SPEDRE

SPEDRE estimates kinetic rate constants for biochemical networks by applying spline-based parameter estimation to infer cell signaling and metabolic dynamics represented with ordinary differential equations (ODEs) from time-series concentration data.

Key Features:

• Spline-based parameter estimation: Employs a spline-based algorithm developed for data-rich scenarios to estimate reaction rate parameters without explicitly solving ODEs.
• ODE modeling: Represents production and consumption of molecular species over time using ordinary differential equations (ODEs).
• Input requirements: Requires a connectivity map of the biochemical network and time-series concentration measurements of molecular species at discrete intervals.
• Optimized for large sparse networks: Targets extensive, sparse networks such as signaling cascades and is applicable when proteomic measurements are available for all network species.
• Output types: Produces optimized values for reaction rate parameters along with associated ranges and bin plots.
• COPASI integration: Utilizes COPASI tools for pre-processing and post-processing of models and data.

Scientific Applications:

• Systems biology parameter inference: Enables kinetic parameter estimation for models of cellular signaling pathways and metabolic networks.
• Proteomics and high-throughput datasets: Facilitates extraction of kinetic information from proteomic time-series where measurements cover all network species.

Methodology:

Models species dynamics with ordinary differential equations (ODEs); applies a spline-based algorithm tailored for data-rich scenarios to estimate kinetic rate constants without explicit ODE integration; employs COPASI tools for pre-processing and post-processing.