Dougherty E.R., Shmulevich I., Chen J., Wang Z.J. (eds.) Genomic Signal Processing and Statistics

Издательство Hindawi, 2005, -449 pp.No single agreed-upon definition seems to exist for the term bioinformatics, which has been used to mean a variety of things, ranging in scope and focus. To cite but a few examples from textbooks, Lodish et al. state that bioinformatics is the rapidly developing area of computer science devoted to collecting, organizing, and analyzing DNA and protein sequences [1]. A more general and encompassing definition, given by Brown, is that bioinformatics is the use of computer methods in studies of genomes [2].More general still, bioinformatics is the science of refining biological information into biological knowledge using computers [3]. Kohane et al. observe that the breadth of this commonly used definition of bioinformatics risks relegating it to the dustbin of labels too general to be useful and advocate being more specific about the particular bioinformatics techniques employed [4].
Genomic signal processing (GSP) is the engineering discipline that studies the processing of genomic signals, by which we mean the measurable events, principally the production of mRNA and protein, that are carried out by the genome. Based upon current technology, GSP primarily deals with extracting information from gene expression measurements. The analysis, processing, and use of genomic signals for gaining biological knowledge constitute the domain of GSP. The aim of GSP is to integrate the theory and methods of signal processing with the global understanding of functional genomics, with special emphasis on genomic regulation [5]. Hence, GSP encompasses various methodologies concerning expression profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks. GSP is a fundamental discipline that brings to genomics the structural model-based analysis and synthesis that form the basis of mathematically rigorous engineering.
Recent methods facilitate large-scale surveys of gene expression in which transcript levels can be determined for thousands of genes simultaneously. In particular, expression microarrays result from a complex biochemical-optical system incorporating robotic spotting and computer image formation and analysis [6, 7, 8, 9, 10]. Since transcription control is accomplished by a method that interprets a variety of inputs, we require analytical tools for the expression profile data that can detect the types of multivariate influences on decision making produced by complex genetic networks. Put more generally, signals generated by the genome must be processed to characterize their regulatory effects and their relationship to changes at both the genotypic and phenotypic levels. Application is generally directed towards tissue classification and the discovery of signaling pathways.
Because transcriptional control is accomplished by a complex method that interprets a variety of inputs, the development of analytical tools that detect multivariate influences on decision making present in complex genetic networks is essential. To carry out such an analysis, one needs appropriate analytical methodologies. Perhaps the most salient aspect of GSP is that it is an engineering discipline, having strong roots in signals and systems theory. In GSP, the point of departure is that the living cell is a system in which many interacting components work together to give rise to execution of normal cellular functions, complex behavior, and interaction with the environment, including other cells. In such systems, the whole is often more than the sum of its parts, frequently referred to as emergent or complex behavior. The collective behavior of all relevant components in a cell, such as genes and their products, follows a similar paradigm, but gives rise to much richer behavior, that is characteristic of living systems. To gain insight into the behavior of such systems, a systems-wide approach must be taken. This requires us to produce a model of the components and their interactions and apply mathematical, statistical, or simulation tools to understand its behavior, especially as it relates to experimental data.Sequence Analysis.
Representation and analysis of DNA sequences.Signal Processing and Statistics Methodologies in Gene Selection.
Gene feature selection.
Classification.
Clustering: revealing intrinsic dependencies in microarray data.
From biochips to laboratory-on-a-chip system.Modeling and Statistical Inference of Genetic Regulatory Networks.
Modeling and simulation of genetic regulatory networks by ordinary differential equations.
Modeling genetic regulatory networks with probabilistic Boolean networks.
Bayesian networks for genomic analysis.
Statistical inference of transcriptional regulatory networks.Array Imaging, Signal Processing in Systems Biology, and Applications in Disease Diagnosis and Treatments.
Compressing genomic and proteomic array images for statistical analyses.
Cancer genomics, proteomics, and clinic applications.
Integrated approach for computational systems biology.