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Composition and Aggregation in Modeling Regulatory Networks

Clifford A. Shaffer* Ranjit Randhawa* John J. Tyson + Departments of Computer Science* and Biology + Virginia Tech Blacksburg, VA 24061. Composition and Aggregation in Modeling Regulatory Networks. Regulatory Network Modeling.

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Composition and Aggregation in Modeling Regulatory Networks

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  1. Clifford A. Shaffer* Ranjit Randhawa* John J. Tyson+ Departments of Computer Science* and Biology+ Virginia Tech Blacksburg, VA 24061 Composition and Aggregation in Modeling Regulatory Networks

  2. Regulatory Network Modeling • Wish to deduce physiological properties of a cell from wiring diagrams of control systems

  3. Frogegg Model

  4. Budding Yeast Model • Wiring diagrams are converted to reactions for simulation • Example: Chen and Tyson’s budding yeast model contains over 30 ODEs, some nonlinear. • About 140 rate constant parameters • Validate model by comparing simulation results against morphological outcomes from over 100 mutants defective in the regulatory network.

  5. Budding Yeast Model

  6. Problem • These models are reaching the limits of human comprehension • Making the model suitable for stochastic simulation increases the number of reactions by a factor of 3-5. • Models of the Mammalian cell cycle will require 100-1000 (more for stochastic simulation).

  7. Solution • Some mechanism must be found to describe models as collections of small building blocks that are combined to form the full model.

  8. Systems Biology Markup Language • SBML is the current standard interchange language within the community of systems biology modelers. • We implement our proposals within the context of SBML language additions.

  9. Prior Efforts • Others (Finney; Ginkel; Schroder&Weimar; Webb) have made proposals for model decomposition within SBML. • These various proposals for have never been implemented. • A major problem appears to be that they view model decomposition as one monolithic problem to solve. • There are actually various distinct mechanisms involved.

  10. Our Approach • We recognize four distinct activities related to model decomposition • Fusion: Take existing models and merge them • Composition: Build up from existing models, no information hiding • Aggregation: Build up from building blocks, controlled interfaces • Flattening: Merge the building blocks back into a “flat” (non-composed) model (for making simulation runs)

  11. Relationships

  12. Fusion • Given two or more existing models, we wish to create a new model that combines the information. • Remains standard SBML • We provide a tool to support users combining models. • Implemented in “wizard” style • Status: Prototype

  13. Fusion: Matching Tables • Fusion is done primarily by defining matching of SBML components • Compartments, reactions, species, etc. • A series of matching tables • Order is important to deal with dependencies

  14. Fusion Tool Setup Wizard

  15. Species Mapping Table

  16. Reaction Mapping Table

  17. Composition • Connects submodels together to form a hierarchy of models • Submodels are each valid SBML models • Add language features to SBML to support composition • Describe hierarchy • Describe interactions, links, replacements • No information hiding within models • Relationship to fusion: the mappings are the glue.

  18. Composition Hierarchy <model id="Big"> <listOfCompartments> <compartment id="comp1" volume="1"/> </listOfCompartments> <listOfSubmodels> <model id="Little"> <listOfCompartments> <compartment id="comp2" volume="1"/> </listOfCompartments> </model> </listOfSubmodels> </model>

  19. Links <link> <from object="comp1"/> <to object="Submodel_Little" <subobject object="comp2"/> </to> </link> • Issue: Merge or replace attribute information?

  20. Is Composition the Right Model? • Composition allows us to take existing models and use them as components to build larger models • No information hiding • Submodels might fit together more or less well • Links let us replace things in one model with things in another • Good for legacy models(?) • We might do better to build models from components designed to work as components, with proper information hiding.

  21. Aggregation • In aggregation, models are built up from components • Each component could be, for example, a collection of reactions • This collection exposes certain variables for input/output via “ports” • Hopefully this is a natural concept for modelers • Not intended as a solution for reusing legacy models.

  22. Toggle Switch

  23. Iconified Toggle Switch

  24. Toggle Switch Component

  25. Flattening • Flattening generates a standard SBML file from our modified file, for the purpose of running simulations, etc. • An automated form of fusion. • The composition/aggregation language features provide what the user would provide during fusion, so automation is possible.

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