Using single-cell models to predict the functionality of synthetic circuits at the population scale

Area of Science:

• Synthetic biology
• Biophysics
• Computational biology

Background:

• Biochemical processes exhibit inherent stochasticity at the single-cell level.
• Cell-to-cell variability is crucial for microbial population dynamics.
• Simple models often fail to predict synthetic biology circuit behavior due to this variability.

Purpose of the Study:

• To develop multiscale stochastic kinetic models that integrate single-cell and population processes.
• To quantitatively predict the population dynamics of a yeast optogenetic differentiation system.
• To demonstrate exploiting cell-to-cell variability for novel circuit functionalities.

Main Methods:

• Augmentation of the chemical master equation.
• Development of multiscale stochastic kinetic models.
• Quantitative prediction of system dynamics.

Main Results:

• Successfully modeled a yeast optogenetic differentiation system.
• Demonstrated quantitative prediction of population dynamics.
• Showcased exploitation of cell-to-cell variability for unintuitive circuit functions.

Conclusions:

• Multiscale stochastic kinetic models accurately predict complex population dynamics.
• Cell-to-cell variability can be harnessed for advanced synthetic biology applications.
• This approach offers a powerful framework for designing robust biological circuits.