Ikerbasque Research Professor
0000-0002-9926-617XAntonio del Sol obtained his BSc in Physics from the Moscow State University "M.V. Lomonosov" and continued his scientific pursuits by completing his PhD in Mathematical Physics from the National Autonomous University of Mexico.
In 1995, Dr. Del Sol joined the Instituto de Física, UNAM (Mexico) as a postdoctoral researcher and by 1996, he became a full-time researcher and was honored with the “Cátedra Patrimonial de Excelencia, Nivel II” by CONACYT. Following this, Dr. Del Sol pursued further postdoctoral studies at the Protein Design Group at Centro Nacional de Biotecnología (CSIC) in Madrid until 2001. Subsequently, he embarked on a BBSRC Research Fellowship at the Bioinformatics Unit at the Computer Science Department at the University College London, UK.
In 2018, Dr. Del Sol joined CIC bioGUNE as an Ikerbasque Research Professor and Group Leader, leading the Computational Biology Group. His laboratory's primary focus lies in computational systems biology, particularly in the realms of stem cell research, aging, and disease modeling. He is also Professor of Bioinformatics at the University of Luxembourg and head of the Computational Biology group at the Luxembourg Centre for Systems Biomedicine (LCSB).
Awards & recognitions
FNR award for Outstanding Scientific Publication for the research article entitled “Transcriptional synergy as an emergent property defining cell subpopulation identity enables population shift” published in the journal Nature Communications, 2018 (DOI: 10.1038/s41467-018-05016-8) (2019). Fujirebio Award for Drug Research and Development (2007). BBSRC Research Fellowship at the University College London (2003-2001). Cátedra Patrimonial de Excelencia, Nivel II, CONACYT, National Autonomous University of Mexico (1997-1996). Medal of Distinction "Gabino Barreda" issued to the best Ph.D. graduated at the National Autonomous University of Mexico (1994).
Latest Publications
2024
2023
2022
2021
The Computational Biology Group is dedicated to developing computational models spanning various levels of biological organization, including cellular, tissue, and organ levels. Our overarching goal is to address challenges in the fields of stem cell research, aging, and disease modeling. Our computational models employ methodologies from different areas of mathematics, engineering, and physics, and integrate multiple sources of biological information (e.g., transcriptomics, epigenomics, proteomics) to study biological processes at different levels of organization.
Research line 1: Stem cell research and regenerative medicine
Cell therapy, promising for treating diseases by replacing damaged cells, faces a challenge in generating specific cell subpopulations. Our computational platform, based on a gene regulatory network model, identifies optimal conversion factors. Currently applied to projects like in-vitro corneal limbus stem cell generation, it aims to treat patients with corneal injuries. The platform is also used for producing corticospinal tract neuron progenitors for spinal cord injury cases. Predictions for converting cardiac cells are being validated, with potential implications for treating cardiovascular diseases.
Research line 2: Aging and rejuvenation
Quantifying cellular biological age holds promise for discovering rejuvenation strategies. We have developed a multi-tissue RNA clock to assess the biological age of cells based on transcriptional profiles, emphasizing key cellular processes. Additionally, a fibroblast-specific RNA clock predicts rejuvenation factors. In addressing brain aging, we have identified dysregulated pathways and molecules, predicting chemical cocktails for brain rejuvenation. Experimental validation is underway in human cells and animal models. Our goal is to apply this approach to Alzheimer's and Parkinson's disease models as a therapeutic strategy against neurodegenerative diseases linked to aging.
Research line 3: Disease modeling
Addressing the cytokine storm in inflammatory diseases, we identified therapeutic targets by mapping immune responses in COVID-19 patients. This approach will be extended to modulate hyperinflammation in systemic inflammatory response syndrome (SIRS), utilizing computational predictions for drug repurposing. In another initiative, we tackle congenital disorders by predicting malfunctioning transcription factors affecting progenitor cell differentiation. Our method identified determinants in cardiac development and mitochondrial DNA-associated Leigh syndrome, suggesting potential therapeutic targets. Ongoing validation in an EU-funded consortium aims to identify repurposable compounds for treating MILS and related conditions.
Collaborations
George Church (Harvard Medical School, USA), Marius Wernig (Stanford University, USA), Michele de Luca (University of Modena and Reggio Emilia, Modena, Italy), Mark H. Tuszynski (University of California San Diego, USA), Ruben Nogueiras (University Santiago de Compostela, Spain), Anne Grapin-Botton (Max Planck Institute of Molecular Cell Biology and Genetics, Germany), Hongkui Deng (Beijing University, China), Carol Schuurmans (University of Toronto, Canada), Magdalena Götz (Ludwig-Maximilians-Universität München, Germany), Deepak Srivastava (Gladstone Institutes, San Francisco, USA).
Links
Webpage: https://www.uni.lu/lcsb-en/research-groups/computational-biology/people/
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Sascha Jung
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Martín Parga Pazos
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Champi Thusangi Wannige
TECHNICIANS / DOCTORAL CANDIDATES -
Aitor Martínez Perez
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Javier Arcos Hodar
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Aleksi Kauro
Members
POSTDOCTORAL RESEARCHERLatest Publications
A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain
Santamaria, G; Iglesias, C; Jung, S; Hodar, JA; Nogueiras, R; del Sol, A;
Advanced Science
2025-07-14
From rare to more common: The emerging role of omics in improving understanding and treatment of severe inflammatory and hyperinflammatory conditions
Keskitalo, S; Seppänen, MRJ; del Sol, A; Varjosalo, M;
JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY
2025-05-05
KLRG1 identifies regulatory T cells with mitochondrial alterations that accumulate with aging
Soto-Heredero, G; Gabandé-Rodríguez, E; Carrasco, E; Escrig-Larena, JI; de las Heras, MMG; Delgado-Pulido, S; Francos-Quijorna, I; Blanco, EM; Fernández-Almeida, A; Abia, D; Rodríguez, MJ; Fernández-Díaz, ...
Nature Aging
2025-04-30
Ice recrystallization inhibitors enable efficient cryopreservation of induced pluripotent stem cells: A functional and transcriptomic analysis
Mommaerts, K; Okawa, S; Schmitt, M; Kofanova, O; Turner, TR; Ben, RN; Del Sol, A; Mathieson, W; Schwamborn, JC; Acker, JP; Betsou, F;
STEM CELL RESEARCH
2024-12-01
Detecting expressed genes in cell populations at the single-cell level with scGeneXpress
Jung, SS; Barlier, C; Perez, AM; del Sol, A;
BRIEFINGS IN BIOINFORMATICS
2024-10-05
The cell rejuvenation atlas: leveraging network biology to identify master regulators of rejuvenation strategies.
Arcos Hodar, Javier; Jung, Sascha; Soudy, Mohamed; Barvaux, Sybille; Del Sol, Antonio;
Aging
2024-09-09
Pten regulates endocytic trafficking of cell adhesion and Wnt signaling molecules to pattern the retina
Touahri, Y; Hanna, J; Tachibana, N; Okawa, S; Liu, H; David, LA; Olender, T; Vasan, L; Pak, A; Mehta, DN; Chinchalongporn, V; Balakrishnan, A; Cantrup, R; Dixit, R; Mattar, P; Saleh, F; Ilnytskyy, ...
CELL REPORTS
2024-03-27
SinCMat: A single-cell-based method for predicting functional maturation transcription factors
Barvaux, S; Okawa, S; del Sol, A;
STEM CELL REPORTS
2024-02-13
Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq
Raevskiy, M; Yanvarev, V; Jung, SS; Del Sol, A; Medvedeva, YA;
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
2023-04-01
Measuring biological age using a functionally interpretable multi-tissue RNA clock
Jung, SS; Hodar, JA; del Sol, A;
AGING CELL
2023-03-16
Transcriptional and Chromatin Accessibility Profiling of Neural Stem Cells Differentiating into Astrocytes Reveal Dynamic Signatures Affected under Inflammatory Conditions
Pavlou, MAS; Singh, K; Ravichandran, S; Halder, R; Nicot, N; Birck, C; Grandbarbe, L; del Sol, A; Michelucci, A;
CELLS
2023-03-01
A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion
Zheng, ML; Xie, BQ; Okawa, S; Liew, SY; Deng, HK; del Sol, A;
STEM CELL REPORTS
2023-01-10
Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration
Moiseeva, V; Cisneros, A; Sica, V; Deryagin, O; Lai, YW; Jung, SC; Andrés, E; An, J; Segalés, J; Ortet, L; Lukesova, V; Volpe, G; Benguria, A; Dopazo, A; Aznar-Benitah, S; Urano, Y; del Sol, ...
NATURE
2022-12-21
ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells
Zheng, ML; Okawa, S; Bravo, M; Chen, F; Martínez-Chantar, ML; Del Sol, A;
NUCLEIC ACIDS RESEARCH
2022-10-06
Neural network learning defines glioblastoma features to be of neural crest perivascular or radial glia lineages
Hu, YZ; Jiang, YW; Behnan, J; Ribeiro, MM; Kalantzi, C; Zhang, MD; Lou, DH; Häring, M; Sharma, N; Okawa, S; Del Sol, A; Adameyko, I; Svensson, M; Persson, O; Ernfors, P;
SCIENCE ADVANCES
2022-06-10
Combinatorial analysis reveals highly coordinated early-stage immune reactions that predict later antiviral immunity in mild COVID-19 patients
Capelle, CM; Ciré, S; Domingues, O; Ernens, I; Hedin, F; Fischer, A; Snoeck, CJ; Ammerlaan, W; Konstantinou, M; Grzyb, K; Skupin, A; Carty, CL; Hilger, C; Gilson, G; Celebic, A; Wilmes, P; Del ...
CELL REPORTS MEDICINE
2022-04-19
Fostering experimental and computational synergy to modulate hyperinflammation
Potapov, I; Kanneganti, TD; del Sol, A;
TRENDS IN IMMUNOLOGY
2022-01-01
Computational Methods to Identify Cell-Fate Determinants, Identity Transcription Factors, and Niche-Induced Signaling Pathways for Stem Cell Research.
Ali, Muhammad; Ribeiro, Mariana Messias; Del Sol, Antonio;
Methods in molecular biology (Clifton, N.J.)
2022-01-01
Computational modelling of stem cell-niche interactions facilitates discovery of strategies to enhance tissue regeneration and counteract ageing
Potapov, I; García-Prat, L; Ravichandran, S; Muñoz-Cánoves, P; del Sol, A;
FEBS JOURNAL
2021-05-14
Proneural genes define ground-state rules to regulate neurogenic patterning and cortical folding
Han, SS; Okawa, S; Wilkinson, GA; Ghazale, H; Adnani, L; Dixit, R; Tavares, L; Faisal, I; Brooks, MJ; Cortay, V; Zinyk, D; Sivitilli, A; Li, SQ; Malik, F; Ilnytskyy, Y; Angarica, VE; Gao, JH; ...
NEURON
2021-09-16