Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

Abstract

13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

Model validation and selection in metabolic flux analysis and flux balance analysis

“…The validation-based approach taken in Ref. 93 93 The generation of MID data in additional labeling experiments to precisely measure all fluxes in a network [28][29][30][31][32][33][34][35] provides the reserved validation datasets needed for the method described in Ref. 93.…”

“…On the experimental side of MFA, there have been advances in designing and implementing parallel labeling experiments, wherein multiple tracers are employed in parallel labeling experiments and the results are simultaneously fit to generate a single 13C‐MFA flux map. This enables more precise estimation of fluxes than experiments with individual tracers or tracer combinations allow 28–35 . Greater resolution in isotopic labeling data through the use of tandem mass spectrometry techniques, which allow for the quantification of positional labeling, can also improve the precision of modeled fluxes, as described in Ref.…”

“…This enables more precise estimation of fluxes than experiments with individual tracers or tracer combinations allow. [28][29][30][31][32][33][34][35] Greater resolution in isotopic labeling data through the use of tandem mass spectrometry techniques, which allow for the quantification of positional labeling, can also improve the precision of modeled fluxes, as described in Ref. 36,37. Recent years have also seen developments in FBA meant to improve the reliability of its predictions.…”

Whither metabolic flux analysis in plants?

“…The ability to resolve some fluxes at a subcellular level, providing quantitative data on parallel cytosolic, mitochondrial, and plastidic fluxes, is an important feature of the method. Technically the method continues to advance ( Beyß et al , 2021 ; Millard et al , 2021 ), but the goal of a high-throughput implementation of the method in plants remains elusive.…”

CHRRT: boosting coordinate hit-and-run with rounding by thinning

“…UCPS has a variety of applications, especially in systems biology, where mass balances under steady state conditions together with physiological constraints induce convex polytopes (De Martino et al, 2015; Theorell and Stelling, 2022). Typical use cases include the unbiased characterization of the solution spaces of metabolic (Schellenberger and Palsson, 2009; Loghmani et al, 2022) and gene networks (Machado et al, 2016), identification of metabolic flux couplings (Heinonen et al, 2019), design of experiments (Schellenberger et al, 2012; Beyß et al, 2021), and assessing the effects of uncertainties in biochemical network formulations (Bernstein et al, 2021; Dinh et al, 2022). Here, genome-scale models (GEMs) are routinely reconstructed from genomic and biochemical information (Robinson et al, 2020), resulting in comprehensive model formulations with high-dimensional polytopes as solution spaces.…”