Exploiting Information and Control Theory for Directing Gene Expression in Cell Populations

Area of Science:

• Microbial Adaptation and Control
• Systems Biology
• Information Theory in Biology

Background:

• Microbial populations adapt to environmental changes via sensing/response or stochastic phenotypic switching.
• Adaptation strategies are influenced by environmental perturbation characteristics and genetic circuit architecture.
• Current control strategies, enabled by computational and synthetic biology, primarily target synthetic gene circuits.

Purpose of the Study:

• To propose a novel framework for controlling microbial populations.
• To extend existing control strategies beyond synthetic gene circuits to natural systems.
• To introduce mutual information as a key metric for characterizing and controlling gene circuits.

Main Methods:

• Reviewing existing literature on microbial adaptation and control strategies.
• Applying information theory concepts, specifically mutual information (MI), to gene circuit analysis.
• Proposing a metric derived from MI to characterize natural gene circuit frequencies.

Main Results:

• Demonstrating that microbial adaptation strategies are context-dependent.
• Identifying limitations in the applicability of current control strategies to natural systems.
• Establishing mutual information as a potential metric for quantifying gene circuit properties.

Conclusions:

• Mutual information can characterize the natural frequency of gene circuits.
• This metric offers a pathway to expand control strategies to natural cellular systems.
• Controlling gene circuits in microbial populations can be achieved using information-theoretic approaches.