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El. knyga: Boolean Networks as Predictive Models of Emergent Biological Behaviors

(Binghamton University, State University of New York), (Indiana University), (Pennsylvania State University), (University of Mount Union), (Pennsylvania State University)
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Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.

This Element shows interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions and the often-incomplete state of system knowledge.

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This Element shows Boolean models of biological systems predicting these systems' responses to perturbations and control interventions.
1. Types of biological networks;
2. Modeling the dynamics of biological networks;
3. Boolean modeling of the dynamics of biological networks;
4. How to build and validate a boolean network model of a specific biological system;
5. Case study boolean systems;
6. How to analyze a boolean model: state transition graphs, attractors, and trap sets;
7. The parity-expanded network;
8. State-space compression and attractor identification using stable motifs;
9. Attractor control; Conclusions; References.
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