Discriminating between rival biochemical network models: three approaches to optimal experiment design.

Area of Science:

• Molecular systems biology
• Computational modeling
• Biochemical networks

Background:

• Systems biology relies on computational models to predict experiments and understand biological mechanisms.
• Ambiguous models that fit data equally well pose a challenge, necessitating experiments to invalidate incorrect ones.
• Optimal experiment design is crucial for discriminating between alternative molecular mechanisms.

Purpose of the Study:

• Develop methodologies for optimal experiment design to discriminate between mathematical models of biological systems.
• Propose methods to maximize the difference between model outputs to facilitate model invalidation.
• Provide tools for a systems biology approach to experiment design.

Main Methods:

• Maximize L2 distance between rival model outputs by optimizing initial conditions.
• Design optimal external stimulus profiles to maximize L2 distance.
• Utilize optimized structural changes (e.g., gene knock-outs) to discriminate models.

Main Results:

• Methodologies for optimal experiment design were developed.
• Numerical implementation was demonstrated using signal processing in starving Dictyostelium amoebae.
• The proposed methods aim to maximize the difference between outputs of competing models.

Conclusions:

• Model-based experiment design enhances the reliability and efficiency of biochemical network model discrimination.
• Model invalidation aids in refining our understanding of biochemical networks.
• The developed methods offer new tools for systems biology experiment design.