|Dr. Marcos Araúzo Bravo is an Industrial Engineer in Automation and Electronics, Doctor of Industrial Technologies from the Polytechnic University of Cartagena and Doctor in Information Technology in 2003 from the Kyushu Institute of Technology (KIT) in Japan, where he also completed his postdoctoral studies in 2006. From 2006 to 2014 he launched and headed the Computational Biology and Bioinformatics laboratory at the Max Planck Institute for Molecular Biomedicine in Münster (Germany).
From 1998 to 2004 he was Associate Professor of the Polytechnic School, Department of Electromechanical Engineering at the University of Burgos.
Coordinator, leader and participant in myriad international projects (FET CIRCULAR VISION, EraCoSysMed 4D-HEALING), nationally and regionally, he is also a member of CIBERfes (Network Centre for Biomedical Research in Frailty and Healthy Ageing) and of the Excellence Thematic Network for Translational Bioinformatics (TransBioNet).
He has more than 150 publications in journals including Science, Nature, Nature Cell Biology, Nature Chemical Biology, American Chemical Journal, Cell and Cell Stem Cell. More than 10,000 citations in Google Scholar, h-index: 47 and i10 index: 106 (February 2020). He is also the co-inventor of 3 registered patents.
He has directed one doctoral thesis and is currently directing another four.
- Development of computational methods for the analysis and modeling of biological systems and their utilization for elucidating better understanding of stem cells, cellular reprogramming stem cells, diseases and aging mechanisms.
- Study the interaction of biological networks (genetic, epigenetic, metabolic, and proteomic) in terms of their typology, perturbation response and dynamics.
- Development of artificial vision methods for the automatic analysis and characterization of cellular and subcellular structures from static and dynamic images.
- Development of data mining methods for medical histories analysis based on artificial intelligent technics to predict health condition, diseases and aging.
- Synergetic integration of the “macroscopic” information provided by the medical histories data with the “microscopic” information provided by image data with the “molecular” information provided by the omics data for better understanding of human diseases and aging.
- Exploration of how genetic variations between individuals influence their cell biological functions and – ultimately – disease, using a combination of iPSC technology and –omics data.
Main research lines
In mathematical modeling of biological systems
- De novo prediction of genomic regulatory hot spots as building blocks for mathematical models of the cross-talk between genetic and epigenetic networks.
- Biological network analysis. By perturbing network components, analyze the induced changes in their performance to understand the synergistic and antagonistic effects of the perturbations. Developing methods to identify the typology and the dynamics of the biological networks analyzing network properties such as the presence of motifs, and integrating systems engineering tools for the analysis of stability of controllability, robustness, response to perturbation and stochasticity. Application for better understanding of stem cells, cellular reprogramming, disease states, disease progression and aging mechanisms.
- Identification and characterization of regulatory cores in pluripotent networks, in stem cells, in diseases and in aging.
- Development of dynamical models to understand the genetic regulatory networks of pluripotent cells, cellular reprogramming, stem cells, diseases and aging.
- Computational quality control of pluripotent cells by high throughput transcriptomics and epigenomics data analysis.
- Integration of transcriptomics data form different platforms to create big corpus datasets.
- Development of computational tools to exploit high throughput data, integrating omics data of different nature (transcriptomics, Chip-Seq, DNA methylomics, histone marks, microRNA expression and proteomics).
- Implementation of data integrative approaches from different omic technologies to elucidate the cross-talk of the main molecular players of pluripotent cells, stem cells, diseases and aging.
- Develop upstream and downstream statistical/machine learning analysis tools for mining next-generation sequencing data (RNAseq, ChIp-Seq of histone modification, and genome-wide DNA methylation.
- Identification of targets for direct reprogramming
- Identification of biomarkers for cancer research.
- Search of DNA sequence patterns and DNA words for building DNA dictionaries and grammars.
- Development of data mining methods for medical histories based on artificial intelligent strategies to predict health condition, diseases and aging.
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