000117627 001__ 117627
000117627 005__ 20240319081008.0
000117627 0247_ $$2doi$$a10.1371/journal.pcbi.1010019
000117627 0248_ $$2sideral$$a129189
000117627 037__ $$aART-2022-129189
000117627 041__ $$aeng
000117627 100__ $$0(orcid)0000-0003-2564-6038$$aAyensa Jiménez, J.$$uUniversidad de Zaragoza
000117627 245__ $$aUnderstanding glioblastoma invasion using physically-guided neural networks with internal variables
000117627 260__ $$c2022
000117627 5060_ $$aAccess copy available to the general public$$fUnrestricted
000117627 5203_ $$aMicrofluidic 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.
000117627 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-097257-B-C31$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106099RB-C44
000117627 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000117627 590__ $$a4.3$$b2022
000117627 592__ $$a1.872$$b2022
000117627 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b10 / 55 = 0.182$$c2022$$dQ1$$eT1
000117627 593__ $$aCellular and Molecular Neuroscience$$c2022$$dQ1
000117627 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b18 / 77 = 0.234$$c2022$$dQ1$$eT1
000117627 593__ $$aComputational Theory and Mathematics$$c2022$$dQ1
000117627 593__ $$aEcology$$c2022$$dQ1
000117627 593__ $$aMolecular Biology$$c2022$$dQ1
000117627 593__ $$aGenetics$$c2022$$dQ1
000117627 593__ $$aModeling and Simulation$$c2022$$dQ1
000117627 593__ $$aEcology, Evolution, Behavior and Systematics$$c2022$$dQ1
000117627 594__ $$a7.1$$b2022
000117627 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000117627 700__ $$0(orcid)0000-0003-0088-7222$$aDoweidar, M. H.$$uUniversidad de Zaragoza
000117627 700__ $$aSanz-Herrera, J.
000117627 700__ $$0(orcid)0000-0001-8741-6452$$aDoblare, M.$$uUniversidad de Zaragoza
000117627 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000117627 773__ $$g18, 4 (2022), E1010019[27 pp.]$$pPLoS Comput. Biol.$$tPLOS COMPUTATIONAL BIOLOGY$$x1553-734X
000117627 8564_ $$s2994080$$uhttps://zaguan.unizar.es/record/117627/files/texto_completo.pdf$$yVersión publicada
000117627 8564_ $$s2387059$$uhttps://zaguan.unizar.es/record/117627/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000117627 909CO $$ooai:zaguan.unizar.es:117627$$particulos$$pdriver
000117627 951__ $$a2024-03-18-14:48:38
000117627 980__ $$aARTICLE