Computational challenges in the analysis of time series gene expression data

Ziv Bar-Joseph

Abstract

Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (for example, handling the different non uniform sampling rates). In this talk I will discuss the current research in this area. I will present problems and (partial) solutions in a number of different analysis levels ranging from experimental design to the data analysis and to systems biology. The main goal of this talk is to expose you to the wide range of computational problems that arise when analyzing time series expression data. Thus, the talk will be self contained and no prior biological knowledge will be required or assumed.

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