Network transformation-based analysis of biochemical systems.

Area of Science:

• Biochemistry
• Systems Biology
• Chemical Kinetics

Background:

• Dynamical systems offer diverse kinetic realizations beneficial for biochemical analysis.
• Reaction networks derived from dynamical systems may lack properties essential for thorough analysis.
• Existing network translation methods are extended to network transformations that modify networks while preserving the dynamical system.

Purpose of the Study:

• To introduce network transformations for modifying reaction networks while preserving the underlying dynamical system.
• To demonstrate the application of these transformations in analyzing biochemical systems like calcium and insulin signaling.
• To develop an algorithm for transforming non-complex factorizable kinetic (NFK) systems to complex factorizable kinetic (CFK) systems.

Main Methods:

• Network transformations that can alter the stoichiometric subspace (shrink, extend, or retain).
• Demonstration using kinetic realizations of calcium signaling and metabolic insulin signaling.
• Development of an algorithm for transforming weakly reversible NFK to weakly reversible CFK systems.

Main Results:

• Positive dependent networks can be translated into weakly reversible networks.
• Transformed systems with positive deficiency prove beneficial for biochemical system analysis.
• The study analyzes structural and kinetic properties of transformed systems, including concordance invariance and variations in injectivity and stationarity.

Conclusions:

• Network transformations offer a powerful approach to modify and analyze biochemical reaction networks.
• The developed algorithm enhances the analysis of NFK systems by converting them to CFK systems.
• Transformed systems provide new insights into the dynamics and properties of complex biological systems.