Browsing by Subject "bioinformatics"

Now showing 1 - 4 of 4

Open Access
Causality analysis in biological networks
(2010) Babur, Özgün
Systems biology is a rapidly emerging field, shaped in the last two decades or so, which promises understanding and curing several complex diseases such as cancer. In order to get an insight about the system – specifically the molecular network in the cell – we need to work on following four fundamental aspects: experimental and computational methods to gather knowledge about the system, mathematical models for representing the knowledge, analysis methods for answering questions on the model, and software tools for working on these. In this thesis, we propose new approaches related to all these aspects. In this thesis, we define new terms and concepts that helps us to analyze cellular processes, such as positive and negative paths, upstream and downstream relations, and distance in process graphs. We propose algorithms that will search for functional relations between molecules and will answer several biologically interesting questions related to the network, such as neighborhoods, paths of interest, and common targets or regulators of molecules. In addition, we introduce ChiBE, a pathway editor for visualizing and analyzing BioPAX networks. The tool converts BioPAX graphs to drawable process diagrams and provides the mentioned novel analysis algorithms. Users can query pathways in Pathway Commons database and create sub-networks that focus on specific relations of interest. We also describe a microarray data analysis component, PATIKAmad, built into ChiBE and PATIKAweb, which integrates expression experiment data with networks. PATIKAmad helps those tools to represent experiment values on network elements and to search for causal relations in the network that potentially explain dependent expressions. Causative path search depends on the presence of transcriptional relations in the model, which however is underrepresented in most of the databases. This is mainly due to insufficient knowledge in the literature. We finally propose a method for identifying and classifying modulators of transcription factors, to help complete the missing transcriptional relations in the pathway databases. The method works with large amount of expression data, and looks for evidence of modulation for triplets of genes, i.e. modulator - factor - target. Modulator candidates are chosen among the interacting proteins of transcription factors. We expect to observe that expression of the target gene depends on the interaction between factor and modulator. According to the observed dependency type, we further classify the modulation. When tested, our method finds modulators of Androgen Receptor; our top-scoring result modulators are supported by other evidence in the literature. We also observe that the modulation event and modulation type highly depend on the specific target gene. This finding contradicts with expectations of molecular biology community who often assume a modulator has one type of effect regardless of the target gene.
Open Access
Div-blast: Diversification of sequence search results
(Public Library of Science, 2014) Eser, E.; Can, T.; Ferhatosmanoglu H.
Sequence similarity tools, such as BLAST, seek sequences most similar to a query from a database of sequences. They return results significantly similar to the query sequence and that are typically highly similar to each other. Most sequence analysis tasks in bioinformatics require an exploratory approach, where the initial results guide the user to new searches. However, diversity has not yet been considered an integral component of sequence search tools for this discipline. Some redundancy can be avoided by introducing non-redundancy during database construction, but it is not feasible to dynamically set a level of non-redundancy tailored to a query sequence. We introduce the problem of diverse search and browsing in sequence databases that produce non-redundant results optimized for any given query. We define diversity measures for sequences and propose methods to obtain diverse results extracted from current sequence similarity search tools. We also propose a new measure to evaluate the diversity of a set of sequences that is returned as a result of a sequence similarity query. We evaluate the effectiveness of the proposed methods in post-processing BLAST and PSIBLAST results. We also assess the functional diversity of the returned results based on available Gene Ontology annotations. Additionally, we include a comparison with a current redundancy elimination tool, CD-HIT. Our experiments show that the proposed methods are able to achieve more diverse yet significant result sets compared to static non-redundancy approaches. In both sequencebased and functional diversity evaluation, the proposed diversification methods significantly outperform original BLAST results and other baselines. A web based tool implementing the proposed methods, Div-BLAST, can be accessed at cedar.cs.bilkent.edu.tr/Div-BLAST © 2014 Eser et al.
Open Access
In silico analysis of mutant p53(R249S) oncogenicity in hepatocellular carcinoma
(2007) Ovezmuradov, Guvanchmurad
Oncogenic properties of mutant p53 proteins still stand as an ill-known subject, and the mechanism responsible for this phenomenon remains to be uncovered. This thesis aims to uncover the effect of p53 codon R249S ((AGG→AGT, arginine to serine) mutation on the development of hepatocellular carcinoma (HCC) through high throughput transcriptomics analysis using oligonucleotide arrays. We compared the expression profiles of HepG2 cells carrying wt and mutant p53(R249S). Microarray data analysis revealed a molecular signature consisting of 84 differentially regulated genes, showing that the expression of mutant p53(R249S) in HepG2 cells resulted in a distinct expression profile. Furthermore, mapping these significant differentiallyexpressed genes to the p53 interaction network revealed a putative interaction network representing functional outcomes of p53(R249S) expression in the context of diverse molecular interactions. Our results clearly demonstrated that several Hepatocyte Nuclear Factors (HNF1A, HNF4A and HNF6) could play an essential role in mediating mutant p53 oncogenic activity in HCC, as the key molecules of the gene network.
Open Access
Modeling cellular processes with PATİKA
(2001) Demir, Emek
Availability of the sequences of entire genomes shifts the scientific curiosity toward the identification of function of the genomes in large scale as in genome studies. In the near future data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. We define an ontology for a comprehensive representation of cellular events. The model presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporations of the stored data, as well as multiple levels of abstraction. Based on this model, we present an integrated environment named PATIKA (Pathway Analysis Tool for Integration and Knowledge Acquisition). PATIKA is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that PATIKA will be a valuable tool for rapid knowledge acquisition; micro array generated large-scale data interpretation; disease gene identification and drug development