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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.