Parameter identification, experimental design and model falsification for biological network models using...

Area of Science:

• Systems Biology
• Biochemical Reaction Networks
• Computational Biology

Background:

• Parameter identification in systems biology is challenging, especially with noisy experimental data.
• High parameter uncertainty requires robust quantification methods for reliable model analysis.

Purpose of the Study:

• To develop a set-based approach for parameter identification in discrete time models of biochemical networks.
• To quantify parameter uncertainty and improve experimental design in systems biology.

Main Methods:

• Developed a set-based approach to determine an outer approximation of the set of consistent parameters (SCP).
• Formulated a feasibility problem to approximate the SCP and analyze complete parameter sets.
• Presented a novel set-based method for experimental design, predicting information content of future measurements.

Main Results:

• The set-based method effectively approximates the SCP, allowing for model falsification by checking if the SCP is empty.
• The experimental design method provides reliable predictions even with limited prior knowledge and uncertain inputs.
• Demonstrated the approach using a discrete time model of the MAP kinase cascade.

Conclusions:

• The developed set-based approach offers a robust method for parameter identification and uncertainty quantification in biochemical networks.
• This approach enhances model analysis and facilitates informed experimental design in systems biology.
• The method is particularly valuable for handling noisy data and limited prior information.