PhosKinTime

Overview

PhosKinTime is a mechanistic framework for reconstructing phosphorylation dynamics from time-resolved molecular measurements and kinase–substrate network information.

Scientific question

How can time-resolved phosphoproteomic measurements and prior kinase–substrate relationships constrain models of signalling-network dynamics? The work focuses on estimating kinetic behaviour rather than treating each time point as an independent observation.

Model and method

The framework represents phosphorylation kinetics with ordinary differential equations. Parameter estimation combines global differential-evolution search with constrained sequential least-squares optimization, followed by sensitivity analysis and model diagnostics.

Inputs

• phosphosite time-course measurements;
• kinase–substrate network information;
• model definitions, parameter bounds and initial conditions.

Outputs

• estimated kinetic parameters and simulated phosphorylation trajectories;
• model-fit and parameter diagnostics;
• sensitivity summaries and scientific visualizations.

My contribution

As part of my master’s thesis, I developed the optimization framework used to reconstruct phosphorylation-network dynamics from LC–MS and kinase–substrate data, including differential-evolution and sequential least-squares parameter estimation.

Provenance

The work originated in my master’s thesis in Theoretical Biophysics at Humboldt-Universität zu Berlin. The public project documentation preserves its thesis origin and acknowledgements.

Scope and limitations

The public materials establish a mechanistic modelling and optimization framework; they do not establish clinical use or generalization beyond the molecular datasets and network priors supplied to a particular analysis. Inference therefore remains conditional on measurement coverage, prior-network quality and model assumptions.