PLMaddon

PLMaddon implements analytical routines for constructing and analyzing power-law kinetic ordinary differential equation (ODE) models to study dynamics and parameter sensitivities in systems biology.

Key Features:

• Extension of SBToolbox: Extends SBToolbox with modules for power-law model construction and analysis.
• Support for non-integer and negative kinetic orders: Handles kinetic orders that are positive, negative, or non-integer within power-law formulations.
• Power-law Taylor expansions: Generates power-law Taylor expansions from other ODE models, including Michaelis-Menten kinetics.
• Steady-state estimation algorithms: Provides algorithms to estimate steady states of power-law ODE models.
• Robustness and sensitivity analysis: Computes logarithmic gains and sensitivities to assess model robustness and parameter influence.

Scientific Applications:

• Systems biology network analysis: Analyzing dynamics, stability, and parameter sensitivities of biological networks described by power-law kinetics.
• Approximation and scale-invariant modeling: Approximating Michaelis-Menten and other ODE kinetics via power-law Taylor expansions and modeling systems exhibiting scale-invariant or fractal-like behavior.

Methodology:

Implements power-law Taylor expansions from ODE models, algorithms for steady-state estimation of power-law ODEs, and computation of logarithmic gains and sensitivities for robustness and sensitivity analysis.