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Published on: October 15, 2013
Multistationarity questions in reduced versus extended biochemical networks
Alicia Dickenstein1, Magalí Giaroli2, Mercedes Pérez Millán3
1Dto. de Matemática, FCEN, Universidad de Buenos Aires, and IMAS (UBA-CONICET), Ciudad Universitaria, Pab. I, C1428EGA, Buenos Aires, Argentina.
This study investigates multistationarity in chemical reaction networks by modifying network structures. We provide general results for MESSI systems to compute multistationarity circuits in biochemical networks.
Area of Science:
- Biochemistry
- Chemical Kinetics
- Systems Biology
Background:
- Understanding multistationarity in chemical reaction networks is crucial for modeling complex biological systems.
- Previous research has explored network modifications and their impact on multistationarity, particularly in mass-action systems.
- Advances in understanding multistationarity have been made by Feliu and Wiuf, Sadeghimanesh and Feliu, and Pérez Millán and Dickenstein.
Purpose of the Study:
- To analyze the effects of adding or removing intermediate complexes on multistationarity in chemical reaction networks.
- To extend existing literature on multistationarity within the framework of mass-action kinetics.
- To establish general results for MESSI (Multistationarity, Elimination, and Structure) systems.
Main Methods:
- Utilizing the framework of chemical reaction networks with mass-action kinetics.
- Analyzing reduced versus extended networks by systematically altering intermediate complexes.
- Developing general results applicable to MESSI systems.
Main Results:
- Clarification and extension of existing literature on multistationarity in chemical reaction networks.
- Establishment of general results for MESSI systems.
- Computation of multistationarity circuits for significant biochemical networks.
Conclusions:
- The modification of intermediate complexes significantly impacts multistationarity in chemical reaction networks.
- The developed general results for MESSI systems provide a powerful tool for analyzing network dynamics.
- This work contributes to a deeper understanding of multistationarity in biochemical systems and its computational prediction.
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