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Strini, E. J., Bertolino, L. T., San Martin, J. A. B., Souza, H. A. O., Pessotti, F., Pinoti, V. F., Ferreira, P. B., De Paoli, H. C., Lubini, G., Del-Bem, L-E., Quiapim, A. C., Mondin, M., Araujo, A. P. U., Eloy, N. B., Barberis, M., & Goldman, M. H. S. (2022). Stigma/Style Cell-Cycle Inhibitor 1, a Regulator of Cell Proliferation, Interacts With a Specific 14-3-3 Protein and Is Degraded During Cell Division. Frontiers in Plant Science, 13, Article 857745. https://doi.org/10.3389/fpls.2022.857745[details]
Barberis, M. (2021). Cyclin/Forkhead-mediated coordination of cyclin waves: an autonomous oscillator rationalizing the quantitative model of Cdk control for budding yeast. Npj Systems Biology and Applications, 7(1), Article 48. https://doi.org/10.1038/s41540-021-00201-w[details]
Barberis, M. (2021). SysMod: the ISCB community for data-driven computational modelling and multi-scale analysis of biological systems. Bioinformatics.
Maissan, P., Mooij, E. J., & Barberis, M. (2021). Sirtuins-Mediated System-Level Regulation of Mammalian Tissues at the Interface between Metabolism and Cell Cycle: A Systematic Review. Biology (Basel), 10(3), Article 194. https://doi.org/10.3390/biology10030194[details]
Puniya, B. L., Amin, R., Lichter, B., Moore, R., Ciurej, A., Bennett, S. J., Shah, A. R., Barberis, M., & Helikar, T. (2021). Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders. Npj Systems Biology and Applications, 7(1), Article 4. https://doi.org/10.1038/s41540-020-00165-3
Verstegen, N. J. M., Ubels, V., Westerhoff, H. V., van Ham, S. M., & Barberis, M. (2021). System-Level Scenarios for the Elucidation of T Cell-Mediated Germinal Center B Cell Differentiation. Frontiers in Immunology, 12, Article 734282. https://doi.org/10.3389/fimmu.2021.734282[details]
Wertheim, K. Y., Puniy, B. L., La Fleur, A., Shah, A. R., Barberis, M., & Helikar, T. (2021). A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Computational Biology, 17(8), Article e1009209. https://doi.org/10.1371/journal.pcbi.1009209[details]
Zorzan, I., López, A. R., Malyshava, A., Ellis, T., & Barberis, M. (2021). Synthetic designs regulating cellular transitions: Fine-tuning of switches and oscillators. Current Opinion in Systems Biology, 25, 11-26. https://doi.org/10.1016/j.coisb.2020.12.002
2020
Abudukelimu, A., Barberis, M., Redegeld, F., Sahin, N., Sharma, R. P., & Westerhoff, H. V. (2020). Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity. Frontiers in Immunology, 10, Article 3091. https://doi.org/10.3389/fimmu.2019.03091[details]
Kolodkin, A. N., Sharma, R. P., Colangelo, A. M., Ignatenko, A., Martorana, F., Jennen, D., Briedé, J. J., Brady, N., Barberis, M., Mondeel, T. D. G. A., Papa, M., Kumar, V., Peters, B., Skupin, A., Alberghina, L., Balling, R., & Westerhoff, H. V. (2020). ROS networks: Designs, aging, Parkinson's disease and precision therapies. Npj Systems Biology and Applications, 6, Article 34. https://doi.org/10.1038/s41540-020-00150-w[details]
Mondeel, T. D. G. A., Ivanov, O., Westerhoff, H. V., Liebermeister, W., & Barberis, M. (2020). Clb3-centered regulations are recurrent across distinct parameter regions in minimal autonomous cell cycle oscillator designs. Npj Systems Biology and Applications, 6, Article 8. https://doi.org/10.1038/s41540-020-0125-0[details]
Mondeel, D. G. A., Holland, P., Nielsen, J., & Barberis, M. (2019). ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast. Nucleic Acids Research, 47(15), 7825–7841. https://doi.org/10.1093/nar/gkz603[details]
van der Zee, L., & Barberis, M. (2019). Advanced Modeling of Cellular Proliferation: Toward a Multi-scale Framework Coupling Cell Cycle to Metabolism by Integrating Logical and Constraint-Based Models. In S. G. Oliver, & J. I. Castrillo (Eds.), Yeast Systems Biology : Methods and Protocols (2nd ed., pp. 365–385). (Methods in Molecular Biology; Vol. 2049). Humana Press. Advance online publication. https://doi.org/10.1007/978-1-4939-9736-7_21[details]
2018
Abudukelimu, A., Barberis, M., Redegeld, F. A., Sahin, N., & Westerhoff, H. V. (2018). Predictable Irreversible Switching Between Acute and Chronic Inflammation. Frontiers in Immunology, 9, Article 1596. https://doi.org/10.3389/fimmu.2018.01596[details]
Barberis, M., Helikar, T., & Verbruggen, P. (2018). Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation. Frontiers in Physiology, 9, Article 879. https://doi.org/10.3389/fphys.2018.00879[details]
Mondeel, D. G. A., Cremazy, F. G. E., & Barberis, M. (2018). GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae. Bioinformatics, 34(12), 2147–2149. https://doi.org/10.1093/bioinformatics/bty052[details]
Puniya, B. L., Todd, R. G., Mohammed, A., Brown, D., Barberis, M., & Helikar, T. (2018). A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage. Frontiers in Physiology, 9, Article 878. https://doi.org/10.3389/fphys.2018.00878[details]
Abudukelimu, A., Mondeel, T. D. G. A., Barberis, M., & Westerhoff, H. V. (2017). Learning to read and write in evolution: from static pseudoenzymes and pseudosignalers to dynamic gear shifters. Biochemical Society Transactions, 45(3), 635-652. https://doi.org/10.1042/BST20160281[details]
Barberis, M., & Verbruggen, P. (2017). Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW). Npj Systems Biology and Applications, 3, Article 26. https://doi.org/10.1038/s41540-017-0028-x[details]
Barberis, M., Todd, R. G., & van der Zee, L. (2017). Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models. FEMS Yeast Research, 17(1), Article fow103. https://doi.org/10.1093/femsyr/fow103[details]
Linke, C., Chasapi, A., González-Novo, A., Al Sawad, I., Tognetti, S., Klipp, E., Loog, M., Krobitsch, S., Posas, F., Xenarios, I., & Barberis, M. (2017). A Clb/Cdk1-mediated regulation of Fkh2 synchronizes CLB expression in the budding yeast cell cycle. Npj Systems Biology and Applications, 3, Article 7. https://doi.org/10.1038/s41540-017-0008-1[details]
Ostrow, A. Z., Kalhor, R., Gan, Y., Villwock, S. K., Linke, C., Barberis, M., Chen, L., & Aparicio, O. M. (2017). Conserved forkhead dimerization motif controls DNA replication timing and spatial organization of chromosomes in S. cerevisiae. Proceedings of the National Academy of Sciences of the United States of America, 114(12), E2411-E2419. Advance online publication. https://doi.org/10.1073/pnas.1612422114[details]
Zhang, Y., Kouril, T., Snoep, J. L., Siebers, B., Barberis, M., & Westerhoff, H. V. (2017). The Peculiar Glycolytic Pathway in Hyperthermophylic Archaea: Understanding Its Whims by Experimentation In Silico. International Journal of Molecular Sciences, 18(4), Article 876. https://doi.org/10.3390/ijms18040876[details]
Westerhoff, H. V., Nakayama, S., Mondeel, T. D. G. A., & Barberis, M. (2015). Systems Pharmacology: An opinion on how to turn the impossible into grand challenges. Drug Discovery Today. Technologies, 15, 23-31. Advance online publication. https://doi.org/10.1016/j.ddtec.2015.06.006[details]
2013
Linke, C., Klipp, E., Lehrach, H., Barberis, M., & Krobitsch, S. (2013). Fkh1 and Fkh2 associate with Sir2 to control CLB2 transcription under normal and oxidative stress conditions. Frontiers in Physiology, 4, Article 173. https://doi.org/10.3389/fphys.2013.00173[details]
Supady, A., Klipp, E., & Barberis, M. (2013). A variable fork rate affects timing of origin firing and S phase dynamics in Saccharomyces cerevisiae. Journal of Biotechnology, 168(2), 174-184. https://doi.org/10.1016/j.jbiotec.2013.06.022[details]
2015
Naldi, A., Monteiro, P. T., Müssel, C., the Consortium for Logical Models and Tools, Kestler, H. A., Thieffry, D., Xenarios, I., Saez-Rodriguez, J., Helikar, T., Chaouiya, C., Albert, R., Barberis, M., Calzone, L., Chasapi, A., Cokelaer, T., Crespo, I., Dorier, J., Dräger, A., Hernandez, C., ... Zañudo, J. G. T. (2015). Cooperative development of logical modelling standards and tools with CoLoMoTo. Bioinformatics, 31(7), 1154-1159. https://doi.org/10.1093/bioinformatics/btv013[details]
2023
Verstegen, N. J. M. C. (2023). Going beyond your form: T-cell dependent B-cell fate determination. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
Chapter 3: Exploring possible interactions of the cell cycle engine with metabolic enzymes in budding yeast(embargo until 28 June 2025)
Chapter 4: Minimal computer model of the cell cycle reveals the network underlying timely Clb2 expression in budding yeast: Predictions and preliminary validation(embargo until 28 June 2025)
Chapter 5: Gear shifting in hyperthermophylic Archaea(embargo until 28 June 2024)
Chapter 6: The peculiar glycolytic pathway in hyperthermophylic Archaea: Understanding its whims by experimentation in silico(embargo until 28 June 2024)
Mondeel, T. D. G. A. (2022). Waves, ChIPs, GEMMs, gears, markers and maps: Computational systems biology from cell cycle oscillations to metabolic fluxes. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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