Abstract
The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.
Highlights
We present unsteady-state flux balance analysis, a constraint-based modeling method and workflow that integrates time-course metabolomics data to predict metabolic flux states for dynamic systems. uFBA and steady-state FBA models were constructed and compared for three dynamic systems: stored human red blood cells (RBCs), stored human platelets, and Saccharomyces cerevisiae during anaerobic batch fermentation and carbon starvation
Metabolomics data is becoming readily available, and there is still a need for tools that can integrate such data into mechanistic models to provide a deeper understanding of systems level metabolic physiology
We present unsteady-state flux balance analysis, a constraint-based modeling method, to study dynamic cellular states. uFBA provides additional utility to existing constraint-based methods that integrate metabolomics data
Summary
We present unsteady-state flux balance analysis (uFBA), a constraint-based modeling method and workflow that integrates time-course metabolomics data to predict metabolic flux states for dynamic systems. uFBA and steady-state FBA ( referred to as FBA) models were constructed and compared for three dynamic systems: stored human red blood cells (RBCs), stored human platelets, and Saccharomyces cerevisiae during anaerobic batch fermentation and carbon starvation. We present unsteady-state flux balance analysis (uFBA), a constraint-based modeling method and workflow that integrates time-course metabolomics data to predict metabolic flux states for dynamic systems. UFBA and steady-state FBA ( referred to as FBA) models were constructed and compared for three dynamic systems: stored human red blood cells (RBCs), stored human platelets, and Saccharomyces cerevisiae during anaerobic batch fermentation and carbon starvation. One classical example of a static system was modeled: Escherichia coli during steady-state exponential growth. We find that for the dynamic systems, inclusion of intracellular metabolomics with uFBA provides different and more accurate predictions than FBA. UFBA predictions for RBC were experimentally validated with isotopic metabolic flux analysis. The static E. coli system served as a negative control, with uFBA and FBA displaying similar predictions
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