In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 4 ( 2022-4-4), p. e1010019-
Abstract:
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 in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. 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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010019
DOI:
10.1371/journal.pcbi.1010019.g001
DOI:
10.1371/journal.pcbi.1010019.g002
DOI:
10.1371/journal.pcbi.1010019.g003
DOI:
10.1371/journal.pcbi.1010019.g004
DOI:
10.1371/journal.pcbi.1010019.g005
DOI:
10.1371/journal.pcbi.1010019.g006
DOI:
10.1371/journal.pcbi.1010019.g007
DOI:
10.1371/journal.pcbi.1010019.g008
DOI:
10.1371/journal.pcbi.1010019.g009
DOI:
10.1371/journal.pcbi.1010019.t001
DOI:
10.1371/journal.pcbi.1010019.t002
DOI:
10.1371/journal.pcbi.1010019.t003
DOI:
10.1371/journal.pcbi.1010019.s001
DOI:
10.1371/journal.pcbi.1010019.s002
DOI:
10.1371/journal.pcbi.1010019.r001
DOI:
10.1371/journal.pcbi.1010019.r002
DOI:
10.1371/journal.pcbi.1010019.r003
DOI:
10.1371/journal.pcbi.1010019.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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