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DNA microarrays (DNA chips) are widely used to perform a large number of DNA hybridization experiments in parallel. Current microarrays allow studying up to 1 million different locations in human genome at the same time - in one experiment. Microarrays are widely used in studying mRNA (transcriptome), genomic DNA (genome), protein-DNA interactions (interactome) and for many other large scale studies.

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DNA sequencing is an important method for system biology studies. There are different technologies that allow determination of nucleotide sequence in DNA molecules. Traditionally, the sequence of genomes is determined with so called Sanger sequencing technology. In recent years, new 2nd and 3rd generation technologies have appeared that allow much faster and higher throughput.

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Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of data generated from the dynamic system. In this paper we consider the case where the data is not sequenced. The learning algorithm is presented a set of data points from the system's operation but with no temporal ordering. The data are simply drawn as individual disconnected points.

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Structural or qualitative (parameter-free) models relying solely on the often well-known network structure provide an alternative approach still are capable to gain useful insights in the functioning of these networks (Klamt et al.,, 2007, BMC Systems Biology). Structures of networks can be analysed to find out its graph-theoretic tasks as cycle analysis, network diameter, determination of incidence matrix, adjacency matrix, distance matrix and others.

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Biosynthesis is a phenomenon wherein chemical compounds are produced from simpler reagents. Biosynthesis, unlike chemosynthesis, takes place within living organisms and is generally catalyzed by enzymes. The process is a vital part of metabolism.