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Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling
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DOI：10.3389/fbinf.2026.1715377.png)
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Abstract
En 中文
Metabolic modelling has wide-ranging applications; including for the improved production of high-value compounds; understanding complex diseases and analysing microbial community interactions. Integrating transcriptomic data with genome-scale metabolic models is crucial for deepening our understanding of complex biological systems; as it enables the development of models tailored to specific conditions; such as particular tissues; environments; or experimental setups. Relatively little attention has been given to the validation and comparison of such integration methods in predicting intracellular fluxes. While a few validation studies offer some insights; their scope remains limited; particularly for organisms like cyanobacteria; for which little metabolic flux data are available. Cyanobacteria hold significant biotechnological potential due to their ability to synthesise a wide range of high-value compounds with minimal resource inputs. Using existing transcriptomic data; we evaluated different methodological options that can be taken when integrating transcriptomics with a genome-scale metabolic model of Synechocystis sp. PCC 6803 (iSynCJ816); when predicting autotrophic flux distributions. We find METRADE* (using single objective optimisation) to be the best-performing method in cyanobacteria owing to its ability to perform well across both metrics but emphasise the importance of configuration and scaling in achieving these outcomes.
Keywords:
validation
cyanobacteria
central carbon metabolism
cellular phenotypes
Flux Balance Analysis
autotrophic flux distributions
constraint-based metabolic models
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