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      Modelling Competing Endogenous RNA Networks

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      PLoS ONE
      Public Library of Science

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          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.

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          Most cited references27

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Stochasticity in gene expression: from theories to phenotypes.

            Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
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              Roles for microRNAs in conferring robustness to biological processes.

              Biological systems use a variety of mechanisms to maintain their functions in the face of environmental and genetic perturbations. Increasing evidence suggests that, among their roles as posttranscriptional repressors of gene expression, microRNAs (miRNAs) help to confer robustness to biological processes by reinforcing transcriptional programs and attenuating aberrant transcripts, and they may in some network contexts help suppress random fluctuations in transcript copy number. These activities have important consequences for normal development and physiology, disease, and evolution. Here, we will discuss examples and principles of miRNAs that contribute to robustness in animal systems. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                26 June 2013
                : 8
                : 6
                : e66609
                Affiliations
                [1 ]Human Genetics Foundation (HuGeF), Torino, Italy
                [2 ]Physics Department and Center for Computational Sciences, Politecnico Torino, Torino, Italy
                Memorial Sloan Kettering Cancer Center, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CB AP RZ. Performed the experiments: CB AP. Analyzed the data: CB AP. Wrote the paper: CB AP RZ.

                Article
                PONE-D-13-01298
                10.1371/journal.pone.0066609
                3694070
                23840508
                13046a1c-de21-4bfe-b77d-2ae6a8b2daa6
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 January 2013
                : 7 May 2013
                Page count
                Pages: 13
                Funding
                RZ acknowledges support from the European Research Council Grant No. OPTINF 267915. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Nucleic Acids
                RNA
                RNA processing
                Biophysics
                Nucleic Acids
                RNA
                RNA processing
                Computational Biology
                Genetics
                Gene Expression
                RNA processing
                Molecular Cell Biology
                Gene Expression
                RNA processing
                Nucleic Acids
                RNA
                RNA processing
                Mathematics
                Applied Mathematics
                Probability Theory
                Stochastic Processes
                Physics
                Biophysics
                Nucleic Acids
                RNA
                RNA processing

                Uncategorized
                Uncategorized

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