Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops.
View Article and Find Full Text PDFMetabolic 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.
View Article and Find Full Text PDFWith most cancer-related deaths resulting from metastasis, the development of new therapeutic approaches against metastatic colorectal cancer (mCRC) is essential to increasing patient survival. The metabolic adaptations that support mCRC remain undefined and their elucidation is crucial to identify potential therapeutic targets. Here, we employed a strategy for the rational identification of targetable metabolic vulnerabilities.
View Article and Find Full Text PDFMetabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces.
View Article and Find Full Text PDFStable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model.
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