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.
Pugsley et al. (Wed,) studied this question.