Simulating Genetic Circuits in Bacterial Populations with Growth Heterogeneity

Area of Science:

• Computational biology
• Systems biology
• Synthetic biology

Background:

• Bacterial populations exhibit heterogeneity in growth rates, influencing gene circuit dynamics.
• Previous models often simplified population structures, potentially overlooking critical effects of growth heterogeneity.

Purpose of the Study:

• To develop and validate a novel stochastic simulation method for gene circuits in heterogeneous bacterial populations.
• To investigate how different population setups (Chemostat vs. Mother Machine) affect population structure and gene circuit behavior under growth heterogeneity.

Main Methods:

• Introduced the "Next Family Method," an efficient stochastic simulation approach for modeling gene circuits.
• Implemented and compared Chemostat-type and Mother Machine population growth models.
• Analyzed key population statistics, including protein concentration distributions, growth rates, and hysteresis curves.

Main Results:

• Demonstrated that population structure significantly impacts gene circuit statistics when growth rates are heterogeneous.
• Observed distinct signatures in protein concentration distributions and hysteresis curves dependent on the population setup.
• Showed that bistable antibiotic resistance circuits exhibit setup-dependent minimal inhibitory concentrations.

Conclusions:

• The Next Family Method provides a robust tool for studying gene circuits in heterogeneous bacterial populations.
• Population structure is a critical factor influencing the behavior and quantitative assessment of genetic circuits.
• Understanding population-specific effects is essential for accurate prediction of antibiotic resistance and circuit behavior.