Gene Expression Across Growth Stages (GEAGS): Modeling Circuit Dynamics in Batch Cultures

Area of Science:

• Synthetic biology
• Computational biology
• Microbial physiology

Background:

• Bacterial gene expression is often modeled assuming constant growth, which is inaccurate for batch cultures.
• Nutrient depletion and growth phase transitions significantly impact transcription, translation, mRNA degradation, and protein dilution.
• Existing models do not fully capture the complexities of gene expression under dynamic growth conditions.

Purpose of the Study:

• To introduce a novel computational framework, Gene Expression Across Growth Phases (GEAGS), for modeling bacterial gene expression.
• To couple molecular reaction networks with logistic population growth for accurate simulations.
• To provide a generalizable workflow for synthetic biologists studying gene expression in batch cultures.

Main Methods:

• Developed a dual-scale modeling approach coupling molecular reaction networks with logistic population growth.
• Utilized growth-dependent rate modifier functions (RMFs) to link population dynamics with molecular processes.
• Provided step-by-step methods for constructing GEAGS models from simple reporter systems to complex circuits.

Main Results:

• The GEAGS framework enables accurate simulation of gene expression dynamics under time-varying growth rates.
• The model effectively accounts for resource competition and its impact on molecular processes.
• Demonstrated applicability to various synthetic gene circuit designs, including optogenetic regulation and feedback control.

Conclusions:

• The GEAGS framework offers a robust computational tool for understanding bacterial gene expression in batch cultures.
• Accurate modeling of growth dynamics is crucial for predicting synthetic gene circuit behavior.
• This workflow facilitates the design and optimization of gene circuits in industrially relevant microbial systems.