Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901
Abstract
The ability to convert atmospheric CO 2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.
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A genome-scale metabolic model has been reconstructed for Synechococcus sp. PCC 11901 to guide rational engineering decisions through flux balance analysis.
“A genome-scale metabolic model has also been reconstructed for PCC 11901 to guide rational engineering decisions through flux balance analysis [78].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [78]
Genome-scale metabolic models of cyanobacteria, most extensively developed for Synechocystis sp. PCC 6803, represent complete reconstructions of the metabolic network that can be interrogated using flux balance analysis to predict intracellular flux distributions, identify competing metabolic sinks, and calculate theoretical yield limits under photoautotrophic conditions.
“GEMs of cyanobacteria, most extensively developed for Synechocystis sp. PCC 6803, represent complete reconstructions of the metabolic network that can be interrogated using flux balance analysis to predict intracellular flux distributions, identify competing metabolic sinks, and calculate theoretical yield limits under photoautotrophic conditions [78,162].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [78]
A dedicated GEM has been reconstructed for the fast-growing strain Synechococcus sp. PCC 11901, enabling dynamic flux balance analysis to identify production engineering targets in this high-productivity chassis.
“while a dedicated GEM has been reconstructed for the fast-growing strain Synechococcus sp. PCC 11901, enabling dynamic flux balance analysis to identify production engineering targets in this high-productivity chassis [78].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [78]
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- Authors
- Ravindran, S., Hajinajaf, N., Kundu, P., Comes, J., Nielsen, D. R., Varman, A. M., & Ghosh, A.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Arizona State University (US)
- Corresponding author
- Varman, Arul M.
- Ghosh, Amit
- Venue
- ACS Synthetic Biology
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-09-19
- Volume
- 13
- Issue
- 10
- Pages
- 3281-3294
- ISSN
- 2161-5063 (ISSN-L)
- OpenAlex
- W4402649317
- Cited by
- 14
- References
- 61
- Citations by year
- 2026: 9 · 2025: 4 · 2024: 1
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Synthetic Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Biotechnology, Ministry of Science and Technology, India (BT/PR37958/GET/119/297/2020)
- Scheme for Promotion of Academic and Research Collaboration (SPARC/2019-2020/P1991/SL)
- Department of Science and Technology, Ministry of Science and Technology, India (CRG/2020/002080)
- Division of Chemical, Bioengineering, Environmental, and Transport Systems (2146114)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- genome-scale metabolic models, cyanobacteria, mixotrophic growth, autotrophic growth, heterotrophic growth, TCA cycle, Calvin cycle, quantum yield, metabolic engineering
- MeSH terms
- Models, Biological, Genome, Bacterial, Biomass, Synechococcus, Metabolic Networks and Pathways, Metabolic Engineering
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