Tampere University of Technology

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Signal processing methods and information approach to systems biology

Research output: Collection of articlesDoctoral Thesis

Details

Original languageEnglish
Place of PublicationTampere
PublisherTampere University of Technology
Number of pages76
ISBN (Electronic)952-15-1750-6
ISBN (Print)952-15-1690-9
StatePublished - 9 Dec 2006
Publication typeG5 Doctoral dissertation (article)

Publication series

NameTampere University of Technology. Publication
PublisherTampere University of Technology
Volume642
ISSN (Print)1459-2045

Abstract

Recent technological advances have made it possible to observe the behavior of biological systems at the genetic level in a high-throughput manner. The ability to do measurements at the system level has made it possible to move from a traditional reductionistic approach to a more global system level approach. Thus, instead of looking at the behavior of the individual components, the goal of this new approach, the systems biology, is to understand the structural and dynamical properties of the system as a whole. Living systems differ from non-living systems, for example, by their ability to process information from their environment and to propagate information over time through the mechanism of evolution. As information processing is a fundamental property of all living systems, we can gain insight into the system level properties by studying the information processing and flow. For example, how information is propagated through the evolution or how the system responses to a perturbation. The content of this thesis is two-fold. In the first part we introduce new signal processing methods for the computational analysis of the biological data. The purpose of the proposed methods is to improve the reliability and the quality of the microarray data. We introduce an unsupervised approach that can be used to verify the clinically determined class labels for the samples. Next we discuss the identification and quantification of the microarray noise sources. We introduce a simulation model that can be used to simulate microarray data with realistic biological and statistical characteristics by utilizing the noise properties of real data. Finally, we discuss how supplemental measurement data can be used to improve the quality of microarray data. As a case study, we show how the cell population distribution can be estimated using fluorescent activated cell sorter data. The second part of the thesis introduces an information-based approach for studying the complex systems. By using the Kolmogorov complexity based information measure we show how the information processing and flow in biological systems can be used to characterize their structure and behavior at the system level. We show that through the information flow, we can discover evolutionary relationships between organisms. In addition we study the information processing of an innate immunity cell macrophage and show that the dynamics of its information processing exhibit criticality.

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