Parameter estimation in models combining signal transduction and metabolic pathways: the dependent input approach

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Biological complexity and limited data hinder standard modeling of gene networks.
• Parameter quantification is a major bottleneck in systems biology.
• Existing methods struggle with complex, integrated biological systems.

Purpose of the Study:

• To present a novel approach for modeling biological networks using "dependent inputs".
• To improve parameter estimation and model validation in systems biology.
• To study nitrogen uptake pathways in Saccharomyces cerevisiae.

Main Methods:

• Utilizing "dependent inputs" to represent unmodeled dynamics.
• Incorporating a priori information into a multi-objective identification criterion.
• Performing perturbation experiments and collecting time-series data (metabolites, mRNA).

Main Results:

• The dependent input approach facilitates model fitting and validation across different cell types (wild-type vs. mutant).
• A novel identification criterion enabled parameter estimation from limited experimental data.
• A nonlinear model of yeast nitrogen uptake was developed and validated.

Conclusions:

• The proposed method offers a robust way to model complex biological systems with limited data.
• The identified model accurately represents yeast metabolic responses to perturbations.
• This approach enhances the reliability of systems biology models for gene and metabolic networks.