Optimal bacterial resource allocation: metabolite production in continuous bioreactors

Area of Science:

• Biotechnology and biochemical engineering
• Microbial systems engineering
• Synthetic biology

Background:

• Continuous bioreactors are crucial for industrial metabolite production.
• Optimizing resource allocation in microbial cultures remains a challenge.
• Synthetic biology offers tools to engineer microbial behavior for enhanced production.

Purpose of the Study:

• To develop and analyze a novel control strategy for maximizing metabolite synthesis in continuous bioreactors.
• To investigate the use of a synthetic growth switch to modulate bacterial resource allocation.
• To formulate and solve the metabolite production maximization problem as an optimal control problem.

Main Methods:

• Development of a coarse-grained self-replicator dynamical model for microbial growth.
• Incorporation of a synthetic growth switch to control RNA polymerase concentration.
• Application of dynamical systems theory to analyze system behavior and persistence conditions.
• Formulation and solution of an Optimal Control Problem using Pontryagin's Maximum Principle.
• Numerical analysis of a two-dimensional optimization problem involving Michaelis-Menten kinetics.

Main Results:

• A novel resource allocation control strategy was proposed for continuous bioreactors.
• Conditions for bacterial population persistence were established.
• A sub-optimal control strategy using constant allocation was identified, leading to optimal steady-state production.
• Comparison of biomass and metabolite production objectives under controlled dilution rates.
• Numerical exploration of the optimization problem considering kinetic constraints.

Conclusions:

• The developed control strategy effectively enhances metabolite synthesis in continuous bioreactors.
• The synthetic growth switch provides external control over bacterial resource allocation.
• The study provides a framework for optimizing bioreactor performance for specific production goals.
• The findings contribute to advancing microbial systems engineering and synthetic biology applications.