KK
K. Kiss
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Rhodospirillum rubrum is a metabolically versatile phototrophic bacterium with potential applications in sustainable hydrogen and bioplastic production. However, large-scale implementation remains limited by insufficient understanding of its growth dynamics under varying environmental conditions and the high energy costs of cultivation. This study presents a simplified, general mathematical model describing R. rubrum growth under anaerobic dark and light conditions, incorporating metabolic state transitions driven by substrate and carbon monoxide availability. The model, parameterized using literature data, captures key physiological features such as substrate inhibition and lag-phase behavior. Experimental validation under anaerobic light conditions demonstrated reasonable agreement with model predictions, while discrepancies were observed under dark conditions, that were attributed to experimental limitations rather than model deficiencies. The proposed framework provides a foundation for future model refinement and integration with other higher and lower level models.
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Rhodospirillum rubrum is a metabolically versatile phototrophic bacterium with potential applications in sustainable hydrogen and bioplastic production. However, large-scale implementation remains limited by insufficient understanding of its growth dynamics under varying environmental conditions and the high energy costs of cultivation. This study presents a simplified, general mathematical model describing R. rubrum growth under anaerobic dark and light conditions, incorporating metabolic state transitions driven by substrate and carbon monoxide availability. The model, parameterized using literature data, captures key physiological features such as substrate inhibition and lag-phase behavior. Experimental validation under anaerobic light conditions demonstrated reasonable agreement with model predictions, while discrepancies were observed under dark conditions, that were attributed to experimental limitations rather than model deficiencies. The proposed framework provides a foundation for future model refinement and integration with other higher and lower level models.