Multiscale modelling of bioprocess dynamics and cellular growth

Area of Science:

• Biotechnology
• Systems Biology
• Metabolic Engineering

Background:

• Fermentation is key for producing valuable bioproducts, but traditional models lack intracellular detail.
• Optimizing bioprocesses requires understanding the link between macroscale variables and intracellular metabolism.
• Current models often overlook the intracellular mechanisms driving fermentation outcomes.

Purpose of the Study:

• To develop a multiscale computational model for bioprocess optimization.
• To integrate intracellular processes like gene expression and ribosome allocation into fermentation models.
• To predict bioreactor dynamics based on genetic construct properties.

Main Methods:

• Developed a multiscale model incorporating coarse-grained intracellular dynamics.
• Integrated construct-specific parameters (promoter strength, ribosomal binding sites).
• Validated model against biomass, substrate, and acetate data in Escherichia coli.

Main Results:

• The model accurately fits experimental data for biomass and substrate utilization.
• Successfully predicted acetate dynamics and heterologous protein expression.
• Revealed interdependencies between gene expression parameters and production outputs like protein yield and acetate secretion.

Conclusions:

• The computational model enables in silico analysis of genetic constructs for bioproduct optimization.
• Assesses the impact of ribosomal binding site strength and promoter combinations on production.
• Facilitates efficient design and optimization of microbial fermentation processes for enhanced bioproduct yields.