Pinter, Ron Y. (Tech. - Israel Inst. of Tech.)

Static and Dynamic Methods for the Analysis of Biological Networks

Scheduled for presentation during the Mini-Symposium "Genomic Signals and Systems" (TuP10), Tuesday, July 25, 2006, 17:25−17:50, Room 103

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

Elucidating the behavior of biological networks requires the development and the application of computational tools and techniques. Naturally, different methods are suitable for different kinds of analyses, e.g. extracting qualitative vs. observing quantitative properties, employing discrete vs. continuous modeling, and utilizing static vs. dynamic analysis. Specifically, methods that are static in nature extract structural and semantic properties from the description of a network or a pathway; dynamic methods aim at understanding and predicting the functional characteristics of a network's or a pathway's behavior.

In this talk I will present a number of methods that we have devised recently to analyze both metabolic as well as regulatory pathways along with the biological consequences that they have yielded. Specifically, the talk will include a detailed description of:

1. A method for the alignment of metabolic pathways [Pinter et al.,Bioinformatics 2005]: Here we present MetaPathwayHunter, a tool that - given a query pathway and a collection of pathways - finds and reports all approximate occurrences of the query in the collection, ranked by similarity and statistical significance. It is based on an efficient graph matching algorithm that extends the functionality of known techniques. The program also supports a visualization interface with which the alignment of two homologous pathways can be graphically displayed. We employed this tool to study the similarities and differences in the metabolic networks of the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae, as represented in highly curated databases. We reaffirmed that most known metabolic pathways common to both species are conserved. Furthermore, wediscovered a few intriguing relationships between pathways that provide insight into the evolution of metabolic pathways. We conclude with a description of biologically meaningful meta-queries, demonstrating the power and flexibility of our new tool in the analysis of metabolic pathways.

2. HFPN-based simulation of metabolic pathways [Assaraf et al., JTB 2006]: Here we devise a hybrid functional Petri nets (HFPN) modeling of folate metabolism under physiological and antifolate inhibit