Although electronic computers are the only "computer species" we are accustomed to, the mathematical notion of a programmablecomputer has nothing to do with wires and logic gates. In fact, Alan Turing's ...
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ISBN:
(纸本)9781581138191
Although electronic computers are the only "computer species" we are accustomed to, the mathematical notion of a programmablecomputer has nothing to do with wires and logic gates. In fact, Alan Turing's notional computer, which marked in 1936 the birth of modern computer science and still stands at its heart, has greater similarity to natural biomolecular machines such as the ribosome and polymerases than to electronic computers. Recently, a new "computer species" made of biological molecules has emerged. these simple molecular computers inspired by the Turing machine, of which a trillion can fit into a microliter, do not compete with electronic computers in solving complex computational problems; their potential lies elsewhere. their molecular scale and their ability to interact directly withthe biochemical environment in which they operate suggest that in the future they may be the basis of a new kind of "smart drugs": molecular devices equipped withthe medical knowledge to perform disease diagnosis and therapy inside the living body. they would detect and diagnose molecular disease symptoms and, when necessary, administer the requisite drug molecules to the cell, tissue or organ in which they operate. In the talk we review this new research direction and report on preliminary steps carried out in our lab towards realizing its vision.
Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of ...
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Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of regulatory control. Gene regulation, however, is a process that depends on many different components such as transcription factors (TFs), cis-regulatory motifs and their temporal and spatial coordination. Accordingly, a promising new direction for computational analysis is the incorporation of multiple data types to discover, for instance, cluster membership, the spatial organization of cis-regulatory motifs and TFs that bind to these motifs. Here, we present such a data-driven framework, comprising four stages, to infer gene regulatory networks (GRNs) by modeling: 1. Motif presence in the promoter; 2. Spatial motif arrangement in co-regulated genes; 3. TFs that bind the respective motifs, and: 4. Dynamic properties of the GRN. A novel method is presented in stage 2, where we optimize for the spatial motif properties: orientation, occurrence of multiple motifs, relative distance between two motifs and distance to the transcription start site (TSS). To find optimal distance based properties in efficient time we describe a dynamic programming approach. To combine multiple motif properties that are shared by genes with similar expression profiles a Hill-climber is employed. Subsequently, in stage 3 and 4, we infer GRNs by assigning TFs to the derived motifs and model time-dependent regulatory relationships between them withthe inferelator approach. None of the stages require the user to manually adjust any parameter, and thus derived properties can be analyzed without the bias introduced by parametrization. We applied this approach to S. cerevisiae data and obtained insight into individual and general properties of the spatial assembly of regulatory elements and inferred the corresponding GRN.
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