Version 1
: Received: 13 January 2021 / Approved: 14 January 2021 / Online: 14 January 2021 (18:01:26 CET)
How to cite:
Cubuk, C.; Loucera, C.; Hidalgo, M.; Amadoz, A.; Carbonell-Caballero, J.; Peña-Chilet, M.; Dopazo, J. A Mechanistic Model for Genome-Scale Estimations of Metabolic Activity. Preprints2021, 2021010280. https://doi.org/10.20944/preprints202101.0280.v1
Cubuk, C.; Loucera, C.; Hidalgo, M.; Amadoz, A.; Carbonell-Caballero, J.; Peña-Chilet, M.; Dopazo, J. A Mechanistic Model for Genome-Scale Estimations of Metabolic Activity. Preprints 2021, 2021010280. https://doi.org/10.20944/preprints202101.0280.v1
Cubuk, C.; Loucera, C.; Hidalgo, M.; Amadoz, A.; Carbonell-Caballero, J.; Peña-Chilet, M.; Dopazo, J. A Mechanistic Model for Genome-Scale Estimations of Metabolic Activity. Preprints2021, 2021010280. https://doi.org/10.20944/preprints202101.0280.v1
APA Style
Cubuk, C., Loucera, C., Hidalgo, M., Amadoz, A., Carbonell-Caballero, J., Peña-Chilet, M., & Dopazo, J. (2021). A Mechanistic Model for Genome-Scale Estimations of Metabolic Activity. Preprints. https://doi.org/10.20944/preprints202101.0280.v1
Chicago/Turabian Style
Cubuk, C., María Peña-Chilet and Joaquin Dopazo. 2021 "A Mechanistic Model for Genome-Scale Estimations of Metabolic Activity" Preprints. https://doi.org/10.20944/preprints202101.0280.v1
Abstract
In the last decade, different omic technologies have experiences an exponential technological advancement. However, metabolomics has not followed a similarly vertiginous improving improvement and are far from genomics or transcriptomics in terms of throughput, cost and even accuracy. Therefore, genome-scale in-silico methodologies to estimate metabolic activities from genomic data constitute an active field. The solutions available fall into two extremes: those with few assumptions about the relationships among proteins and metabolites, which are easy-to-use but less accurate and those that account for the complex relationships among molecules and proteins defined in the metabolic pathways, which are more accurate but require mathematical skills. Here, we introduce Metabolica, an algorithm that considers the complex functional relationships among all the molecules and proteins involved in the metabolic pathway analyzed but keeping an easy use that do not require of advanced mathematical skills. Metabolica has been implemented in a freely available software R package. The software inputs transcriptomic data and infers the activities of the reactions that produce the different metabolites in the pathway analyzed. An example shows how detected dysregulated metabolites in several cancers are related to patient survival.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
