DOC PREVIEW
Stanford CS 374 - Lecture Notes

This preview shows page 1-2-3-4-5 out of 14 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 14 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Construction of Networks from Diverse Data Sources Presenter: Neda Nategh CS374, Fall 2006 Scribe: Nandhini N S Lecture 16, 11/7/2006 Construction of Networks from diverse data sources Index 1.1 Introduction 1.2 Basics of Interaction Networks 1.3 Defining Gene Fusion and its Utility 1.4 Listing the experiments used to build interaction networks. 1.5 Some Statistics Terminology 1.6 About KEGG. 2.1 On Building a Probabilistic Functional Network of yeast genes. 2.2 A Description of the Functional Block Diagram. 3.1 On Deriving Integrated Protein Interaction Networks for 11 Microbes. 3.2 Describing the algorithm. 3.3 Training Set Generation. 3.4 Network Integration. 3.5 Advantages of the algorithm presented. References: 1. “A Probabilistic Functional Network of Yeast Genes” Insuk Lee, Shailesh V. Date, Alex T. Adai, Edward M. Marcotte, http://www.sciencemag.org, VOL 306, 26 NOVEMBER 2004. 2. “Integrated Protein Interaction Networks for 11 Microbes” Balaji S. Srinivasan, Antal F. Novak, Jason A. Flannick, Serafim Batzoglou, Harley H. McAdams. 3. http://www.genome.jp/kegg/ 4. Wikipedia: http://en.wikipedia.org/ 5. http://www.chem.uic.edu/web1/OCOL-II/WIN/SPEC/MS/MSF.HTM 1.1 Introduction Knowing more about the properties of interaction networks and aligning such interaction networks has always been a topic of active research. Two studies ([1] and [2]) present algorithms to integrate protein networks from diverse data sources. 1.2 Basics of Interaction Networks The properties of an interaction network can be divided into the Biological aspects and the Computational aspects.Construction of Networks from Diverse Data Sources Presenter: Neda Nategh CS374, Fall 2006 Scribe: Nandhini N S Lecture 16, 11/7/2006 The Biological Aspect is represented by two types of interactions between proteins – Physical Interactions and Complex Interactions. Physical Interactions refer to protein pairs which are in direct contact with each other, and complex interactions refer to protein pairs which form part of the same functional module (Fig 1). A complex interaction could be a metabolic pathway, a signaling module or a multiprotein complex. Metabolic Pathway – This is defined as a series of chemical reactions occurring within the cell (catalyzed by enzymes) which either results in the formation of a metabolic product or in the catalysis of yet another pathway (in which case it is known as a flux generating step). Some examples of metabolic pathways are anaerobic respiration, Glycolysis and Krebs cycle (Fig 2). Signal transduction – A process by which a cell converts one kind of signal/stimulus to another. Such processes usually involve a series of biochemical reactions inside the cell, which are carried out by enzymes and linked through secondary messengers. (Source: news.uns.purdue.edu/UNS/images/cramer.photo2.jpeg) Fig 2 A Metabolic Pathway Fig 1 A Protein ComplexConstruction of Networks from Diverse Data Sources Presenter: Neda Nategh CS374, Fall 2006 Scribe: Nandhini N S Lecture 16, 11/7/2006 The goal is to create high coverage protein interaction networks combining different forms of data sources, which in themselves also could be used to create protein interaction networks. In [2], four kinds of evidence – co-expression, co-evolution, coinheritance, and co-location have been used to build probabilistic interaction networks. Co-expression – co-expression is the condition of two genes being expressed together. (Gene Expression, or simply expression is a process by which a gene’s DNA sequence is converted to some structure or functional unit of a cell ; in very generic terms this refers to the conversion from genes to proteins). Microarray techniques are used for expression profiling. Co-evolution – This refers to evolutionary interactions between and within molecules where each element (a gene in this case) exerts an evolutionary influence on the other. Co-inheritance – The property of two proteins inheriting common features due to linkage. Co-Location – The condition of two genes occupying neighboring locations. 1.3 Defining Gene Fusion and its utility Gene fusion is defined as the accidental joining of the DNA of two genes, such as can occur in a translocation or inversion. Gene fusions can give rise to hybrid proteins or to the mis-regulation of the transcription of one gene by the enhancers of the other. By creating a fusion gene of the gene from the protein of interest and that of a green fluorescent protein, the protein of interest may be observed using fluorescence microscopy. 1.4 The following experiments are used to extract information about the above data to create the probabilistic model of the interaction network. 1. Microarray analysis of gene expression. 2. Systematic protein interaction mapping. 3. Mass spectrometry. 4. Yeast two hybrid. 5. Synthetic lethal screens.Construction of Networks from Diverse Data Sources Presenter: Neda Nategh CS374, Fall 2006 Scribe: Nandhini N S Lecture 16, 11/7/2006 Microarray analysis of gene expression A collection of microscopic DNA spots is attached to a solid surface, such as a glass/plastic or silicon chip, forming an array. The analysis of these arrays helps in expression profiling, since this is capable of monitoring the expression levels for thousands of genes simultaneously (Fig 3). (Source: http://accessexcellence.org/RC/VL/GG/microArray.html) Mass Spectrometry In mass spectrometry, a substance is bombarded with an electron beam with sufficient energy to fragment the molecule. The cations and radical cations produced through this technique are of interest: the charge to mass ratio (e/m) is measured for these particles. These measurements aid in obtaining the physical composition of the given sample (protein complex in this case) by using the former to generate the mass spectrum ( the distribution of mass). In context of the current topic, Mass Spectrometry is a technique that can be used for Protein characterization, identification and quantitation. Fig 3 Microarray


View Full Document

Stanford CS 374 - Lecture Notes

Documents in this Course
Probcons

Probcons

42 pages

ProtoMap

ProtoMap

19 pages

Lecture 3

Lecture 3

16 pages

Load more
Download Lecture Notes
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Lecture Notes and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Lecture Notes 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?