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Research Interests
Julio has a background in modeling signaling networks in the immune system and cancer, and is broadly interested in how large biological data sets can ben interrogated with mathematical models to unveil the molecular basis of disease as a means to address unmet medical needs.
Our research is application-driven and tailored towards producing computational models that integrate diverse data sources to better understand and treat diseases. Because of this, we collaborate closely with experimental groups. A major emphasis is to build context-specific models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine existing biochemical knowledge with different types of large scale data. We believe that this biological knowledge can be instrumental to move from pure correlation to causation in large data sets, and thereby identify the molecular processes that underlie specific phenomena.
We develop and apply methods to extract mechanistic features from diverse omics data, recently also for single-cell data. We then combine these multi-omics data sets into causal networks. Finally, we build dynamic models of specific subsystems using logic formalisms that we can analyze and simulate to predict the effect of new perturbations.
We apply these strategies in the context of many disease conditions. Particular areas of interest for us are cancer, in particular large-scale drug screenings, fibrosis (in particular in kidney, heart, and liver), and co-morbidities in heart failure.
While our research is driven by applications, we develop open-source computational tools that share freely with the scientific community.
Finally, we support scientific crowdsourcing, specifically collaboratives competitions, through the DREAM challenges.
Julio has a background in modeling signaling networks in the immune system and cancer, and is broadly interested in how large biological data sets can ben interrogated with mathematical models to unveil the molecular basis of disease as a means to address unmet medical needs.
Our research is application-driven and tailored towards producing computational models that integrate diverse data sources to better understand and treat diseases. Because of this, we collaborate closely with experimental groups. A major emphasis is to build context-specific models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine existing biochemical knowledge with different types of large scale data. We believe that this biological knowledge can be instrumental to move from pure correlation to causation in large data sets, and thereby identify the molecular processes that underlie specific phenomena.
We develop and apply methods to extract mechanistic features from diverse omics data, recently also for single-cell data. We then combine these multi-omics data sets into causal networks. Finally, we build dynamic models of specific subsystems using logic formalisms that we can analyze and simulate to predict the effect of new perturbations.
We apply these strategies in the context of many disease conditions. Particular areas of interest for us are cancer, in particular large-scale drug screenings, fibrosis (in particular in kidney, heart, and liver), and co-morbidities in heart failure.
While our research is driven by applications, we develop open-source computational tools that share freely with the scientific community.
Finally, we support scientific crowdsourcing, specifically collaboratives competitions, through the DREAM challenges.
研究兴趣
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Alberto Valdeolivas, Bettina Amberg,Nicolas Giroud,Marion Richardson,Eric J. C. Gálvez, Solveig Badillo, Alice Julien-Laferrière,Demeter Túrós,Lena Voith von Voithenberg,Isabelle Wells, Benedek Pesti,Amy A. Lo,
Mira L Burtscher,Stephan Gade,Martin Garrido-Rodriguez,Anna Rutkowska,Thilo Werner,H Christian Eberl, Massimo Petretich, Natascha Knopf, Katharina Zirngibl,Paola Grandi,Giovanna Bergamini,Marcus Bantscheff,
Molecular systems biologyno. 4 (2024): 458-474
biorxiv(2024)
TOXICOLOGICAL SCIENCESno. 1 (2024): 14-30
Magi Andorra,Ana Freire, Irati Zubizarreta,Nicole Kerlero de Rosbo,Steffan D. Bos,Melanie Rinas,Einar A. Høgestøl, Sigrid A. de Rodez Benavent,Tone Berge, Synne Brune-Ingebretse,Federico Ivaldi,Maria Cellerino,
Journal of Neurologyno. 3 (2024): 1133-1149
biorxiv(2024)
Physiology (Bethesda, Md.)no. 3 (2024): 0-0
Stijn N. R. Fuchs, Ursula S. A. Stalmann,Inge A. M. Snoeren,Eric Bindels,Stephani Schmitz,Bella Banjanin,Remco M. Hoogenboezem,Stanley van Herk, Mohamed Saad, Wencke Walter, Torsten Haferlach, Lancelot Seillier,
BLOOD ADVANCESno. 3 (2024): 766-779
Metabolic engineering (2024): 297-298
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