A new GIMME–based heuristic for compartmentalised transcriptomics data integration

• Diego Troitiño-Jordedo ^{[3] [2]} ; Lucas Carvalho ^{[1] [4]} ; David Henriques ^[3] ; Vítor Pereira ^[1] ; Miguel Rocha ^[1] ; Eva Balsa-Canto ^[3]
  1. [1] Universidade do Minho

     Universidade do Minho

     Braga (São José de São Lázaro), Portugal

     
  2. [2] Universidade de Santiago de Compostela

     Universidade de Santiago de Compostela

     Santiago de Compostela, España

     
  3. [3] Biosystems and Bioprocess Engineering Group, IIM–CSIC, Vigo, Spain
  4. [4] Center for Computing in Engineering and Sciences, UNICAMP Campinas, São Paulo, Brazil

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• Localización: Practical applications of computational biology and bioinformatics, 17th International Conference (PACBB 2023) / Miguel Rocha (ed. lit.), Florentino Fernández Riverola (ed. lit.), Mohd Saberi Mohamad (ed. lit.), Ana Belén Gil González (ed. lit.), 2023, ISBN 978-3-031-38078-5, págs. 44-52
• Idioma: inglés
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  • Genome-scale models (GEMs) are structured representations of a target organism’s metabolism based on existing genetic, biochemical, and physiological information. These models store the available knowledge of the physiology and metabolic behaviour of organisms and summarise this knowledge in a mathematical description.Flux balance analysis uses GEMs to make predictions about cellular metabolism through the solution of a constrained optimisation problem. The gene inactivity moderated by metabolism and expression (GIMME) approach further constrains FBA by means of transcriptomics data. The underlying idea is to deactivate those reactions for which transcriptomics is below a given threshold. GIMME uses a unique threshold for the entire cell. Therefore, non-essential reactions can be deactivated, even if they are required to meet the production of a certain external metabolite, because of their low associated transcript expression values.Here, we propose a new approach to enable the selection of different transcriptomics thresholds for different cell compartments or modules, such as cellular organelles and specific metabolic pathways. The approach was compared with the original GIMME in the analysis of a number of examples related to yeast batch fermentation for the production of ethanol from glucose or xylose. In some cases, the original GIMME results in biological unfeasibility, while the compartmentalised version successfully recovered flux distributions.The method is implemented in the python-based toolbox MEWpy and can be applied to other metabolic studies, opening the opportunity to obtain more refined and realistic flux distributions, which explain the connections between genotypes, environment and phenotypes.