Improving productivity of citramalate from CO2 by Synechocystis sp. PCC 6803 through design of experiment
Open accessAbstract
Background: Cyanobacteria have long been suggested as an industrial chassis for the conversion of carbon dioxide to products as part of a circular bioeconomy. The slow growth, carbon fixation rates, and limits of carbon partitioning between biomass and product in cyanobacteria must be overcome to fully realise this industrial potential. Typically, flux towards heterologous pathways is limited by the availability of core metabolites. Citramalate is produced in a single enzymatic step through the condensation of the central metabolites pyruvate and acetyl-CoA; improvements in citramalate productivity can, therefore, be used as a measure of overcoming this limitation. Furthermore, citramalate is a useful biomaterial precursor and provides a route to renewable methyl methacrylate and poly(methyl methacrylate), which is often traded as Perspex or Plexiglas. Results: Here, we describe a phenomenon where the concerted optimisation of process parameters significantly increased citramalate production in Synechocystis sp. PCC 6803. Design of experiment principles were used to determine the optima for each parameter and the interplay between multiple parameters. This approach facilitated a ~ 23-fold increase in citramalate titre from initial unoptimised experiments. The process of scale-up from batch cultures to 0.5, 2, and 5 L photobioreactors is described. At the 2-L scale, citramalate titres from carbon dioxide reached 6.35 g/L with space–time yields of 1.59 g/L/day whilst 5-L PBRs yielded 3.96 ± 0.23 g/L with a productivity of 0.99 ± 0.06 g/L/day. We believe the decrease in productivity from 2-L to 5-L scale was likely due to the increased pathlength and shading for light delivery reducing incident light per cell. However, changes in productivity and growth characteristics are not uncommon when scaling up biotechnology processes and have numerous potential causes. Conclusions: This work demonstrates that the use of a process parameter control regime can ameliorate precursor limitation and enhance citramalate production. Since pyruvate and/or acetyl-CoA give rise to numerous products of biotechnological interest, the workflow presented here could be employed to optimise flux towards other heterologous pathways. Understanding the factors controlling and thus increasing carbon partitioning to product will help progress cyanobacteria as part of a carbon–neutral circular bioeconomy. This is the first study using design of experiment to optimise overall carbon fixation rate and carbon partitioning to product, with the goal of improving the performance of a cyanobacterium as a host for biological carbon capture.
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The Phyco-Flow system integrates optical and spectroscopic sensors that allow biomass concentration to be assessed based on light transmission and absorption in the visible and near-infrared range.
“This system integrates optical and spectroscopic sensors that allow the biomass concentration to be assessed based on light transmission and absorption in the visible and near-infrared range.”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [256]
The Results and discussion section names Phyco-Flow only as an example of a serpentine reactor; it does not describe its sensors. The Materials and methods section uses optical density at 720 nm to assess biomass in different reactors. Searches for Phyco-Flow, spectroscopic sensors, and visible/near-infrared transmission found no passage supporting the claimed features of Phyco-Flow. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPhyco-Flow uses AI algorithms to predict crop growth and automatically regulate CO2 supply, which improves photosynthetic efficiency and biomass accumulation.
“In addition, Phyco-Flow uses AI algorithms to predict crop growth and automatically regulate CO2 supply, which improves photosynthetic efficiency and biomass accumulation [256].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [256]
The Results and discussion section names Phyco-Flow only as an example of a serpentine reactor and says the study’s workflow has not been tested in such reactors. The Methods describe predictive modelling of experimental conditions, but our copy does not say that Phyco-Flow uses AI to predict growth or automatically regulate CO2, or that it improves photosynthetic efficiency and biomass accumulation. No page numbers were provided for these passages. (LLM, conf=0.79)
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Dębowski, M., Kazimierowicz, J., & Zieliński, M. (2025) · Applied Sciences · doi:10.3390/app15137324 · Cited by 30 · see citing works
- Authors
- Faulkner, M., Andrews, F., & Scrutton, N.
- Affiliations
- University of Manchester (GB)
- Corresponding author
- Faulkner, Matthew
- Scrutton, Nigel
- Venue
- Biotechnology for Biofuels and Bioproducts
- Published
- 2024-12-05
- Volume
- 17
- Issue
- 1
- Pages
- 143-143
- ISSN
- 2731-3654 (ISSN-L)
- PMC
- PMC11622482
- OpenAlex
- W4405059060
- Cited by
- 8
- References
- 54
- Citations by year
- 2026: 4 · 2025: 4
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Biotechnology for Biofuels and Bioproducts (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/S01778X/1)
- 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 › Photosynthetic Processes and Mechanisms
- Keywords
- Synechocystis sp. PCC 6803, cyanobacteria, CO2 fixation, process parameter optimization, experimental design, scale-up, photobioreactors, pyruvate, acetyl-CoA, carbon allocation, methyl methacrylate, circular bioeconomy, bioprocess productivity
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