ArticleScientific reports2020
Mathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 37 citations in OpenAlex.
- Mimicking tumor hypoxia in a glioblastoma-on-a-chip.Materials today. Bio · 2026Article
- Computational Assessment of Oxygen Availability and Shear Stress in Microfluidic Cell Culture Chambers for Optimized Cell Adhesion.Biosensors · 2026Article
- Glioblastoma stem cells show transcriptionally correlated spatial organization.Communications biology · 2026Article
- Artificial Intelligence-Aided Microfluidic Cell Culture Systems.Biosensors · 2025Review
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- Integrating computational fluid dynamics into organ-on-chip systems: a glioblastoma-centred design and validation framework.Frontiers in bioengineering and biotechnology · 2025Review
- Modeling of brain tumors usingHeliyon · 2024Review
- Tetralol derivative NNC-55-0396 targets hypoxic cells in the glioblastoma microenvironment: an organ-on-chip approach.Cell death & disease · 2024Article
- A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity.Bulletin of mathematical biology · 2023Article
- Understanding glioblastoma invasion using physically-guided neural networks with internal variables.PLoS computational biology · 2022Article
- Going with the Flow: Modeling the Tumor Microenvironment Using Microfluidic Technology.Cancers · 2021Review
- AnSmall methods · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters' values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid "biomimetic in vitro-in silico" platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.
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