Multiomic Data Integration and Analysis via Model-Driven Approaches

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The increasing importance of the high-throughput technologies in the study of biological systems has transformed molecular biology into a data-rich discipline. Thus, integrating multiple layers of omic data from different biological processes has become one of the major challenges in the scope of systems biology. Metabolomics, proteomics, and transcriptomics provide quantitative measurements of metabolites, proteins and transcripts, respectively. However, inferring metabolic fluxes from these ‘omics’ may lead to erroneous conclusions because there is not a linear relationship between these omics and metabolic fluxes. In this sense, computational modelling of metabolism has become an essential tool for the comprehensive understanding and characterization of cellular processes. This chapter summarizes the state-of-the-art of model-driven multiomics data integration based on two of the most relevant metabolic modelling methods: kinetic and constraint-based modelling. Finally, some of the most relevant methods to integrate transcriptomics, proteomics, and metabolomics into both modelling approaches are summarized.
Original languageEnglish
Book seriesComprehensive Analytical Chemistry
Pages (from-to)447-476
Publication statusPublished - 2018


  • Analytical Chemistry
  • Spectroscopy
  • Constraint-based modelling
  • GEM
  • Kinetic modelling
  • Metabolic model
  • Metabolism
  • Metabolomic
  • Omic data integration
  • Proteomic
  • Tracer-based metabolomics
  • Transcriptomic


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