Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

Abstract

Motivation: The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as Flux Balance Analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Results: Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into Flux Balance Analysis predictions of a multi-tissue, diel model of Arabidopsis thaliana's central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C Metabolic Flux Analysis (MFA) compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps, as measured by weighted averaged percent error values, dropped from between 169-180% and 94-103% in high light and low light conditions, respectively, to between 10-12% and 9-11%, depending on the gene expression dataset used. The incorporation of gene expression data into the modeling process also substantially altered the predicted carbon and energy economy of the plant.

Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches

“…These findings could indicate that for these reactions, the fluxes are controlled at the gene expression level [53] . However, other works found that a combination of both omics yields slightly better results [54] , while others point to higher predictive power from proteomics data [55] , [56] . In essence, it seems that the two omics contain relevant information for metabolic modeling and it would probably be best to use a combination of both in order to maximize the amount of available information.…”

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

“…In favorable cases, individual internal fluxes can be quantitatively estimated in vivo using independent methods and compared directly to ones from a predicted flux map to provide a powerful form of validation. For example, in a study from our group 74 the ratio of the cyclic electron flow (CEF) to linear electron flow (LEF) fluxes in photosynthesis predicted by FBA was evaluated against CEF/LEF ratios from fluorescence measurements for validation purposes. Though less specific, the sum of FBA‐predicted values for fluxes that produce and/or consume a product (such as CO 2 ) can also be compared to experimental measurements.…”

A role for fermentation in aerobic conditions as revealed by computational analysis of maize root metabolism during growth by cell elongation

“…Moreover, with appropriate experimental constraints the approach can make accurate predictions about the metabolic phenotype (Cheung et al., 2013). The predictive accuracy of the models can be further refined by incorporating ‘omic’ data into the model optimisation process (Kaste & Shachar‐Hill, 2023; Robaina‐Estévez & Nikoloski, 2017; Scheunemann et al., 2018).…”