Akademik Veri Yönetim Sistemi
Tezin Türü: Doktora
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2014
Öğrenci: ÖYKÜ EREN ÖZSOY
Danışman: TOLGA CAN
Özet:Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction of biological networks. We propose an integer linear programming (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small networks of size 10-15 genes due to computational complexity. In order to scale for large networks, we propose a divide and conquer based heuristic, in which a given reference network is divided into smaller sub-networks that are solved separately and the solutions are merged together to form the solution for the large network. However the solution that we have developed for reconstruction of signaling networks using RNA interference data is not suitable for networks with multiple sources and sinks. In order to handle such networks, we use gene expression data and develop another ILP based graph theoretical method. We validate our proposed approaches on real, semi-synthetic and synthetic data sets, and comparison with the state of the art shows that our proposed approaches are able to scale better.