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Related Experiment Video

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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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Published on: January 30, 2018

Parameter inference for biochemical systems that undergo a Hopf bifurcation.

Paul D W Kirk¹, Tina Toni, Michael P H Stumpf

¹Division of Molecular Biosciences, Imperial College, London, England.

Biophysical Journal

|May 6, 2008

Summary

Understanding parameter inference in biological models is crucial. This study links parameter inferability to dynamical stability in nonlinear ordinary differential equation models undergoing Hopf bifurcations.

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Area of Science:

Mathematical Biology
Systems Biology
Biochemical Engineering

Background:

Parametric mathematical models are widely used to represent biological systems.
Inferring model parameters is essential for understanding and predicting biological system behavior.
Nonlinear ordinary differential equation models are common in systems biology.

Purpose of the Study:

To investigate parameter inferability in nonlinear ordinary differential equation models undergoing bifurcations.
To analyze the impact of Hopf bifurcations on the likelihood function of model parameters.
To explore the relationship between parameter inference and dynamical stability.

Main Methods:

Systematic investigation of the likelihood function's shape.
Analysis of parameter inferability in a generic biochemical reaction model.
Focus on models exhibiting Hopf bifurcations.

Main Results:

Parameter inferability is intrinsically linked to the parameters' influence on dynamical stability.
Changes in the likelihood function shape correlate with Hopf bifurcations.
Demonstrated a direct relationship between inference and stability in the studied model.

Conclusions:

Parameter inferability is not independent of the system's dynamical properties.
Understanding bifurcations is key to assessing parameter identifiability in biological models.
This research provides a foundation for further studies on inference and stability in complex biological models.