Quantifying massively parallel microbial growth with spatially mediated interactions.

Area of Science:

• Microbiology
• Biotechnology
• Systems Biology

Background:

• Quantitative microbial growth understanding is crucial for pathogen control and biotechnology.
• Microbial growth is poorly characterized spatially, showing location-dependent variability even in isogenic populations.

Purpose of the Study:

• To investigate and quantify the spatial variability in microbial population growth.
• To identify the key factors contributing to location-dependent growth differences.
• To develop a model that accounts for environmental influences on microbial proliferation.

Main Methods:

• Machine learning regression models were used to identify location as a dominant factor in growth variability.
• Mechanistic resource consumer models were developed, treating nutrient and energy source concentration as latent variables.
• A dual approach combining machine learning and explicit population growth modeling was employed.

Main Results:

• Location significantly impacts microbial growth variability at specific stages.
• Nutrient and energy source diffusion and initial physiological states were identified as key drivers of spatial variability.
• A mechanistic model successfully captured growth variability across a shared environment.

Conclusions:

• Spatial variability in microbial growth is influenced by a combination of initial conditions and environmental factors.
• The developed model allows for the determination of intrinsic growth parameters, removing location-based confounders.
• This work provides a framework for large-scale experimentation on microbial eco-evolutionary dynamics.