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

Raya Khanin, Desmond J. Higham

Research output: Chapter in Book/Report/Conference proceedingChapter

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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.
Original languageEnglish
Title of host publicationMicroRNA Profiling in Cancer:
Subtitle of host publicationA bioinformatics by perspective
EditorsYuriy Gusev
Pages197-216
Number of pages20
EditionChapter 10
ISBN (Electronic)9789814267540
Publication statusPublished - Oct 2009

Fingerprint

RNA
Genes
Gene expression
Kinetics
Cytology
Microarrays
Ordinary differential equations
Mass spectrometry
Mathematical models
Experiments

Keywords

  • cancer
  • computational simulations
  • microRNAs
  • mathematical models
  • gene regulation

Cite this

Khanin, R., & Higham, D. J. (2009). Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. In Y. Gusev (Ed.), MicroRNA Profiling in Cancer: : A bioinformatics by perspective (Chapter 10 ed., pp. 197-216)
Khanin, Raya ; Higham, Desmond J. / Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. MicroRNA Profiling in Cancer: : A bioinformatics by perspective. editor / Yuriy Gusev. Chapter 10. ed. 2009. pp. 197-216
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Khanin, R & Higham, DJ 2009, Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. in Y Gusev (ed.), MicroRNA Profiling in Cancer: : A bioinformatics by perspective. Chapter 10 edn, pp. 197-216.

Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. / Khanin, Raya; Higham, Desmond J.

MicroRNA Profiling in Cancer: : A bioinformatics by perspective. ed. / Yuriy Gusev. Chapter 10. ed. 2009. p. 197-216.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Khanin R, Higham DJ. Mathematical and computational modelling of post-transcriptional gene relation by micro-RNA. In Gusev Y, editor, MicroRNA Profiling in Cancer: : A bioinformatics by perspective. Chapter 10 ed. 2009. p. 197-216