Modelling co-translational dimerization for programmable nonlinearity in synthetic biology.

Area of Science:

• Synthetic biology
• Systems biology
• Molecular biology

Background:

• Nonlinearity is crucial for biological network functions like bistability and oscillations.
• Rational design of nonlinearity in biological systems is challenging due to limited mechanistic modeling.
• Existing models often overlook co-translational dimerization as a source of nonlinearity.

Purpose of the Study:

• To introduce a mathematical model for gene regulatory circuits that explicitly simulates co-translational protein dimerization.
• To provide a method for fine-tuning nonlinearity in biological systems by controlling dimerization dynamics.
• To establish design principles for engineering predictable nonlinear dynamics in synthetic circuits.

Main Methods:

• Modeling gene regulatory circuits with a focus on co-translational dimerization.
• Balancing co-translational and post-translational dimerization to control nonlinearity.
• Identifying design rules (e.g., protein length, gene separation) for tuning dimerization dynamics in vivo.

Main Results:

• A novel model accurately simulates protein dimerization during translation, not just after.
• Demonstrated a method to precisely control nonlinearity by adjusting the balance of dimerization timing.
• Proposed practical design strategies for modulating dimerization dynamics within living cells.

Conclusions:

• Co-translational dimerization is a viable and controllable source of nonlinearity in gene circuits.
• The developed model and design rules facilitate the engineering of synthetic biological systems with predictable nonlinear behaviors.
• This work enhances the programmability of synthetic biology by providing tools to precisely engineer molecular dynamics.