Dynamical modeling of microRNA action on the protein translation process

TL;DR

This paper demonstrates that analyzing dynamical data, rather than steady state data, can help distinguish the mechanisms of microRNA regulation in protein translation, revealing that microRNA effects are observable only when they influence the system's dominant processes.

Contribution

The study introduces a dynamical analysis approach to differentiate microRNA mechanisms in protein translation, extending previous steady state models and proposing the concept of the dominant system.

Findings

Dynamical data can discriminate microRNA action mechanisms.

MicroRNA effects are observable when affecting the dominant system.

The dominant system varies under different experimental conditions.

Abstract

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 most dominant: moreover, many experimental reports deliver controversal messages on what is the concrete mechanism actually observed in the experiment. Parker and Nissan (Parker and Nissan, RNA, 2008) demonstrated that it is 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. In contrary, we show that dynamical data allow to discriminate some of the mechanisms of microRNA action. We demonstrate this using the same models as in (Parker and Nissan, RNA, 2008) for the sake of comparison but the methods developed (asymptotology of biochemical networks) can be used for other models. As one of the results of our analysis, 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.