Title page for ETD etd-06282007-191549

Type of Document Master's Thesis
Author Lam, Huy Hong
Author's Email Address lamhuy@vt.edu
URN etd-06282007-191549
Title Discrete Transition System Model and Verification for Mitochondrially Mediated Apoptotic Signaling Pathways
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Hsiao, Michael S. Committee Chair
Abbott, A. Lynn Committee Member
Samuels, David C. Committee Member
  • SAT
  • Mitochondria
  • Model Checking
  • Reachability Analysis
  • Generic Algorithm
  • Apoptosis
  • Fault Analysis
Date of Defense 2007-06-20
Availability unrestricted
Computational biology and bioinformatics for apoptosis have been gaining much

momentum due to the advances in computational sciences. Both fields use

extensive computational techniques and modeling to mimic real world

behaviors. One problem of particular interest is on the study of

reachability, in which the goal is to determine if a target state or protein concentration in the model is

realizable for a signaling pathway. Another interesting problem is

to examine faulty pathways and how a fault can make a previously

unrealizable state possible, or vice versa. Such analysis can be extremely

valuable to the understanding of apoptosis. However, these analyses can be costly or even impractical

for some approaches, since they must simulate every aspect of the model.

Our approach introduces an abstracted model to represent a portion

of the apoptosis signaling pathways as a finite state machine. This

abstraction allows us to apply hardware testing and verification techniques

and also study the behaviors of the system without full simulation. We proposed a

framework that is tailor-built to implement these verification techniques

for the discrete model. Through solving Boolean constraint satisfaction

problems (SAT-based) and with guided stimulation (Genetic Algorithm), we

can further extract the properties and behaviors of the system.

Furthermore, our model allows us to conduct cause-effect analysis

of the apoptosis signaling pathways. By constructing single- and

double-fault models, we are able to study what fault(s) can cause the model to

malfunction and the reasons behind it. Unlike simulation, our abstraction

approach allows us to study the system properties and system manipulations

from a different perspective without fully relying on simulation. Using

these observations as hypotheses, we aim to conduct laboratory experiments

and further refine our model.

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