KInfer

KInfer infers rate constants from experimentally measured time-course data for parameter estimation in biochemical reaction networks.

Key Features:

• Robustness: Employs methods resilient to stochastic variation and inherent biological noise to provide reliable parameter estimates.
• Minimal Input Requirement: Operates with minimal initial information to deduce rate constants in complex biochemical systems.

Scientific Applications:

• Synthetic Models: Applied to a 12-state model of the SERCA pump and to a genetic network with feedback between regulator and effector genes.
• Real Biological Systems: Applied to a model of the interaction between the inhibitor kappaB kinase enzyme and its substrate in the NF-kappaB signal transduction pathway, and to a stiff model of the fermentation pathway in Lactococcus lactis.

Methodology:

Generates an inference model that deduces rate constants from time-course data, emphasizing robustness to experimental uncertainties and noise in nonlinear dynamic biological systems.