Main description:

This volume covers a variety of topics related to the practice of rule-based modeling, a type of mathematical modeling useful for studying biomolecular site dynamics. There is an emphasis on software tools and detailed descriptions of techniques. The chapters in this book discuss topics such as software tools and frameworks for compartmental modeling (Pycellerator, RuleBuilder, Prgy, rxncon, MSMB, and ML-Rules); tools for spatial modeling (Simmune, Smoldyn, MCell-R, SRSim, and CellOrganizer); rule-based models to analyze proteomic data; model annotation; Markov chain aggregation; BioJazz; and methods to identify model parameters (Data2Dynamics, RKappa, and BioNetFit). Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary resources, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Modeling Biomolecular Site Dynamics: Methods and Protocols is a valuable resource for both the novice and expert rule-based modeler. It will also appeal to systems biologists and help them enhance their studies with easy-to-read and write rule-based models.

Contents:

Preface...Foreword...Table of Contents...Contributing Authors...
1. A Pycellerator TutorialBruce E. Shapiro and Eric Mjolsness
2. Using RuleBuilder to Graphically Define and Visualize BioNetGen-Language Patterns and Reaction RulesRyan Suderman, G. Matthew Fricke, and William S. Hlavacek
3. Strategy-Driven Exploration for Rule-Based Models of Biochemical Systems with PORGYOana Andrei, Maribel Fernandez, Helene Kirchner, and Bruno Pinaud
4. Using rxncon to Develop Rule-Based ModelsJesper Romers, Sebastian Thieme, Ulrike Munzner, and Marcus Krantz
5. Efficiently Encoding Complex Biochemical Models with the Multistate Model Builder (MSMB)Alida Palmisano, Stefan Hoops, Layne T. Watson, Thomas C. Jones, Jr., John J. Tyson and Clifford A. Shaffer
6. Multi-Level Modeling and Simulation of Cellular Systems: An Introduction to ML-RulesTobias Helms, Tom Warnke, and Adelinde M. Uhrmacher
7. Using Python for Spatially Resolved Modeling with SimmuneBastian R. Angermann and Martin Meier-Schellersheim
8. Rule-Based Modeling using Wildcards in the Smoldyn SimulatorSteven S. Andrews
9. MCell-R: A Particle-Resolution Network-Free Spatial Modeling FrameworkJose-Juan Tapia, Ali Sinan Saglam, Jacob Czech, Robert Kuczewski, Thomas M. Bartol, Terrence J. Sejnowski, and James R. Faeder
10. Spatial Rule-Based Simulations: the SRSim SoftwareRichard Henze, Gerd Grunert, Bashar Ibrahim, and Peter Dittrich
11. CellOrganizer: Learning and Using Cell Geometries for Spatial Cell SimulationsTimothy D. Majarian, Ivan Cao-Berg, Xiongtao Ruan, and Robert F. Murphy
12. Using Mechanistic Models for Analysis of Proteomic DataLily A. Chylek
13. Annotations for Rule-Based ModelsMatteo Cavaliere, Vincent Danos, Ricardo Honorato-Zimmer, and William Waites
14. Markov Chain Aggregation and Its Application to Rule-Based ModellingTatjana Petrov
15. In Silico Evolution of Signaling Networks Using Rule-Based Models: Bistable Response DynamicsSong Feng and Orkun S. Soyer
16. Recipes for Analysis of Molecular Networks Using the Data2Dynamics Modeling EnvironmentBernhard Steiert, Clemens Kreutz, Andreas Raue, and Jens Timmer
17. RKappa: Software for Analyzing Rule-Based Models
Anatoly Sorokin, Oksana Sorokina, and J. Douglas Armstrong
18. A Step-by-Step Guide to Using BioNetFitWilliam S. Hlavacek, Jennifer Longo, Lewis R. Baker, Maria del Carmen Ramos Alamo, Alexander Ionkov, Eshan D. Mitra, Ryan Suderman, Keesha E. Erickson, Raquel Dias, Joshua Colvin, Brandon R. Thomas, and Richard G. Posner