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Ranjit Randhawa* Clifford A. Shaffer* John J. Tyson + Departments of Computer Science* and Biology + Virginia Tech Blacksburg, VA 24061. Fusing and Composing Macromolecular Regulatory Network Models. Regulatory Network Modeling.
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Ranjit Randhawa* Clifford A. Shaffer* John J. Tyson+ Departments of Computer Science* and Biology+ Virginia Tech Blacksburg, VA 24061 Fusing and Composing Macromolecular Regulatory Network Models
Regulatory Network Modeling • Wish to deduce physiological properties of a cell from wiring diagrams of control systems
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.
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).
Solution • Some mechanism must be found to describe models as collections of small building blocks that are combined to form the full model.
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.
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
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
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
Composition and the Fusion Wizard • There are significant similarities between fusion and composition • Fusion defines a series of steps taken to merge models • Series of steps captured by the fusion tool can be viewed as an “audit trail” used in generating the mapping tables • Precisely this same information can be used to describe the set of instructions needed to connect/link the submodels for composition
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>
Links <link> <from object="comp1"/> <to object="Submodel_Little" <subobject object="comp2"/> </to> </link>
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.
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.
Flattening • Flattening generates a standard SBML file from our modified file, for the purpose of running simulations, etc. • An automated form of fusion. • The additional language features provide what the user would provide during fusion, so automation is possible.
Summary • We recognize four distinct activities related to model decomposition [Status] • Fusion: Take existing models and merge them [Implemented] • Composition: Build up from existing models, no information hiding [Implemented] • Aggregation: Build up from building blocks, controlled interfaces [Designing stage] • Flattening: Merge the building blocks back into a “flat” (non-composed) model (for making simulation runs) [Implemented]