During the last eight years, our group has designed, implementad and vali<;lated different tools for the automatic processing of ex·perimental data in cardlac physiology. We have worked in close collaboration with researchers in cellular and molecular physiology, and the systems are currently used in different international research laboratories. The tools use advanced methods in image processing, signa! analysis and mechina learning and allow extracting quantitative information both in individual cell experiments and with cell cultures. The present project aims to increase the impact of previous studies, and intends to do so using different approaches: On the one hand, improving our understanding of basic phenomena in the function of cardiac cells at the ce.llular and molecular level. To this extent, we propase a coordinated project involving experimental researchers and experts in biophysical modeling. Our objective is to continue developing advanced characterization tools for experimental data, but now with a more functional approach that allows the integration of relevant data at different scales. Our role, however, will not be limited to creating new analysis tools and improving the existing ones, but also to develop systems to improve the impact on clinical practice and on the design of specific drugs far cardiovascular diseases. Pattern recognition methods will be used to extract relevant information from a cardiology database that includes both clinical and treatment indicators and cellular biomarkers. The development of data visualization tools is also posed far the representation of functional cell indicators. This has a great relevance to achieve that the basic leve! studies can be efficiently transferred in a clinical context both in the diagnostic phases and in the design of pharmacological targets. To do this, our research group incorporates two expert researchers in data visualiiation with an extensiva experience in biomedical applications. The general project presents three general objectives: In the first objective, genetic and functional biomarkers of ventricular fibrillation will be studied. In this first aim we will appty artificial intelligence tools to a cardiology database with clinical parameters and cellular function indicators. The second objective is to identify molecular targets that reduce the spontaneous calcium ralease events in cardiac cells. Tools will be developed to automatically analyze the activation patterns of the ryanodine receptors from fluorescence microscopy images of transgenic mice in which RyR are labeled with fluorescence green protein. Analysis tools will also be developed to characterize calcium propagation fronts in cell cultures. The third objective aims improving the impact of studies at the cellular and molecular leve! in the phases of prevention and treatment of cardiovascular diseases. We will develop new ways of visualizing data to improve the impact of basic studies in a clinical context.
Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016
Programa Estatal de I+D+i Orientada a los Retos de la Sociedad