Using noise for model-testing.

Area of Science:

• Molecular biology
• Systems biology
• Computational biology

Background:

• Realistic biological models must account for cellular and intracellular noise.
• Existing methods often rely on deterministic models, which may not fully capture biological complexity.

Purpose of the Study:

• To present a novel computational approach for validating nonlinear biological models affected by noise.
• To provide a method for invalidating models based on parameter values or noise properties by comparing predictions with measurement data.

Main Methods:

• Utilizing computational techniques based on Kushner and Øksendal's work.
• Calculating analytical upper bounds for the exit probability of system trajectories from a defined phase space set.
• Comparing these bounds with experimental measurement data.

Main Results:

• The approach was successfully applied to biological examples of increasing complexity.
• Demonstrated utility in reverse engineering, specifically for determining model parameters and noise properties.
• Successfully applied to the Hog1 signaling pathway.

Conclusions:

• The novel approach offers a complementary method to deterministic models for validating noisy biological systems.
• It is particularly valuable for reverse engineering in systems biology.
• Provides a robust framework for assessing model validity under noisy conditions.