Modeling Gene Regulatory Networks with Probabilistic Polynomial Systems
Author Information
Author(s): Dimitrova Elena S, Mitra Indranil, Jarrah Abdul Salam
Primary Institution: Clemson University
Hypothesis
Can probabilistic polynomial dynamical systems effectively reverse engineer gene regulatory networks from experimental data?
Conclusion
The proposed method successfully identifies stochastic models that retain key features of original gene regulatory networks.
Supporting Evidence
- The method was applied to the yeast cell cycle model and successfully identified key features.
- The generated models compared favorably to results from other algorithms.
- The algorithm can handle large regulatory networks, making it applicable to many biological systems.
Takeaway
This study shows how to use math to understand how genes work together, even when the data is messy.
Methodology
The study developed an algorithm for reverse engineering gene regulatory networks using probabilistic polynomial dynamical systems.
Limitations
The complexity of the Gröbner fan computation increases with the number of network nodes, making it challenging for large networks.
Digital Object Identifier (DOI)
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