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Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA

Khanin, Raya and Higham, Desmond J. (2009) Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. In: MicroRNA Profiling in Cancer:. World Scientific, pp. 197-216. ISBN 9789814267540

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    Abstract

    Mathematical models and computational simulations have proved valuable in many areas of cell biology, including gene regulatory networks. When properly calibrated against experimental data, kinetic models can be used to describe how the concentrations of key species evolve over time. A reliable model allows ‘what if’ scenarios to be investigated quantitatively in silico, and also provides a means to compare competing hypotheses about the underlying biological mechanisms at work. Moreover, models at different scales of resolution can be merged into a bigger picture ‘systems’ level description. In the case where gene regulation is post-transcriptionally affected by microRNAs, biological understanding and experimental techniques have only recently matured to the extent that we can postulate and test kinetic models. In this chapter, we summarize some recent work that takes the first steps towards realistic modelling, focusing on the contributions of the authors. Using a deterministic ordinary differential equation framework, we derive models from first principles and test them for consistency with recent experimental data, including microarray and mass spectrometry measurements. We first consider typical mis-expression experiments, where the microRNA level is instantaneously boosted or depleted and thereafter remains at a fixed level. We then move on to a more general setting where the microRNA is simply treated as another species in the reaction network, with microRNA-mRNA binding forming the basis for the post-transcriptional repression. We include some speculative comments about the potential for kinetic modelling to contribute to the more widespread sequence and network based approaches in the qualitative investigation of microRNA based gene regulation. We also consider what new combinations of experimental data will be needed in order to make sense of the increased systems-level complexity introduced by microRNAs.

    Item type: Book Section
    ID code: 31774
    Keywords: cancer, computational simulations, microRNAs, mathematical models , gene regulation, Probabilities. Mathematical statistics
    Subjects: Science > Mathematics > Probabilities. Mathematical statistics
    Department: Faculty of Science > Mathematics and Statistics
    Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 22 Jun 2011 15:19
    Last modified: 09 Dec 2013 12:36
    URI: http://strathprints.strath.ac.uk/id/eprint/31774

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