Title page for ETD etd-04302008-115237

Type of Document Dissertation
Author Randhawa, Ranjit
Author's Email Address rrandhawa@vt.edu
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
  • Model Composition and Aggregation
  • Systems Biology
  • Verification & Validation
  • Modeling & Simulation
  • JigCell
  • Macromolecular Regulatory Networks
Date of Defense 2008-04-04
Availability unrestricted
Mathematical 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

larger models.

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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