What is it about?

Glioblastoma is the most aggressive and common brain cancer. Fortunately we have learnt a lot about this cancer in last decades. We know that tumor cells migrate, proliferate, die and consume oxygen and nutrients, and we also know that, under hypoxic conditions, these cells migrate towards more oxygenated regions, close to the blood vessels and then start to fast proliferate near them. This mechanism, known as the go-or-grow behaviour, is indeed one of the reasons of the tumor fast propagation and its invassive capacity. But, how is exactly this transition? Is it sharp? Smooth? Is it reversible or irreversible? In this work we define a method able to unveil this transition, something that enables a better tumor evolution prediction. The method is based on the hybridization of Neural Network technologies with the known physics of the problem. The predictive power of Neural Newtorks is concentrated in the unknown biophysics of the problem, whereas the known phenomena are introduced using mathematical equations. The resulting Netowrk (Physically-Guided Neural Network) looks for a solution that fits the data while satisfying the physics of the problem, enhancing the predictive and explanatory capacity. The presented method demonstrates to be able to unravel this switch between proliferation and migration for different metabolic behaviours and to predict the cell culture evolution using computers for different external stimuli.

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Why is it important?

Although a patient is diagnosed with glioblastoma, each tumor is different and therefore may be addressed differently. This include the use of different drugs, surgery, radiotherapy or chemotherapy treatments. If a patient with glioblastoma came to the hospital. and a biopsy were removed and cultured in a device that subject the tumoral tissue to variable stimuli, the proposed methodology could unravel the specific response of this particular treatment to hypoxia. That is, we would be able to unravel the specific metabolic processes that govern that specific tumor. It is possible to use that knowledge to simulate its response to a certain drug or therapy and thus decide which treatment would be the best one for that particular patient. In that case, we would be taking the first steps towards personalized medicine… and perhaps towards a brighter future for glioblastoma patients.


This article shows how disciplines such as biology, mathematical modelling and Artificial Intelligence can be satisfactorily combined to solve a specific challenging problem, as it is the one of cell metabolic switch in response to tumoral microenvironment stimuli. Interdisciplinarity plays a fundamental role in a world increasingly dominated by artificial intelligence methods, in which biological interpretability play a fundamental role for science to advance without becoming opaque to the researcher, the physician and the patient.

Jacobo Ayensa-Jiménez
Universidad de Zaragoza

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This page is a summary of: Understanding glioblastoma invasion using physically-guided neural networks with internal variables, PLoS Computational Biology, April 2022, PLOS, DOI: 10.1371/journal.pcbi.1010019.
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