Gonçalves, Daniel M.Henriques, RuiCosta, Rafael S.2024-01-222024-01-222023-10-172001-0370PURE: 81977599PURE UUID: bb9f98a1-8e45-496e-9db2-f91ff7222f50Scopus: 85174449267WOS: 001096771800001PubMed: 37876626PubMedCentral: PMC10590844ORCID: /0000-0002-7539-488X/work/151424027http://hdl.handle.net/10362/162653The authors also wish to acknowledge the European Union's Horizon BioLaMer project under grant agreement number [ 101099487 ]. Publisher Copyright: © 2023 The AuthorsThe accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository [1].141745516engFlux balance analysisGenome-scale modelsMetabolic fluxesOmics dataSupervised machine learningSystems biologyBiotechnologyBiophysicsStructural BiologyBiochemistryGeneticsComputer Science Applicationsjournal article10.1016/j.csbj.2023.10.002https://www.scopus.com/pages/publications/85174449267