Open Access Highly Accessed Research article

Dynamical modeling of microRNA action on the protein translation process

Andrei Zinovyev123*, Nadya Morozova4, Nora Nonne4, Emmanuel Barillot123, Annick Harel-Bellan4 and Alexander N Gorban56

Author Affiliations

1 Institut Curie, Bioinformatics and Computational Systems Biology Of Cancer, Paris, France

2 INSERM, U900, Paris, F-75248 France

3 Mines ParisTech, Centre for Computational Biology, Fontainebleau, F-77300 France

4 CNRS FRE 2944, Institut André Lwoff, Villejuif, France

5 University of Leicester, Center for Mathematical Modeling, Leicester, UK

6 Institute of Computational Modeling SB RAS, Department of nonequilibrium systems, Krasnoyarsk, Russia

For all author emails, please log on.

BMC Systems Biology 2010, 4:13  doi:10.1186/1752-0509-4-13

Published: 24 February 2010

Abstract

Background

Protein translation is a multistep process which can be represented as a cascade of biochemical reactions (initiation, ribosome assembly, elongation, etc.), the rate of which can be regulated by small non-coding microRNAs through multiple mechanisms. It remains unclear what mechanisms of microRNA action are the most dominant: moreover, many experimental reports deliver controversial messages on what is the concrete mechanism actually observed in the experiment. Nissan and Parker have recently demonstrated that it might be impossible to distinguish alternative biological hypotheses using the steady state data on the rate of protein synthesis. For their analysis they used two simple kinetic models of protein translation.

Results

In contrary to the study by Nissan and Parker, we show that dynamical data allow discriminating some of the mechanisms of microRNA action. We demonstrate this using the same models as developed by Nissan and Parker for the sake of comparison but the methods developed (asymptotology of biochemical networks) can be used for other models. We formulate a hypothesis that the effect of microRNA action is measurable and observable only if it affects the dominant system (generalization of the limiting step notion for complex networks) of the protein translation machinery. The dominant system can vary in different experimental conditions that can partially explain the existing controversy of some of the experimental data.

Conclusions

Our analysis of the transient protein translation dynamics shows that it gives enough information to verify or reject a hypothesis about a particular molecular mechanism of microRNA action on protein translation. For multiscale systems only that action of microRNA is distinguishable which affects the parameters of dominant system (critical parameters), or changes the dominant system itself. Dominant systems generalize and further develop the old and very popular idea of limiting step. Algorithms for identifying dominant systems in multiscale kinetic models are straightforward but not trivial and depend only on the ordering of the model parameters but not on their concrete values. Asymptotic approach to kinetic models allows putting in order diverse experimental observations in complex situations when many alternative hypotheses co-exist.