Genomic Signal Processing
Genomic Signal Processing (GSP) is the engineering discipline that studies the processing of genomic signals.The theory of signal processing is utilized in both structural and functional understanding. 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.
Gene prediction typically refers to the area of computational that is concerned with algorithmically identifying biology genomic DNA, that are stretches of sequence, usually biologically functional. This especially includes protein-genes, but may also include other functional coding RNA genes and regulatory regions. Gene elements such as finding is
one of the first and most important steps in understanding the genome of a species once it has been sequenced.
Genomic signal processing (GSP) is the engineering discipline that studies the processing of genomic signals. Owing to the major role played in genomics by transcriptional signaling and the related pathway modeling, it is only natural that the theory of signal processing should be utilized in both structural and functional understanding. 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. Hence, GSP encompasses various methodologies concerning expression profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks. GSP isa fundamental discipline that brings to genomics the structural model-based analysis and synthesis that form the basis of mathematically rigorous engineering.
Application is generally directed towards tissue classification and the discovery of signaling pathways, both based on the expressed macromolecule phenotype of the cell. Accomplishment of these aims requires a host of signal processing approaches. These include signal representation relevant to transcription, such as wavelet decomposition and more general decompositions of stochastic time series, and system modeling using nonlinear dynamical systems. The kind of correlation-based analysis commonly used for understand ing pairwise relations between genes or cellular effects cannot capture the complex network of nonlinear information processing based upon multivariate inputs from inside and outside the genome. Regulatory models require the kind of nonlinear dynamics studied in signal processing and control, and in particular the use of stochastic dataflow networks common to distributed computer systems with stochastic inputs. This is not to say that existing model systems suffice. Genomics requires its own model systems, not simply straightforward adaptations of currently formulated models. New systems must capture the specific biological mechanisms of operation and distributed regulation at work withinthe genome. It is necessary to develop appropriate mathematical theory, including optimization, for the kinds of external controls required for therapeutic intervention as well as approximation theory to arrive at nonlinear dynamical models that are sufficiently complex to adequately represent genomic regulation for diagnosis and therapy while not being overly complex for the amounts of data experimentally feasible or for the computational limits of existing computer hardware.
A cell relies on its protein components for a wide variety of its functions, including energy production, biosynthesis of component macromolecules, maintenance of cellular architecture, and the ability to act upon intra- and extra-cellular stimuli. Each cell in an organism contains the information necessary to produce the entire repertoire of proteins the organism can specify. Since a cell’s specific functionality is largely determined by the genes it is expressing, it is logical that transcription, the first step in the process of converting the genetic information stored in an organism’s genome into protein, would be highly regulated by the control network that coordinates and directs cellular activity. A primary means for regulating cellular activity is the control of pro tein production via the amounts of mRNA expressed by in dividual genes. The tools to build an understanding of genomic regulation of expression will involve the characterization of these expression levels. Microarray technology, both cDNA and oligonucleotide, provides a powerful analytic tool for genetic research. Since our concern in this paper is to articulate the salient issues for GSP, and not to delve deeply into microarray technology, we confine our brief discussion to cDNA microarrays.
Complementary DNA microarray technology combines robotic spotting of small amounts of individual, pure nucleic acid species on a glass surface, hybridization to this array with multiple fluorescently labeled nucleic acids, and detection and quantitation of the resulting fluor-tagged hybrids by a scanning confocal microscope. A basic application is quantitative analysis of fluorescence signals representing the relative abundance of mRNA from distinct tissue samples. Complementary DNA microarrays are prepared by printing thousands of cDNAs in an array format on glass microscope slides, which provide gene-specific hybridization targets. Distinct mRNA samples can be labeled with different fluors and then co-hybridized onto each arrayed gene. Ratios (or sometimes the direct intensity measurements) of gene expression levels between the samples can be used to detect meaningfully different expression levels between the samples for a given gene. Given an experimental design with multiple tissue samples, microarray data can be used to cluster genes based on expression profiles, to characterize and classify disease based on the expression levels of gene sets, and for other signal processing tasks.
Genomic Signal Processing (GSP) is the engineering discipline that studies the processing of genomic signals.The theory of signal processing is utilized in both structural and functional understanding. 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.
Gene prediction typically refers to the area of computational that is concerned with algorithmically identifying biology genomic DNA, that are stretches of sequence, usually biologically functional. This especially includes protein-genes, but may also include other functional coding RNA genes and regulatory regions. Gene elements such as finding is
one of the first and most important steps in understanding the genome of a species once it has been sequenced.
Genomic signal processing (GSP) is the engineering discipline that studies the processing of genomic signals. Owing to the major role played in genomics by transcriptional signaling and the related pathway modeling, it is only natural that the theory of signal processing should be utilized in both structural and functional understanding. 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. Hence, GSP encompasses various methodologies concerning expression profiles: detection, prediction, classification, control, and statistical and dynamical modeling of gene networks. GSP isa fundamental discipline that brings to genomics the structural model-based analysis and synthesis that form the basis of mathematically rigorous engineering.
Application is generally directed towards tissue classification and the discovery of signaling pathways, both based on the expressed macromolecule phenotype of the cell. Accomplishment of these aims requires a host of signal processing approaches. These include signal representation relevant to transcription, such as wavelet decomposition and more general decompositions of stochastic time series, and system modeling using nonlinear dynamical systems. The kind of correlation-based analysis commonly used for understand ing pairwise relations between genes or cellular effects cannot capture the complex network of nonlinear information processing based upon multivariate inputs from inside and outside the genome. Regulatory models require the kind of nonlinear dynamics studied in signal processing and control, and in particular the use of stochastic dataflow networks common to distributed computer systems with stochastic inputs. This is not to say that existing model systems suffice. Genomics requires its own model systems, not simply straightforward adaptations of currently formulated models. New systems must capture the specific biological mechanisms of operation and distributed regulation at work withinthe genome. It is necessary to develop appropriate mathematical theory, including optimization, for the kinds of external controls required for therapeutic intervention as well as approximation theory to arrive at nonlinear dynamical models that are sufficiently complex to adequately represent genomic regulation for diagnosis and therapy while not being overly complex for the amounts of data experimentally feasible or for the computational limits of existing computer hardware.
A cell relies on its protein components for a wide variety of its functions, including energy production, biosynthesis of component macromolecules, maintenance of cellular architecture, and the ability to act upon intra- and extra-cellular stimuli. Each cell in an organism contains the information necessary to produce the entire repertoire of proteins the organism can specify. Since a cell’s specific functionality is largely determined by the genes it is expressing, it is logical that transcription, the first step in the process of converting the genetic information stored in an organism’s genome into protein, would be highly regulated by the control network that coordinates and directs cellular activity. A primary means for regulating cellular activity is the control of pro tein production via the amounts of mRNA expressed by in dividual genes. The tools to build an understanding of genomic regulation of expression will involve the characterization of these expression levels. Microarray technology, both cDNA and oligonucleotide, provides a powerful analytic tool for genetic research. Since our concern in this paper is to articulate the salient issues for GSP, and not to delve deeply into microarray technology, we confine our brief discussion to cDNA microarrays.
Complementary DNA microarray technology combines robotic spotting of small amounts of individual, pure nucleic acid species on a glass surface, hybridization to this array with multiple fluorescently labeled nucleic acids, and detection and quantitation of the resulting fluor-tagged hybrids by a scanning confocal microscope. A basic application is quantitative analysis of fluorescence signals representing the relative abundance of mRNA from distinct tissue samples. Complementary DNA microarrays are prepared by printing thousands of cDNAs in an array format on glass microscope slides, which provide gene-specific hybridization targets. Distinct mRNA samples can be labeled with different fluors and then co-hybridized onto each arrayed gene. Ratios (or sometimes the direct intensity measurements) of gene expression levels between the samples can be used to detect meaningfully different expression levels between the samples for a given gene. Given an experimental design with multiple tissue samples, microarray data can be used to cluster genes based on expression profiles, to characterize and classify disease based on the expression levels of gene sets, and for other signal processing tasks.
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