Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations
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Published:2021-07-23
Issue:7
Volume:17
Page:e1009234
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ISSN:1553-7358
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Container-title:PLOS Computational Biology
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language:en
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Short-container-title:PLoS Comput Biol
Author:
de Atauri Pedro,
Tarrado-Castellarnau MíriamORCID,
Tarragó-Celada JosepORCID,
Foguet CarlesORCID,
Karakitsou EffrosyniORCID,
Centelles Josep JoanORCID,
Cascante MartaORCID
Abstract
Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.
Funder
agència de gestió d’ajuts universitaris i de recerca
Instituto de Salud Carlos III
Ministerio de Economía y Competitividad
ministerio de ciencia e innovación
Ministerio de Educación y Formación Profesional
Institució Catalana de Recerca i Estudis Avançats
Publisher
Public Library of Science (PLoS)
Subject
Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics
Cited by
2 articles.
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