Gene regulatory networks model the interactions within the cell and thus it is essential to understand their structure and to develop some control mechanisms that could effectively deal with them. This dissertation tackles these two aspects. To handle the first problem, a new constraint-based modeling algorithm is proposed that can both increase the quality of the output and decrease the computational requirements for learning the structure of gene regulatory networks by integrating multiple biological data types and applying a special method for dense nodes in the network. Constraint-based structure learning algorithms generally perform well on sparse graphs and it is true that sparsity is not uncommon. However, some domains like gene regulatory networks are characterized by the possibility of having some dense regions in the underlying graph and the proposed algorithm is capable of dealing with this issue. The algorithm is based on a well-known structure learning algorithm called the PC algorithm, and extends it in multiple aspects. Once a network exists, we could address the second problem, namely control of the gene regulatory network for various applications where the curse of dimensionality is the main issue. It is possible that hundreds of genes may regulate one biological activity in an organism and this implies a huge state space even in the case of Boolean models. The thesis proposes effective methods to find control policies for large-scale networks. The modeling and control algorithms proposed in this dissertation have been evaluated on both synthetic and real data sets. The test results demonstrate the efficiency and effectiveness of the proposed approaches.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610619/index.pdf |
| Date | 01 June 2009 |
| Creators | Tan, Mehmet |
| 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 public access |
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