Dynamic modelling of signalling pathways when ordinary differential equations are not feasible

Area of Science:

• Systems Biology
• Mathematical Modeling
• Cellular Signaling

Background:

• Ordinary differential equations (ODEs) are standard for modeling biological pathways but require extensive data and can be computationally intensive.
• ODE models face challenges with mechanistic interpretability and handling numerous compounds in complex signaling networks.

Purpose of the Study:

• To extend the retarded transient function (RTF) approach for modeling dose-dependencies in cellular signaling dynamics.
• To enable RTF models to predict responses in both time and concentration domains, mirroring the capabilities of ODE models.
• To provide an intuitive framework for investigating signaling differences across biological conditions or cell types.

Main Methods:

• The retarded transient function (RTF) approach was extended to incorporate concentration or dose-dependencies.
• The enhanced RTF method was applied to time- and dose-dependent inflammasome activation data in macrophages stimulated with nigericin sodium salt.
• The approach is implemented in a MATLAB-based toolbox (Data2Dynamics) and as R code.

Main Results:

• The extended RTF approach successfully models dose-dependent kinetics in cellular signaling.
• The method provides intuitively interpretable parameters characterizing signal dynamics.
• Predictive modeling of time- and dose-dependencies is achievable even with partial quantification of cellular components.

Conclusions:

• The extended RTF approach offers a versatile and effective framework for modeling dose-dependent cellular signaling.
• This method enhances the understanding of biological responses to varying input concentrations.
• RTF modeling provides a powerful alternative to ODEs for systems biology applications, offering improved interpretability and data efficiency.