Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating...

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Fitting Ordinary Differential Equation (ODE) models to experimental data is crucial for understanding signal transduction networks (STNs).
• Current parameter fitting methods require extensive simulations, especially for models with numerous perturbations, leading to significant computational cost.
• Steady State Perturbation Response (SSPR) data is valuable but computationally intensive to fit using traditional simulation-based approaches.

Purpose of the Study:

• To develop a computationally efficient approach for fitting ODE models to SSPR data.
• To reduce the need for simulating perturbation experiments during the model fitting process.
• To accelerate the analysis of signal transduction networks.

Main Methods:

• Proposes fitting ODE models by matching scaled Jacobian matrices (SJM) instead of directly using SSPR data.
• Numerically calculates SJMs from the model's rate equations.
• Estimates SJMs from SSPR data using Modular Response Analysis (MRA).

Main Results:

• The proposed method avoids simulating perturbation experiments, leading to substantial savings in computation time.
• Demonstrates the effectiveness of matching SJMs for fitting ODE models to both simulated and real SSPR data.
• Successfully applied the approach to models of the Mitogen Activated Protein Kinase (MAPK) pathway.

Conclusions:

• Matching scaled Jacobian matrices offers a computationally efficient alternative to simulation-based fitting for ODE models of STNs.
• This approach significantly speeds up the analysis of biological signaling pathways using SSPR data.
• The method provides a valuable tool for systems biology research, enabling faster model parameterization and hypothesis testing.