SKiMpy

SKiMpy provides semiautomatic generation, parameterization, and analysis of large-scale kinetic models to study dynamic and adaptive responses in signaling pathways, gene expression networks, metabolic processes, and multispecies bioreactor systems.

Key Features:

• Efficient Kinetic Modeling: SKiMpy performs semiautomatic generation of large-scale kinetic models for signaling pathways, gene expression networks, and metabolic processes.
• Parameterization Around Steady-State Reference: SKiMpy parameterizes kinetic models around a steady-state reference to enable analysis of deviations from equilibrium.
• Multispecies Bioreactor Simulations: SKiMpy simulates multispecies interactions in bioreactors for assessment of biotechnological processes such as fermentation and waste treatment.
• Python 3 Implementation: SKiMpy is provided as a Python 3 package for computational construction, parameterization, and simulation of kinetic models.

Scientific Applications:

• Signaling Pathways: Model complex signaling networks to study temporal cellular responses to stimuli.
• Gene Expression Networks: Explore gene regulatory mechanisms and the dynamics of gene activation and repression.
• Metabolic Processes: Analyze metabolic pathway dynamics and predict effects of changes in enzyme activities or metabolite concentrations.
• Biotechnological Process Assessment: Assess and optimize multispecies bioreactor processes, including fermentation and waste treatment.

Methodology:

Semiautomatic generation of kinetic models, parameterization around steady-state references, and simulation of multispecies bioreactors implemented in Python 3.