Download e-book for kindle: Frontiers in Computational and Systems Biology by Chieh-Chun Chen, Sheng Zhong (auth.), Jianfeng Feng,

By Chieh-Chun Chen, Sheng Zhong (auth.), Jianfeng Feng, Wenjiang Fu, Fengzhu Sun (eds.)

ISBN-10: 1849961956

ISBN-13: 9781849961950

ISBN-10: 1849961964

ISBN-13: 9781849961967

Biological and biomedical reviews have entered a brand new period as a result of common use of mathematical types and computational methods. What used to be not more than a theoretician's myth twenty years in the past has develop into the blooming box of computational biology. in addition, this strong and stimulating learn paradigm in organic reviews has in flip ended in new advancements in arithmetic, physics and computing device science.

This particular quantity surveys state of the art examine on statistical equipment in molecular and platforms biology, with contributions from top specialists within the box. every one bankruptcy discusses theoretical points, functions to organic difficulties, and attainable destiny advancements. figuring out the biology at a molecular and approach point stands one of the most enjoyable demanding situations confronted via smooth technology. this article truly demonstrates how computational and mathematical methods proceed to handle this challenge.

Topics and features:

  • Presents using thermodynamic versions to investigate gene regulatory mechanisms
  • Reviews significant algorithms for RNA secondary constitution prediction, with a spotlight on ensemble-based ways
  • Discusses how advancements within the region of oligo arrays has resulted in a greater realizing of the array mechanism and enhancements in microarray facts research
  • Examines the appliance of versions of stochastic procedures in nonequilibrium thermodynamics and organic sign transduction
  • Describes phylogenetic footprinting tools for TFBS identity, in line with alignments
  • Introduces penalized regression-based tools for developing genetic interplay or regulatory networks
  • Investigates the explicit function performed by way of irreversible Markov procedures in modeling mobile biochemical platforms
  • Explores the concept that of gene modules in a transcriptional regulatory community

With studies of present sizzling issues in computational biology and platforms biology, provided through a world number of best researchers, this is often a necessary textual content for researchers and graduate scholars in computational biology, biology and mathematics.

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Additional resources for Frontiers in Computational and Systems Biology

Example text

Thus, the essence of the sampling algorithm is stochastic traceback. , U = I exp[−E(I )/RT]. The sampling algorithm generates a sample of secondary structures in proportion to their Boltzmann probabilities, guaranteeing a statistical representation of the Boltzmannweighted ensemble. A statistical sample of the ensemble allows sampling estimates of the probabilities of any structural motifs, from the simplest elements of base pair and unpaired base, to loops of various types, to more complex structures consisting of stems and loops that may be of special interest in a given application.

For a complete suite of RNAi design tools, it is essential to address both the issue of gene silencing potency and the issue of targeting specificity. This is particularly important for largescale loss-of-function screens by using siRNA libraries [32] or shRNA libraries [10, 87, 96]. 4 Posttranscriptional Gene Regulation by MicroRNAs MicroRNAs are endogenous noncoding RNAs (ncRNAs) of ∼22 nt and are among the most abundant regulatory molecules in multicellular organisms. microRNAs typically negatively regulate specific mRNA targets through essentially two mechanisms: (1) when a microRNA is perfectly or nearly perfectly complementary to mRNA target sites, as is the case for most plant microRNAs, it causes mRNA target cleavage [85]; and (2) a microRNA with incomplete complementarity to sequences in the 3 untranslated region (3 UTR) of its target (as is the case for most animal microRNAs) can cause translational repression or mRNA destabilization [34].

M. Steyaert. Energy landscape of k-point mutants of an RNA molecule. Bioinformatics, 21(22):4140–4147, 2005. 18. R. Cullen. Viruses and microRNAs. Nat Genet, 38:S25–30, 2006. 19. J. Cupal, C. Flamm, A. F. Stadler. Density of states, metastable states, and saddle points exploring the energy landscape of an RNA molecule. Proc Int Conf Intell Syst Mol Biol, 5:88–91, 1997. 20. D. Didiano and O. Hobert. Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat Struct Mol Biol, 13(9):849–851, 2006.

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Frontiers in Computational and Systems Biology by Chieh-Chun Chen, Sheng Zhong (auth.), Jianfeng Feng, Wenjiang Fu, Fengzhu Sun (eds.)


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