SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments

Area of Science:

• Systems Biology and Computational Biology
• Biochemical Network Modeling
• Quantitative Systems Pharmacology

Background:

• Ordinary differential equations (ODEs) are crucial for modeling molecular species dynamics in cell signaling and metabolic networks.
• Estimating kinetic rate constants from time-series data is essential for both de novo and adapted biochemical models.
• Existing spline-based methods offer parameter estimation for data-rich scenarios, avoiding explicit ODE solving.

Purpose of the Study:

• To develop and present a web server implementing a spline-based method for kinetic parameter estimation.
• To provide a tool, Systematic Parameter Estimation for Data-Rich Environments (SPEDRE), for estimating reaction rates in biochemical networks.
• To facilitate the analysis of large, sparse biological networks, particularly signaling cascades.

Main Methods:

• Utilizes a spline-based parameter estimation algorithm suitable for data-rich environments.
• Accepts network connectivity and discrete time-series molecular concentration data as input.
• Leverages COPASI tools for pre-processing and post-processing of data and results.

Main Results:

• SPEDRE provides optimized values for reaction rate parameters in biochemical networks.
• Outputs include parameter ranges and bin plots, offering insights into parameter uncertainty.
• Achieves global coverage of the parameter space for networks with complete species data, albeit at low resolution and approximate accuracy.

Conclusions:

• SPEDRE offers an efficient web-based solution for estimating kinetic parameters in complex biological networks.
• The tool is particularly beneficial for large, sparse networks common in cell signaling research.
• SPEDRE is freely accessible, promoting wider adoption in systems biology research.