Revisiting Intervention in Context-Sensitive Probabilistic Boolean Networks
Author Information
Author(s): Faryabi Babak, Vahedi Golnaz, Chamberland Jean-Francois, Datta Aniruddha, Dougherty EdwardR
Primary Institution: Texas A&M University
Hypothesis
What is the impact of reducing the state space in context-sensitive probabilistic Boolean networks on intervention performance?
Conclusion
The reduction in state space leads to a loss of intervention performance, particularly when the switching probabilities are low.
Supporting Evidence
- The study shows that the approximate representation can describe the dynamics of the context-sensitive probabilistic Boolean network.
- Numerical studies indicate that the performance of the approximate strategy is close to optimal when switching probabilities are high.
- The intervention strategies derived from the full state space outperform those from the reduced state space under typical conditions.
Takeaway
This study looks at how simplifying complex biological models can make them easier to use, but it might also make them less effective.
Methodology
The study derives transition probability matrices for context-sensitive probabilistic Boolean networks and compares intervention strategies through numerical simulations.
Limitations
The performance of the approximate strategy degrades for smaller switching probabilities, which are typical in context-sensitive PBNs.
Digital Object Identifier (DOI)
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