Inverse Problems in Systems Biology: A Critical Review

Area of Science:

• Computational Biology
• Systems Biology
• Biophysics

Background:

• Systems biology integrates computational models with experimental data for understanding complex biological systems.
• Computational models require continuous refinement via experiments, which can be resource-intensive.
• Noise in experimental data and inherent model complexity pose challenges in parameter estimation.

Purpose of the Study:

• To critically review inverse problems in systems biology.
• To propose a strategy for determining the minimal information required to analyze dynamic biological models.
• To address challenges arising from numerous unknown or non-measurable parameters in biological models.

Main Methods:

• Review of inverse problem methodologies in systems biology.
• Analysis of "sloppy models" and parameter unidentifiability.
• Conceptual strategy development for information minimization.

Main Results:

• Inverse problems can be enhanced by incorporating limited noise to address ill-posed scenarios.
• Sloppy models in systems biology are characterized by poorly constrained parameters, leading to unidentifiability.
• A strategy is needed to infer and analyze biological systems effectively despite model complexity.

Conclusions:

• Addressing ill-posed problems and parameter unidentifiability is crucial for advancing systems biology.
• Minimizing information requirements is key to overcoming challenges in analyzing complex dynamic biological models.
• Further research is needed to develop robust strategies for parameter estimation and model analysis in systems biology.