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Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors
This study introduces influence networks with forces, offering a new way to model cell processes. These networks provide a hierarchy of semantics, aiding in building complex biological models and analyzing system attractors.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemistry
Background:
- Biochemical reaction networks are standard for modeling cell processes.
- Influence diagrams are used for representing molecular interactions.
- Bridging influence and reaction networks can enhance model building.
Purpose of the Study:
- Introduce a formalism of influence networks with forces.
- Equip influence networks with a hierarchy of semantics (Boolean, Petri net, stochastic, differential).
- Compare expressive power and analyze computational properties.
Main Methods:
- Formalism of influence networks with forces.
- Hierarchy of discrete and continuous semantics.
- Analysis of monotonicity properties.
- Algorithm development for attractor computation.
Main Results:
- Influence networks have equivalent expressive power to reaction networks under differential semantics.
- Influence networks are weaker than reaction networks under discrete semantics.
- Developed a (positive) Boolean semantics and compared it with (negative) Boolean semantics.
- Derived an algorithm to compute attractors in both positive and negative Boolean semantics.
Conclusions:
- Influence networks offer a valuable alternative for modeling biological systems.
- The hierarchy of semantics provides flexibility in model analysis.
- The developed algorithm aids in understanding system dynamics and attractors.
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