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Boolean modeling: A logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions
Réka Albert
, Juilee Thakar
Physics
Institute for Computational and Data Sciences (ICDS)
Huck Institutes of the Life Sciences
Materials Research Institute (MRI)
Penn State Cancer Institute
Cancer Institute, Next-Generation Therapies
Research output
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Contribution to journal
›
Article
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peer-review
104
Scopus citations
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Keyphrases
Dynamic Approach
100%
Signaling Network
100%
Logic-based
100%
Gene Regulatory Network
100%
Boolean Modeling
100%
Boolean Model
100%
Biomolecules
66%
Dynamic Model
66%
Cell Behavior
66%
Cell Fate
66%
Interaction System
66%
Biomolecular Systems
66%
Downstream Process
33%
Parameter-free
33%
System Dynamics
33%
Multi-cell
33%
Quantitative Model
33%
Network Applications
33%
Kinetic Parameters
33%
Follow-up Experiment
33%
Cellular Phenotype
33%
Logic Model
33%
Exploratory Modelling
33%
Stable State
33%
Engineering
State Variable
100%
Biomolecule
50%
Dynamic Models
50%
Interacting System
50%
Multiple Cell
25%
Systems Dynamics
25%
Kinetic Parameter
25%
Free Parameter
25%
Stable State
25%
Biochemistry, Genetics and Molecular Biology
Cell Fate
100%
Regulatory Network
100%
Cell Function
50%
Kinetic Parameter
50%