Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution...

Area of Science:

• Biotechnology
• Metabolic Engineering
• Systems Biology

Background:

• Genome-scale models (GSMs) predict cyanobacterial phenotypes in photobioreactors (PBRs) by focusing on maximal yields.
• Cyanobacterial metabolism is influenced by intracellular enzymes and PBR conditions.
• Traditional GSMs do not fully capture the complexity of PBR environments.

Purpose of the Study:

• To develop a hybrid platform integrating a GSM with growth kinetics, cell movements, and light distribution.
• To connect intracellular metabolic information with extracellular PBR conditions for improved productivity prediction.
• To map flux dynamics and predict overall production in PBRs.

Main Methods:

• Integration of a genome-scale metabolic model of Synechocystis 6803.
• Incorporation of growth kinetics, cell movement, and light distribution functions.
• Development of a hybrid platform for multi-scale analysis.

Main Results:

• Cyanobacteria can achieve high biomass concentrations (>20 g/L) in PBRs.
• Cellular fluxome can exhibit stochastic changes due to random cell movements.
• Self-shading activates the oxidative pentose phosphate pathway in subpopulations.
• Glycogen synthesis may support growth in dark zones, impacting bio-production strategies.

Conclusions:

• The integrated GSM predicts yield, rate, and titer in large-scale PBRs.
• It reveals mutant physiologies under diverse bioreactor conditions, unlike traditional GSMs.
• This approach aids in designing robust strains for industrial settings.