Inferring cell division kinetics in Chlamydomonas reinhardtii from flow cytometry with Gaussian process regression

Area of Science:

• Biotechnology
• Microbiology
• Optical Physics

Background:

• Cell size is crucial for microorganism metabolic activity and bioprocess optimization.
• Current methods like flow cytometry for cell size estimation are limited by optical properties, affecting accuracy.
• Accurate cell size distribution is vital for model development and process monitoring in microbial applications.

Purpose of the Study:

• To enhance the accuracy of cell size distribution estimation in microorganisms.
• To develop a novel physical model for improved light scattering analysis.
• To refine bioprocess monitoring and modeling through precise cell size data.

Main Methods:

• A novel physical model based on light scattering around a sphere was developed.
• The model's accuracy was benchmarked against microscopy image analysis.
• Cell division rates were inferred using Gaussian process regression on a population balance model with corrected size distributions.

Main Results:

• The proposed physical model demonstrated substantial improvement in cell size distribution estimation compared to standard methods.
• The corrected size distribution enabled accurate inference of cell division rates.
• The model successfully described observed size distributions and estimated division kinetics in Chlamydomonas reinhardtii.

Conclusions:

• The novel light scattering model significantly enhances cell size distribution accuracy.
• This approach provides mechanistic insights into algal cell division and heterogeneity.
• The findings support improved bioprocess development and monitoring using green algae.