Recent developments in parameter estimation and structure identification of biochemical and genomic systems

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Biological systems are complex, necessitating mathematical modeling for quantitative understanding.
• Parameter estimation and network identification are critical yet challenging aspects of biological modeling.
• High-throughput data now enable 'top-down' or inverse approaches for biological system analysis.

Purpose of the Study:

• To review inverse modeling methods for biological systems, particularly within Biochemical Systems Theory (BST).
• To provide a practical workflow for parameter estimation and network identification.
• To address challenges in data, models, system structure, and algorithms for inverse problems.

Main Methods:

• Review of optimization and support algorithms for inverse problems in BST.
• Focus on methods for numerical integration, data smoothing, slope estimation, complexity reduction, and parameter space constraints.
• Exploration of data preprocessing, model redundancy handling, and structure identification techniques.

Main Results:

• Over one hundred methods exist, making a comprehensive overview difficult.
• The proposed workflow guides users through estimation, identifies issues, and suggests solutions.
• Addresses challenges related to data quality, model complexity, and algorithmic limitations.

Conclusions:

• Inverse modeling in BST offers a powerful approach for understanding biological networks.
• The presented workflow facilitates entry into the field for researchers.
• Identifies current limitations and open questions in inverse modeling for biological systems.