Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling
Open accessAbstract
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.
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When constrained by transcriptomic or proteomic data, genome-scale metabolic models become condition-specific, enabling more accurate identification of bottlenecks that would not be apparent from any single data layer alone.
“When constrained by transcriptomic or proteomic data, these models become condition-specific, enabling more accurate identification of bottlenecks that would not be apparent from any single data layer alone [163].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [163]
The Introduction says transcriptomic integration can produce condition-specific metabolic flux predictions, supporting that part of the claim. The paper does not establish the same result for proteomic constraints or show that integration identifies bottlenecks more accurately than any single data layer alone. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTranscriptomic data has been integrated with the Synechocystis sp. PCC 6803 GEM to evaluate and improve constraint-based flux prediction methods under photoautotrophic conditions.
“Recent advances have begun to realise this paradigm in practice: transcriptomic data has been integrated with the Synechocystis sp. PCC 6803 GEM to evaluate and improve constraint-based flux prediction methods under photoautotrophic conditions [163]”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [163]
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Walshe, J., & Saha, S. K. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48060638 · Cited by 1 · see citing works
“…with the Synechocystis sp. PCC 6803 GEM to evaluate and improve constraint-based flux prediction methods under photoautotrophic conditions [163], while a dedicated GEM has been reconstructed for the fast-growing strain Synechococcus sp. PCC 11901, enabling dynamic flux balance analysis to…”
- Authors
- Pugsley, T., Hanke, G., & Duffy, C. D. P.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Duffy, Christopher D. P.
- Venue
- Frontiers in Bioinformatics
- Publisher
- Frontiers Media SA
- Published
- 2026-02-04
- Volume
- 6
- Pages
- 1715377-1715377
- ISSN
- 2673-7647 (ISSN-L)
- PMC
- PMC12913417
- OpenAlex
- W7127568331
- Cited by
- 2
- References
- 46
- Citations by year
- 2026: 2
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/R004838/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- genome-scale metabolic models, cyanobacteria, Synechocystis sp. PCC 6803, constraint-based metabolic modeling, metabolic flux prediction, model validation
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