Modeling stochastic dynamics in biochemical systems with feedback using maximum caliber

Area of Science:

• Biochemistry
• Systems Biology
• Computational Biology

Background:

• Complex biological feedback systems are common.
• Current modeling approaches often require hard-to-obtain data like reaction rates and topologies.
• This necessitates treating these parameters as adjustable, limiting model accuracy.

Purpose of the Study:

• To present a general stochastic modeling method for small chemical and biochemical systems, particularly those with feedback.
• To introduce Maximum Caliber (MaxCal) as a more parsimonious alternative to existing dynamical modeling techniques.
• To demonstrate MaxCal's ability to capture feedback effects with fewer assumptions and parameters.

Main Methods:

• Developed Maximum Caliber (MaxCal), a dynamical modeling approach analogous to Maximum Entropy.
• Utilized average rate quantities and correlations from short experimental trajectories.
• Applied the method to model a bistable genetic toggle switch.

Main Results:

• MaxCal reliably inferred the statistics of stochastic bistability in simulated data.
• The method accurately predicted full dynamical distributions without complex reaction schemes.
• Demonstrated the parsimony of MaxCal, requiring fewer model assumptions and parameters.

Conclusions:

• Maximum Caliber (MaxCal) offers a more efficient and accurate stochastic modeling approach for biological feedback systems.
• The method successfully models complex dynamics, such as stochastic bistability, from limited data.
• MaxCal is broadly applicable to various small chemical and biochemical systems.