The last 15 years in development of biology were marked with accumulation of unprecedentedly huge arrays of experimental data. The information was amassed with exclusively high rates due to the advent of highly efficient experimental technologies that provided for high throughput genomic sequencing; of functional genomics technologies allowing investigation of expression dynamics of large groups of genes using expression DNA chips; of proteomics methods giving the possibility to analyze protein compositions of cells, tissues, and organs, assess the dynamics of the cell proteome, and reconstruct the networks of protein-protein interactions; and of metabolomics, in particular, high resolution mass spectrometry study of cell metabolites, and distribution of metabolic fluxes in the cells with a concurrent investigation of the dynamics of thousands metabolites in an individual cell. Analysis, comprehension, and use of the tremendous volumes of experimental data reflecting the intricate processes underlying the functioning of molecular genetic systems are unfeasible in principle without the systems approach and involvement of the state-of-the-art information and computer technologies and efficient mathematical methods for data analysis and simulation of biological systems and processes. The need in solving these problems initiated the birth of a new science- postgenomic bioinformatics or systems biology in silico.

The last 15 years in development of biology were marked with accumulation of unprecedentedly huge arrays of experimental data. The information was amassed with exclusively high rates due to the advent of highly efficient experimental technologies that provided for high throughput genomic sequencing; of functional genomics technologies allowing investigation of expression dynamics of large groups of genes using expression DNA chips; of proteomics methods giving the possibility to analyze protein compositions of cells, tissues, and organs, assess the dynamics of the cell proteome, and reconstruct the networks of protein-protein interactions; and of metabolomics, in particular, high resolution mass spectrometry study of cell metabolites, and distribution of metabolic fluxes in the cells with a concurrent investigation of the dynamics of thousands metabolites in an individual cell. Analysis, comprehension, and use of the tremendous volumes of experimental data reflecting the intricate processes underlying the functioning of molecular genetic systems are unfeasible in principle without the systems approach and involvement of the state-of-the-art information and computer technologies and efficient mathematical methods for data analysis and simulation of biological systems and processes. The need in solving these problems initiated the birth of a new science- postgenomic bioinformatics or systems biology in silico.

Bioinformatics of Genome Regulation and Structure covers:

-regulatory genomic sequences: databases, knowledge bases, computer analysis, modeling, and recognition;
-large-scale genome analysis and functional annotation;
-gene structure detection and prediction;
-comparative and evolutionary genomics;
-computer analysis of genome polymorphism and evolution; computer analysis and modeling of transcription, splicing, and translation; structural computational biology: structure-function organization of genomic DNA, RNA, and proteins;
-gene networks, signal transduction pathways, and genetically controlled metabolic pathways: principles of organization, operation, and evolution;
-data warehousing, knowledge discovery and data mining; and,
-analysis of basic patterns of genome operation, organization, and evolution. The data presented may be used while solving a wide range of problems, both basic and applied, in various directions of molecular biology, molecular genetics, biotechnology, pharmacogenetics, pharmacology, and adjacent fields of medicine, veterinary, and agrobiology.

- The data gathered can be used to solve a wide range of problems - for basic science and applied science