Modelling Competing Endogenous RNA Networks

Abstract

MicroRNAs (miRNAs) are small RNA molecules, about 22 nucleotide long, which post-transcriptionally regulate their target messenger RNAs (mRNAs). They accomplish key roles in gene regulatory networks, ranging from signaling pathways to tissue morphogenesis, and their aberrant behavior is often associated with the development of various diseases. Recently it has been experimentally shown that the way miRNAs interact with their targets can be described in terms of a titration mechanism. From a theoretical point of view titration mechanisms are characterized by threshold effect at near-equimolarity of the different chemical species, hypersensitivity of the system around the threshold, and cross-talk among targets. The latter characteristic has been lately identified as competing endogenous RNA (ceRNA) effect to mark those indirect interactions among targets of a common pool of miRNAs they are in competition for. Here we propose a stochastic model to analyze the equilibrium and out-of-equilibrium properties of a network of miRNAs interacting with mRNA targets. In particular we are able to describe in detail the peculiar equilibrium and non-equilibrium phenomena that the system displays in proximity to the threshold: (i) maximal cross-talk and correlation between targets, (ii) robustness of ceRNA effect with respect to the model's parameters and in particular to the catalyticity of the miRNA-mRNA interaction, and (iii) anomalous response-time to external perturbations.

Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit

“…Compared with the previous model of a single-target system (15, 16), a major difference was the addition of φ ji , which indicated the regulation by ceRNA j upon ceRNA i. The parameter α (0 ≤ α ≤ 1) was introduced to represent the probability that a miRNA is destroyed after degrading its target (miRNA loss rate) (8,9). When α = 1, miRNA is degraded via a stoichiometric interaction with its target RNA; when α = 0, miRNA is fully recycled through a catalytic interaction with its target.…”

“…Although recent in silico studies have predicted that the loss rate can strongly influence ceRNA effect in the steady state (8,9), the loss rate for miRNA regulation is largely unknown. Previous studies have estimated this parameter ranging from a pure stoichiometric to almost a full catalytic mechanism (8,9,22,30,31). In this work, we constructed a variant ceRNA system containing one partially paired (miRNA-type) target and one perfectly paired (RNAi-type) target.…”

“…The strength of ceRNA regulation is largely determined by the relative abundance and binding strength of ceRNAs and miRNAs and whether the miRNA-bound ceRNA decays through a stoichiometric mechanism or a catalytic mechanism (8)(9)(10). The threshold-like behavior of the ceRNA regulation has been experimentally observed by measuring the abundance of two ceRNAs, phosphatase and tensin homolog (PTEN) and vesicle-associated membrane protein (VAMP)-associated protein A (VAPA) across various cell lines (8).…”

Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments

“…We perform the mathematical analysis in simple ceRNETs but our observations are applicable to more complex ceRNETs involving numerous ceRNAs and miRNAs (36). Although our model currently allows for ceRNA identification and qualitative but not quantitative predictions, once the concentrations of all components of a ceRNET are established, as suggested by Ebert and Sharp (37), it may be suitable to predict the overall effect on a ceRNET in response to single-factor perturbations.…”

Analysis of substrate competition in regulatory network motifs: Stimulus–response curves, thresholds and ultrasensitivity

“…The receptorligand binding motif represents the simplest context in which substrate competition can arise. It may be viewed as a particular case of molecular titration (Buchler and Louis, 2008) which has been observed and modeled in numerous regulatory systems including gene regulation by small RNAs (Levine et al, 2007;Mukherji et al, 2011;Loinger et al, 2012;Bosia et al, 2013) and coupling effects in protein degradation due to saturation of the degradation machinery (Cookson et al, 2011). Competition effects may also arise from the binding of substrates or enzymes to scaffold proteins (Witzel et al, 2012).…”