128025 20260217205502.0 doi 10.1007/s40571-023-00639-1 sideral 135209 ART-2024-135209 eng Gonçalves, Inês G. Neurorosettes: a novel computational modelling framework to investigate the Homer-Wright rosette formation in neuroblastoma 2024 Access copy available to the general public Unrestricted Cancer deregulates the interactions between cells and their microenvironment, leading to disrupted architectures. Homer-Wright rosettes, observed in neuroblastoma, comprise radial arrangements of neurons surrounding a meshwork of fibres. Currently, scientists believe that the presence of Homer-Wright rosettes reflects aberrant neuronal differentiation. Nonetheless, additional understanding of how these structures develop is required since neither experimental nor computational research has characterised this mechanism properly. In this work, we propose a mechanics-based computational framework to investigate Homer-Wright rosette formation. Our model depicts neurons as a combination of spherical (cell bodies) and cylindrical (neurites) agents, and it includes proliferation, neuronal differentiation, and adhesion/repulsion dynamics between neurons. We implemented our framework as an open-source user-friendly Python package called neurorosettes that provides real-time rendering of simulation results, making it adequate for general researchers to test and visualize hypotheses of Homer-Wright rosette formation. Furthermore, we present three example use-cases to replicate the emergence of this rosette subtype and investigate how mechanical interactions between neurons and neuronal differentiation may regulate its architecture. Due to the spare amount of experimental data on the formation of these histological patterns, our applications serve primarily as preliminary examples of how our tool can be used and extended. Although our preliminary results show the relevance of mechanical interactions and poor neuronal differentiation to Homer-Wright rosette formation, these factors appear to only predict the initial stages of rosette formation. Overall, our tool can improve the theoretical knowledge on this process and drive the design of new experimental studies to validate model results. info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 2.8 2024 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 29 / 136 = 0.213 2024 Q1 T1 MECHANICS 66 / 171 = 0.386 2024 Q2 T2 0.623 2024 Computational Mechanics 2024 Q1 Computational Mathematics 2024 Q2 Numerical Analysis 2024 Q2 Fluid Flow and Transfer Processes 2024 Q2 Modeling and Simulation 2024 Q2 Civil and Structural Engineering 2024 Q2 5.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion García-Aznar, Jose Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 11 (2024), 565-577 Computational Particle Mechanics 2196-4378 3979609 http://zaguan.unizar.es/record/128025/files/texto_completo.pdf Versión publicada 2580857 http://zaguan.unizar.es/record/128025/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:128025 articulos driver 2026-02-17-20:22:29 ARTICLE