Reference : Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegen...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/52436
Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.
English
Kishk, Ali mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Pires Pacheco, Maria Irene mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
Heurtaux, Tony mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Sinkkonen, Lasse mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Pang, Jun mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)]
Fritah, Sabrina [> >]
Niclou, Simone mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
2022
Cells
11
16
Yes
International
2073-4409
2073-4409
[en] Biomass ; Brain Neoplasms/genetics ; Genome, Human ; Glioma ; Humans ; Neurodegenerative Diseases/genetics ; astrocyte ; brain metabolism ; glioma ; metabolic modelling ; neurodegenerative diseases ; neuron
[en] Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
http://hdl.handle.net/10993/52436

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