Understanding glioblastoma invasion using physically-guided neural networks with internal variables

Ayensa Jiménez, J. (Universidad de Zaragoza) ; Doweidar, M. H. (Universidad de Zaragoza) ; Sanz-Herrera, J. ; Doblare, M. (Universidad de Zaragoza)

Resumen: Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically- Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced by means of nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution for defining the network structure. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine. © 2022 Ayensa-Jiménez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Idioma: Inglés

DOI: 10.1371/journal.pcbi.1010019

Año: 2022

Publicado en: PLOS COMPUTATIONAL BIOLOGY 18, 4 (2022), E1010019[27 pp.]
ISSN: 1553-734X

Factor impacto JCR: 4.3 (2022)

Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 10 / 55 = 0.182 (2022) - Q1 - T1
Categ. JCR: BIOCHEMICAL RESEARCH METHODS rank: 18 / 77 = 0.234 (2022) - Q1 - T1

Factor impacto CITESCORE: 7.1 - Agricultural and Biological Sciences (Q1) - Biochemistry, Genetics and Molecular Biology (Q2) - Neuroscience (Q1) - Computer Science (Q1) - Mathematics (Q1) - Environmental Science (Q1)

Factor impacto SCIMAGO: 1.872 - Cellular and Molecular Neuroscience (Q1) - Computational Theory and Mathematics (Q1) - Ecology (Q1) - Molecular Biology (Q1) - Genetics (Q1) - Modeling and Simulation (Q1) - Ecology, Evolution, Behavior and Systematics (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-097257-B-C31

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)