The need to analyze and closely study the gene related mechanisms motivated the
research on the modeling and control of gene regulatory networks (GRN). Dierent
approaches exist to model GRNs / they are mostly simulated as mathematical models
that represent relationships between genes. Though it turns into a more challenging
problem, we argue that partial observability would be a more natural and realistic
method for handling the control of GRNs. Partial observability is a fundamental
aspect of the problem / it is mostly ignored and substituted by the assumption that
states of GRN are known precisely, prescribed as full observability. On the other hand,
current works addressing partially observability focus on formulating algorithms for
the nite horizon GRN control problem. So, in this work we explore the feasibility of
realizing the problem in a partially observable setting, mainly with Partially Observable
Markov Decision Processes (POMDP). We proposed a POMDP formulation for
the innite horizon version of the problem. Knowing the fact that POMDP problems
suer from the curse of dimensionality, we also proposed a POMDP solution method
that automatically decomposes the problem by isolating dierent unrelated parts of
the problem, and then solves the reduced subproblems. We also proposed a method
to enrich gene expression data sets given as input to POMDP control task, because
in available data sets there are thousands of genes but only tens or rarely hundreds of
samples. The method is based on the idea of generating more than one model using
the available data sets, and then sampling data from each of the models and nally
ltering the generated samples with the help of metrics that measure compatibility,
diversity and coverage of the newly generated samples.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614317/index.pdf |
| Date | 01 April 2012 |
| Creators | Erdogdu, Utku |
| Contributors | Polat, Faruk |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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