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Using evolutionary algorithms in the modeling and analysis of gene signaling pathways

Dr. Sorin Draghici
Professor in the Department of Clinical and Translational Science
Director of the Intelligent Systems and Bioinformatics Laboratory
Wayne State University


In molecular biology and genetics, our data gathering capabilities have greatly surpassed the available data analysis techniques. Once heralded as the holy grail, the capability of obtaining a comprehensive lists of genes, proteins and metabolites that are different between the disease and normal phenotypes is routine today. And yet, the holy grail of high-throughput has not delivered so far. Even though such high-throughput comparisons are relatively easy to perform, understanding the phenomena that determine the measured changes is as challenging as ever, if not more so. There is a large gap between our ability to collect data and our ability to interpret it. Hence, a crucial challenge is to develop effective ways to analyze the vast amount of data that has been and will continue to be collected.

At the same time, living organisms are complex systems whose emerging phenotypes are the results of thousands of complex interactions taking place on various metabolic and signaling pathways. Being able to correctly infer the perturbed pathways interactions that cause the disease from a list of differentially expressed (DE) genes or proteins may be the key to transforming the now abundant high-throughput expression data into biological knowledge.

In the first part, this tutorial will describe the goals of pathway analysis, a few approaches used in the analysis of gene signaling networks, as well as the current challenges in this area. In the second part, the tutorial will address the use of evolutionary approaches in this area. Examples using real gene expression data and real pathways will illustrate the capabilities and limitations of various methods, as well as challenges faced when adopting an evolutionary approach.

Goal of the Tutorial:

The intended audience will be those students and practitioners who are interested in using evolutionary approaches in the analysis of gene signaling networks and other biological pathways. The goals of the tutorial include providing the audience with an understanding of the current methods used in the analysis of biological pathways, current work in this area, and the potential for advancing this area using evolutionary approaches.


What is a pathway?

A basic description of a pathway. Pathways as graphs with their basic properties. Biochemical pathways vs. signaling pathways vs. protein-protein interaction networks. Similarities and differences from an analysis point of view.

What are the goals of pathway analysis?

This will discuss the common inputs of this process, the expected outputs, and the reasons for them.

What are some of the main methods used in this area?

This will cover enrichment analysis (enrichment score, hypergeometric, SAFE, etc.), functional class scoring (Gene Set Enrichment Analysis - GSEA), and impact analysis (Pathway-Express, Pathway-Guide, SPIA). Other methods may be covered if the time allows. These methods will be illustrated showing the evolution of ideas, from the simplest to the most evolved. The presentation will include detailed comparisons on real data sets and real pathways.

Some challenges in this area:

The discussion will include the lack of a gold standard, the insufficient validation currently used when new methods are introduced, the noise intrinsically associated with genomic data, etc.

The use of evolutionary approaches in this area:

What can be achieved with evolutionary approaches. Why these approaches are promising?

Practical examples of using evolutionary algorithms in this area:

This part will present some experiments and results using genetic algorithms to discover the degree to which various genes participate in the processes described on various pathways. Real data from 24 experiments will be included in the analysis.



Sorin Draghici has obtained his B.Sc. and M.Sc. degrees in Computer Engineering from "Politehnica" University in Bucharest, Romania followed by a Ph.D. degree in Computer Science from University of St-Andrews (third oldest university in UK after Oxford and Cambridge). He currently holds the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, and is a professor in the Department of Clinical and Translational Science and the Department of Computer Science, as well as the head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University (http://vortex.cs.wayne.edu). He is also the chief of the Systems Biology in Reproduction of the Perinatology Research Branch of the National Institute for Child Health and Development and founder and CEO of Advaita Corporation (www.advaitabio.com). A senior member of IEEE, Dr. ghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine.

His publications include two books ("Data Analysis Tools for DNA Microarrays and Statistics" and "Data Analysis for Microarrays using R", both published by CRC Press in 2003 and 2012, respectively), 8 book chapters, and over 100 peer-reviewed journal and conference publications which gathered over 5,000 citations. His best known work is in the area of computerized ontological analysis of high-throughput gene expression experiments. His research in this area has produced the first tool for ontological analysis (Onto-Express, 2002), as well as a set of other 5 related tools (Onto-Tools). These have been made available as a service to the community, from the web page of his laboratory at http://vortex.cs.wayne.edu, and are currently used by more than 11,000 scientists from over 50 countries. His second major contribution is in the area of pathway analysis. In this area, the impact analysis published in 2007 in Genome Research was the first approach able to incorporate topology in the analysis of signaling pathways, and currently has more than 250 citations. His research focused on developing new methods for pathway analysis is currently funded by two NIH grants and one NSF grant totaling over 5 million dollars.


The length of the tutorial:
two hours.


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