Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species

Area of Science:

• Systems Biology
• Computational Biology
• Synthetic Biology

Background:

• Gene regulatory networks involve complex interactions often exceeding experimental monitoring capabilities.
• Experimental gene expression data is frequently noisy and indirect, such as using fluorescence instead of direct protein counts.

Purpose of the Study:

• To develop a method for inferring biophysical information and characterizing gene circuits from limited and convoluted experimental data.
• To utilize the principle of Maximum Caliber (MaxCal) for building stochastic models of gene networks.

Main Methods:

• Employed Maximum Caliber (MaxCal) to build minimal stochastic models based on synthesis, degradation, and feedback information.
• Combined MaxCal with Maximum Likelihood (ML) for parameter inference from fluctuating protein expression trajectories.
• Validated the methodology on synthetic data from single-gene autoactivating and two-gene toggle switch circuits, including scenarios with noisy fluorescence data.

Main Results:

• The MaxCal + ML methodology successfully inferred basic rate parameters for genetic switches of increasing complexity.
• The model accurately captured trajectory fluctuations even when data mimicked noisy experimental conditions (e.g., fluorescence instead of protein numbers).
• An effective feedback parameter was derived, quantifying interactions within the characterized circuits.

Conclusions:

• MaxCal provides a powerful framework for characterizing gene circuits using limited and noisy biological data.
• This approach holds promise for understanding biological circuit evolution and advancing the design of synthetic gene circuits for specific functions.