Model selection in systems and synthetic biology

Area of Science:

• Systems biology
• Computational biology
• Mathematical modeling

Background:

• Mechanistic models are integral to systems biology.
• Parameterizing models is a significant challenge.
• Differentiating between alternative models is increasingly important.

Purpose of the Study:

• To provide an overview of recent developments in model selection.
• To focus on practical and statistically sound approaches.
• To outline the application of these methods in systems biology.

Main Methods:

• Review of recent statistical and optimization routines.
• Focus on practical model selection techniques.
• Exploration of conceptual foundations for model comparison.

Main Results:

• Emerging focus on choosing the best model over parameter inference.
• Development of practical and statistically robust model selection methods.
• Identification of scope for application in systems biology.

Conclusions:

• Model selection is a key challenge in systems biology.
• Practical, statistically grounded methods are available for model selection.
• These methods enhance the ability to describe biological systems accurately.