate and bivariate processes with an explicit statistical analysis of the time and tests for hypothesis testing are proposed. Software for simulation and analysis is developed. More info and applications to cell biology in https://www.uv.es/tracs.
