Khanin, Raya and Higham, Desmond J. (2009) Mathematical and computational modelling of posttranscriptional gene relation by microRNA. In: MicroRNA Profiling in Cancer:. World Scientific, pp. 197216. 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 posttranscriptionally 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 misexpression 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 microRNAmRNA binding forming the basis for the posttranscriptional 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 systemslevel 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 
Depositing user:  Pure Administrator 
Date Deposited:  22 Jun 2011 14:19 
Last modified:  12 Dec 2015 02:28 
Related URLs:  
URI:  http://strathprints.strath.ac.uk/id/eprint/31774 
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