Causality analysis in biological networks

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Ph.D.) -- Bilkent University, 2010.Includes bibliographical references leaves 69-78.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.Babur, ÖzgünPh.D

BP_Prior v2.3.2

<p>BioPAX (BP) Prior s a tool for people who work with proteomics data and want to conduct pathway-context aware analysis. Given a set of annotated protein states, (such as phosphorylations, active/inactive tags and concentration levels), this programs creates a map from the input states onto the Pathway Commons 2 entities and finds the minimum distance between them -- the distances are extracted from BioPAX graphs. The ouput, so called prior information network, is a tab-limited file in Simple Interaction Format and it contains:</p> <p>- Source/target entities</p> <p>- Directional distance measure between these two entities</p> <p>- Pubmed IDs associated with links as external references</p> <p>- Reactions and their types included in the path between two entities.</p

BP_Prior v2.11.0

<p>BioPAX (BP) Prior s a tool for people who work with proteomics data and want to conduct pathway-context aware analysis. Given a set of annotated protein states, (such as phosphorylations, active/inactive tags and concentration levels), this programs creates a map from the input states onto the Pathway Commons 2 entities and finds the minimum distance between them -- the distances are extracted from BioPAX graphs. The ouput, so called prior information network, is a tab-limited file in Simple Interaction Format and it contains:</p> <p>- Source/target entities</p> <p>- Directional distance measure between these two entities</p> <p>- Pubmed IDs associated with links as external references</p> <p>- Reactions and their types included in the path between two entities.</p

SBGNViz: A Tool for Visualization and Complexity Management of SBGN Process Description Maps.

BACKGROUND:Information about cellular processes and pathways is becoming increasingly available in detailed, computable standard formats such as BioPAX and SBGN. Effective visualization of this information is a key recurring requirement for biological data analysis, especially for -omic data. Biological data analysis is rapidly migrating to web based platforms; thus there is a substantial need for sophisticated web based pathway viewers that support these platforms and other use cases. RESULTS:Towards this goal, we developed a web based viewer named SBGNViz for process description maps in SBGN (SBGN-PD). SBGNViz can visualize both BioPAX and SBGN formats. Unique features of SBGNViz include the ability to nest nodes to arbitrary depths to represent molecular complexes and cellular locations, automatic pathway layout, editing and highlighting facilities to enable focus on sub-maps, and the ability to inspect pathway members for detailed information from EntrezGene. SBGNViz can be used within a web browser without any installation and can be readily embedded into web pages. SBGNViz has two editions built with ActionScript and JavaScript. The JavaScript edition, which also works on touch enabled devices, introduces novel methods for managing and reducing complexity of large SBGN-PD maps for more effective analysis. CONCLUSION:SBGNViz fills an important gap by making the large and fast-growing corpus of rich pathway information accessible to web based platforms. SBGNViz can be used in a variety of contexts and in multiple scenarios ranging from visualization of the results of a single study in a web page to building data analysis platforms

BP_Prior v2.9.1 (a.k.a. PERA)

<p>BioPAX (BP) Prior (also known as PERA) is a tool for people who work with proteomics data and want to conduct pathway-context aware analysis. Given a set of annotated protein states, (such as phosphorylations, active/inactive tags and concentration levels), this programs creates a map from the input states onto the Pathway Commons 2 entities and finds the minimum distance between them -- the distances are extracted from BioPAX graphs. The ouput, so called prior information network, is a tab-limited file in Simple Interaction Format and it contains:</p> <p>- Source/target entities</p> <p>- Directional distance measure between these two entities</p> <p>- Pubmed IDs associated with links as external references</p> <p>- Reactions and their types included in the path between two entities.</p