The document summarizes the initiatives and research lines of Refbio Fase II at the BioDonostia Health Research Institute. Refbio has unique access to clinical data and diverse omics data through its connection to the Donostia University Hospital and Donostia Technological Park. Its main research lines include computational modeling of biological systems, analysis of biological networks, development of artificial vision and data mining methods, and integration of different data types for studies on stem cells, neurodegenerative diseases, cancer, and aging. Some key results mentioned are studies on transcriptomics, proteomics, methylomics, and microRNA expression as well as algorithm development in areas like metabolic engineering, structural biology, and prediction of DNA motifs and transcription

1. Refbio Fase IIRefbio Fase II
Marcos J. Araúzo Bravo
BioDonostia Health Research Institute
Group Leader Computational Biology and Systems
Biomedicine
BioDonostia initiatives in
clinical BigData and e-Health

2. Refbio Fase IIRefbio Fase II
Strength in BigData and e-Health
Our uniqueness is in the access to the Donostia University Hospital
and the Donostia Technological park allowing us:
•“Access” clinical histories
•Access diverse omics data: gene & μRNA expression, DNA-
methylation, TF-ChIP, histone marks, proteomics
Our strength:
•Rich experience in analysis and modeling biological data
•Wide international collaboration network
•Funding by local, national and international private and public
organizations

3. Refbio Fase IIRefbio Fase II
• Computational modeling of biomedical systems
• Interaction study of biological networks (genetic, epigenetic,
metabolic, proteomic) for perturbation response and dynamics
• Development of artificial vision methods for analysis of cellular
imaging and MRI from static and dynamic data
• Development of data mining methods for medical history
analysis based on artificial intelligence
• Development of software for biomedical sensors
• Integration of “macroscopic” information (medical histories)
with ”microscopic” information (image data) and with
“molecular” information (omics data)
ALL applied to stem cells, cellular reprogramming,
neurodegenerative diseases, cancer and aging
Main research lines

4. Refbio Fase IIRefbio Fase II
Some omics- results
Transcriptomics
Kim et al, Nature, 2008
Kim et al, Cell, 2009
Kim et al, Nature, 2009
Ko et al, Nature, 2010
Han et al, Cell, 2010
Han et al, Nature Cell Biology, 2011
Knochbloch et al, Nature, 2012
Moore et al, Science, 2015
Rao et al, Cell Stem Cell, 2016
Song et al, Cell Stem Cell, 2016
Proteomics
Singhal et al, Cell, 2010
Esch et al, Nature Cell Biology, 2012
Methylomics
Santourlidis et al, Stem Cell Res., 2011
Hargus et al, Cell Reports, 2014
Al-Quraishy, Parasitology Research, 2014
Dhikl et al, J. Steroid Bioch. Mol. Biology, 2015
Dorn et al, Haematologica, 2015
Luu et al, Bioinformatics, 2016
Micro RNA expression
Zaehres et al, Exp. Hematology, 2010
CHiP-Seq
Greber et al, EMBO, 2011
Single-cell omics
Grinberg et al, PNAS, 2013
Ohnishi et al, Nature Cell Biology, 2014
Gerovska & Araúzo-Bravo, Mol. Human Reproduction, 2016

5. Refbio Fase IIRefbio Fase II
Some algorithm results
Metabolic engineering
Metabolic flux analysis
Araúzo-Bravo and Kazuyuki, Journal of Biotechnology, 2003
Zaid et al, FEMS Microbiology Letters, 2004
Zaid et al, Applied Microbiology and Biotecnology Letters, 2004
Peng et al, FEMS Microbiology Letters, 2004
Sarkar et al, Archives. of Microbiology, 2008
Structural biology
DNA proteins and drugs interactions
Protein communications Araúzo-Bravo et al, Journal American Chemical Society, 2005
Ahmad et al, Nucleic Acid Research, 2006
Del Sol et al, Genome Biology, 2007
Araúzo-Bravo et al, Nucleic Acid Research, 2008
Prediction of DNA motifs
TF binding motifs
DNA methylation motifs
Müller-Molina et al, PLoS ONE, 2012
Luu et al, Genome Research, 2013
Luu et al, Bioinformatics, 2016

6. Merci!
¡Gracias!
www.refbio.eu
Marcos J. Araúzo Bravo
marcos.arauzo@biodonostia.org
mararabra@yahoo.co.uk
Programa financiado por el FEDER