**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 is a 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 understanding pair-wise 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 within the genome. It is necessary to develop appropriate mathematical theory, including optimization for the kinds of external controls required for therapeutic intervention and approximation theory to arrive at nonlinear dynamical models that are sufficiently complex to adequately represent genomic regulation for diagnosis and therapy, and also not too complex for the amounts of data experimentally feasible or for the computational limits of existing computer hardware.**