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Müller et al. 2019
Towards a quantitative assessment of inorganic carbon cycling in photosynthetic microorganisms
Complete version of the model will be available soon on newly rebuilt version of the webpage.
Photosynthetic organisms developed various strategies to mitigate high light stress. For instance, aquatic organisms are able to spend excessive energy by exchanging dissolvedcarbon dioxide (dCO2) and bicarbonate (HCO32-) with the environment. Simultaneous uptake and excretion of the two carbon species is referred to as inorganic carbon cycling (ICC). Often, ICC is indicated by displacements of the extracellular dCO2 signal from the equilibrium value after changing the light conditions. In this work, we additionally use (i) the extracellular pH signal, which requires non- or weakly-buffered medium, and (ii) a dynamic model of carbonate chemistry in the aquatic environment in order to detect and quantitatively describe ICC. Based on simulations and experiments in precisely controlled photobioreactors,we show that the magnitude of the observed dCO2 displacement crucially depends on extracellular pH level and buffer concentration. Moreover, we find that the dCO2 displacement can also be caused by simultaneous uptake of both dCO2 and HCO32- (no ICC). In a next step, the dynamic model of carbonate chemistry allows for a quantitative assessment of cellular dCO2 , HCO32- , and H+ exchange rates from the measured dCO2 and pH signals. Limitations of the method are discussed.
model: Müller et al. 2019
Müller S, Zavřel T, Červený J (2019) Towards a quantitative assessment of inorganic carbon cycling in photosynthetic microorganisms. Eng Life Sci:1-13
publication: Müller et al. 2019
Simulation type: fixed
Simulation type: reaction
Simulation type: reaction
Simulation type: reaction
Simulation type: reaction
Simulation type: reaction
Simulation type: reaction
Simulation type: reaction
Constant quantities
Assigned quantities
M. Trojak, D. Safranek, J. Hrabec, J. Salagovic, F. Romanovska, J. Cerveny: E-Cyanobacterium.org: A Web-Based Platform for Systems Biology of Cyanobacteria. In: Computational Methods in Systems Biology, CMSB 2016, Vol. 9859 of LNCS, pp. 316-322. Springer, 2016. DOI