Type of Document Dissertation Author Randhawa, Ranjit Author's Email Address firstname.lastname@example.org URN etd-04302008-115237 Title Model Composition and Aggregation in Macromolecular Regulatory Networks Degree PhD Department Computer Science Advisory Committee
Advisor Name Title Shaffer, Clifford A. Committee Chair Tyson, John J. Committee Co-Chair Balci, Osman Committee Member Ramakrishnan, Naren Committee Member Sible, Jill C. Committee Member Keywords
- Model Composition and Aggregation
- Systems Biology
- Verification & Validation
- Modeling & Simulation
- Macromolecular Regulatory Networks
Date of Defense 2008-04-04 Availability unrestricted AbstractMathematical models of regulatory networks become more difficult to construct
and understand as they grow in size and complexity. Large regulatory network models
can be built up from smaller models, representing subsets of
reactions within the larger network. This dissertation focuses on
novel model construction techniques that extend the ability of
biological modelers to construct larger models by supplying them with tools
for decomposing models and using the resulting components to construct
Over the last 20 years, molecular biologists have amassed a great
deal of information about the genes and proteins that carry out
fundamental biological processes within living cells --- processes
such as growth and reproduction, movement, signal reception and
response, and programmed cell death. The full complexity of these
macromolecular regulatory networks is too great to tackle mathematically
at the present time. Nonetheless, modelers have had success building
dynamical models of restricted parts of the network. Systems
biologists need tools now to support composing "submodels" into
more comprehensive models of integrated regulatory networks.
We have identified and developed four novel processes
(fusion, composition, flattening, and aggregation)
whose purpose is to support the construction of larger models.
Model Fusion combines two or more models in an irreversible manner.
In fusion, the identities of the original (sub)models are lost.
Beyond some size, fused models will become too complex
to grasp and manage as single entities. In this case, it may be
more useful to represent large models as compositions of distinct
components. In Model Composition one thinks of models not as monolithic entities but
rather as collections of smaller components (submodels) joined
together. A composed model is built from two or more submodels by
describing their redundancies and interactions.
While it is appealing in the short term to build larger models from
pre-existing models, each developed independently for their own
purposes, we believe that ultimately it will become necessary to
build large models from components that have been designed for the
purpose of combining them. We define Model Aggregation as a restricted form of
composition that represents a collection of model elements as a
single entity (a "module"). A module contains a definition of
pre-determined input and output ports. The process
of aggregation (connecting modules via their interface ports) allows
modelers to create larger models in a controlled manner.
Model Flattening converts a composed or aggregated model with some
hierarchy or connections to one without such connections.
The relationships used to
describe the interactions among the submodels are lost, as the
composed or aggregated model is converted into a single large (flat)
model. Flattening allows us to use existing simulation
tools, which have no support for composition or aggregation.
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