Sekar, John
(2015)
Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Rule-based modeling is a graph-based approach to specifying the kinetics of cell signaling
systems. A reaction rule is a compact and explicit graph-based representation of a kinetic process,
and it matches a class of reactions that involve identical sites and identical kinetics. Compact rule-
based models have been used to generate large and combinatorially complex reaction networks,
and rules have also been used to compile databases of kinetic interactions targeting specific cells
and pathways. In this work, I address three technological challenges associated with rule-based
modeling. First, I address the ability to generate an automated global visualization of a rule-based
model as a network of signal flows. I showed how to analyze a reaction rule and extract a set of
bipartite regulatory relationships, which can be aggregated across rules into a global network. I
also provide a set of coarse-graining approaches to compress an automatically generated network
into a compact pathway diagram, even for models with 100s of rules. Second, I resolved an
incompatibility between two recent advances in rule-based modeling: network-free simulation
(which enables simulation without generating a reaction network), and energy-based rule-based
modeling (which enables specifying a model using cooperativity parameters and automated
accounting of free energy). The incompatibility arose because calculating the reaction rate requires
computing the reaction free energy in an energy-based model, and this requires knowledge of both
reactants and products of the reaction, but the products are not available in a network-free
simulation until after the reaction event has fired. This was resolved by expanding each energy-
based rule into a number of normal reaction rules for which reaction free energies can be calculated
unambiguously. Third, I demonstrated a particular type of modularization that is based on treating
a set of rules as a module. This enables building models from combinations of modular hypotheses
and supplements the other modularization strategies such as macros, types and energy-based
compression.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
15 December 2015 |
| Date Type: |
Publication |
| Defense Date: |
4 December 2015 |
| Approval Date: |
15 December 2015 |
| Submission Date: |
14 December 2015 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
189 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Medicine > Computational Biology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Thesis Dissertation of John Arul Prakash Sekar. |
| Date Deposited: |
15 Dec 2015 17:20 |
| Last Modified: |
15 Nov 2016 14:31 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/26674 |
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