Profil

PACHECO Maria

Main Referenced Co-authors
SAUTER, Thomas  (32)
KISHK, Ali  (10)
BINTENER, Tamara Jean Rita  (6)
De Zio, Daniela (4)
Di Leo, Luca (4)
Main Referenced Keywords
Humans (5); Cancer (3); cancer (3); Metabolic Modelling (3); metabolic modelling (3);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (7)
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group) (2)
Luxembourg Centre for Systems Biomedicine (LCSB) (1)
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) (1)
Main Referenced Disciplines
Life sciences: Multidisciplinary, general & others (21)
Biochemistry, biophysics & molecular biology (9)
Geriatrics (2)
Computer science (1)
Human health sciences: Multidisciplinary, general & others (1)

Publications (total 34)

The most downloaded
325 downloads
PACHECO, M. (2016). Fast reconsonstruction of compact context-specific network models [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/29381 https://hdl.handle.net/10993/29381

The most cited

173 citations (OpenCitations)

PACHECO, M. (2016). Fast reconsonstruction of compact context-specific network models [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/29381 https://hdl.handle.net/10993/29381

Di Leo, L., Pagliuca, C., KISHK, A., Rizza, S., Tsiavou, C., Pecorari, C., Dahl, C., PIRES PACHECO, M. I., Tholstrup, R., Brewer, J. R., Berico, P., Hernando, E., Cecconi, F., Ballotti, R., Bertolotto, C., Filomeni, G., Gjerstorff, M. F., SAUTER, T., Lovat, P., ... De Zio, D. (18 June 2024). AMBRA1 levels predict resistance to MAPK inhibitors in melanoma. Proceedings of the National Academy of Sciences of the United States of America, 121 (25), 2400566121. doi:10.1073/pnas.2400566121
Peer Reviewed verified by ORBi

KISHK, A., PIRES PACHECO, M. I., HEURTAUX, T., & SAUTER, T. (27 March 2024). Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. Briefings in Bioinformatics, 25 (3). doi:10.1093/bib/bbae199
Peer Reviewed verified by ORBi

Bintener, T., PIRES PACHECO, M. I., PHILIPPIDOU, D., MARGUE, C., KISHK, A., Del Mistro, G., Di Leo, L., MOSCARDO GARCIA, M., HALDER, R., SINKKONEN, L., De Zio, D., KREIS, S., Kulms, D., & SAUTER, T. (26 July 2023). Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma. Cell Death and Disease, 14 (468). doi:10.1038/s41419-023-05955-1
Peer reviewed

Frias, A., Di Leo, L., Antoranz, A., Nazerai, L., Carretta, M., Bodemeyer, V., Pagliuca, C., Dahl, C., Claps, G., Mandelli, G. E., Andhari, M. D., PIRES PACHECO, M. I., SAUTER, T., Robert, C., Guldberg, P., Madsen, D. H., Cecconi, F., Bosisio, F. M., & De Zio, D. (2023). Ambra1 modulates the tumor immune microenvironment and response to PD-1 blockade in melanoma. Journal for ImmunoTherapy of Cancer, 11 (3). doi:10.1136/jitc-2022-006389
Peer Reviewed verified by ORBi

PIRES PACHECO, M. I., Ji, J., Prohaska, T., MOSCARDO GARCIA, M., & SAUTER, T. (2022). scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data. Metabolites.
Peer reviewed

DIDIER, J., DE LANDTSHEER, S., PIRES PACHECO, M. I., KISHK, A., SCHNEIDER, J., Demuth, I., & SAUTER, T. (26 October 2022). Improving Machine Learning-based Prediction of Frailty in Elderly People with Digital Wearables : Data from the Berlin Aging Study II (BASE-II) [Poster presentation]. European Digital Medicine Conference Luxembourg 2022, Belval, Luxembourg.

DIDIER, J., DE LANDTSHEER, S., PIRES PACHECO, M. I., KISHK, A., SCHNEIDER, J., Demuth, I., & SAUTER, T. (09 October 2022). Machine learning-based prediction of frailty in elderly people : Data from the Berlin Aging Study II (BASE-II) [Poster presentation]. 21st International Conference on Systems Biology, Berlin, Germany.

MOSCARDO GARCIA, M., PIRES PACHECO, M. I., & SAUTER, T. (2022). Integration of external biomass reactions into existing metabolic models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/52225.

TERNES, D., TSENKOVA, M., POZDEEV, V., MEYERS, M., KONCINA, E., ATATRI, S., SCHMITZ, M., KARTA, J., SCHMOETTEN, M., Heinken, A., RODRIGUEZ, F., Delbrouck, C., GAIGNEAUX, A., GINOLHAC, A., Dan Nguyen, T. T., Grandmougin, L., Frachet-Bour, A., Martin-Gallausiaux, C., PIRES PACHECO, M. I., ... LETELLIER, E. (2022). The gut microbial metabolite formate exacerbates colorectal cancer progression. Nature Metabolism. doi:10.1038/s42255-022-00558-0
Peer reviewed

SAUTER, T., Bintener, T., KISHK, A., PRESTA, L., Prohaska, T., Guignard, D., Zeng, N., Cipriani, C., Arshad, S., Pfau, T., MARTINS CONDE, P., & PIRES PACHECO, M. I. (2022). Project-based learning course on metabolic network modelling in computational systems biology. PLoS Computational Biology, 18 (1), 1009711. doi:10.1371/journal.pcbi.1009711
Peer Reviewed verified by ORBi

Cipriani, C., PIRES PACHECO, M. I., KISHK, A., Wachich, M., ABANKWA, D., SCHAFFNER-RECKINGER, E., & SAUTER, T. (2022). Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay. Pharmaceuticals (Basel, Switzerland), 15 (2). doi:10.3390/ph15020179
Peer reviewed

Bintener, T., PIRES PACHECO, M. I., KISHK, A., DIDIER, J., & SAUTER, T. (2022). Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS. In Methods in Molecular Biology (pp. 221-240). Clifton, N.J., United States: Springer. doi:10.1007/978-1-0716-2513-2_17
Peer reviewed

KISHK, A., PIRES PACHECO, M. I., HEURTAUX, T., SINKKONEN, L., PANG, J., Fritah, S., NICLOU, S., & SAUTER, T. (2022). Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells, 11 (16). doi:10.3390/cells11162486
Peer Reviewed verified by ORBi

MARTINS CONDE, P., Pfau, T., PIRES PACHECO, M. I., & SAUTER, T. (2021). A dynamic multi-tissue model to study human metabolism. NPJ Systems Biology and Applications, 7 (1), 5. doi:10.1038/s41540-020-00159-1
Peer Reviewed verified by ORBi

Di Leo, L., Bodemeyer, V., Bosisio, F. M., Claps, G., Carretta, M., Rizza, S., Faienza, F., Frias, A., Khan, S., Bordi, M., PIRES PACHECO, M. I., Di Martino, J., Bravo-Cordero, J. J., Daniel, C. J., Sears, R. C., Donia, M., Madsen, D. H., Guldberg, P., Filomeni, G., ... Cecconi, F. (2021). Loss of Ambra1 promotes melanoma growth and invasion. Nature Communications, 12 (1), 2550. doi:10.1038/s41467-021-22772-2
Peer Reviewed verified by ORBi

KISHK, A., PIRES PACHECO, M. I., & SAUTER, T. (2021). DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling. iScience, 24 (11), 103331. doi:10.1016/j.isci.2021.103331
Peer reviewed

MOSCARDO GARCIA, M., PIRES PACHECO, M. I., BINTENER, T. J. R., PRESTA, L., & SAUTER, T. (2021). Importance of the biomass formulation for cancer metabolic modeling and drug prediction. iScience, 24 (10), 103110. doi:10.1016/j.isci.2021.103110
Peer Reviewed verified by ORBi

SAUTER, T., & PIRES PACHECO, M. I. (2020). Testing informed SIR based epidemiological model for COVID-19 in Luxembourg. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/43970. doi:10.1101/2020.07.21.20159046

BINTENER, T. J. R., PIRES PACHECO, M. I., & SAUTER, T. (2020). Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochemical Society Transactions. doi:10.1042/BST20190867
Peer Reviewed verified by ORBi

Rampler, E., Egger, D., Schoeny, H., Rusz, M., PACHECO, M., Marino, G., Kasper, C., & Koellensperger, G. (2019). The Power of LC-MS Based Multiomics: Exploring Adipogenic Differentiation of Human Mesenchymal Stem/Stromal Cells. Molecules. doi:10.3390/molecules24193615
Peer Reviewed verified by ORBi

PACHECO, M., BINTENER, T. J. R., & SAUTER, T. (2019). Towards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data. In Automated Reasoning for Systems Biology and Medicine. Springer. doi:10.1007/978-3-030-17297-8_15

PACHECO, M., BINTENER, T. J. R., TERNES, D., Kulms, D., HAAN, S., LETELLIER, E., & SAUTER, T. (May 2019). Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine, 43 (May 2019), 98-106. doi:10.1016/j.ebiom.2019.04.046
Peer Reviewed verified by ORBi

GREENHALGH, K., RAMIRO GARCIA, J., Heinken, Ullmann, P., BINTENER, T. J. R., PACHECO, M., Baginska, J., SHAH, P., FRACHET BOUR, A., HALDER, R., FRITZ, J., SAUTER, T., Thiele, I., HAAN, S., LETELLIER, E., & WILMES, P. (30 April 2019). Integrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells. Cell Reports, 27, 1621–1632. doi:10.1016/j.celrep.2019.04.001
Peer Reviewed verified by ORBi

PACHECO, M., BINTENER, T. J. R., & SAUTER, T. (2019). Towards the network-based prediction of repurposed drugs using patient-specific metabolic models. EBioMedicine. doi:10.1016/j.ebiom.2019.04.017

PACHECO, M., & SAUTER, T. (2018). The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models. In M. Fondi, Metabolic Network Reconstruction and Modeling. Springer.
Peer reviewed

PACHECO, M., & SAUTER, T. (2018). The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models. Methods in Molecular Biology, (1716), 101-110. doi:10.1007/978-1-4939-7528-0_4
Peer reviewed

PACHECO, M. (2016). Fast reconsonstruction of compact context-specific network models [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/29381

PACHECO, M., PFAU, T., & SAUTER, T. (2016). Benchmarking procedures for high-throughput context specific reconstruction algorithms. Frontiers in Physiology. doi:10.3389/fphys.2015.00410
Peer Reviewed verified by ORBi

PACHECO, M., JOHN, E.* , Kaoma, T., Heinäniemi, M., Nicot, N., Vallar, L., BUEB, J.-L., SINKKONEN, L., & SAUTER, T. (19 October 2015). Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network. BMC Genomics, 16 (809). doi:10.1186/s12864-015-1984-4
Peer Reviewed verified by ORBi
* These authors have contributed equally to this work.

PFAU, T., PACHECO, M., & SAUTER, T. (2015). Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond. Briefings in Bioinformatics. doi:10.1093/bib/bbv100
Peer Reviewed verified by ORBi

HILLJE, A.-L., Beckmann, E., PAVLOU, M. A., Jäger, C., PACHECO, M., SAUTER, T., SCHWAMBORN, J. C., & Lewejohann, L. (2015). The neural stem cell fate determinant TRIM32 regulates complex behavioral traits. Frontiers in Cellular Neuroscience. doi:10.3389/fncel.2015.00075
Peer Reviewed verified by ORBi

VLASSIS, N., PACHECO, M., & SAUTER, T. (January 2014). Fast reconstruction of compact context-specific metabolic network models. PLoS Computational Biology, 10 (1), 1003424. doi:10.1371/journal.pcbi.1003424
Peer Reviewed verified by ORBi

VLASSIS, N., PACHECO, M., & SAUTER, T. (2013). Fast reconstruction of compact context-specific metabolic network models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/11095.

VLASSIS, N., PACHECO, M., & SAUTER, T. (2013). Fastcore: An algorithm for fast reconstruction of context-specific metabolic network models. In Proc. 8th BeNeLux Bioinformatics Conference.
Peer reviewed

Contact ORBilu