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miRNA regulatory circuits in ES cells differentiation: a chemical kinetics modeling approach.

Luo Z, Xu X, Gu P, Lonard D, Gunaratne PH, Cooney AJ, Azencott R - PLoS ONE (2011)

Bottom Line: For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data.When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction.This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Houston, Houston, Texas, United States of America. boluomiduo1@gmail.com

ABSTRACT
MicroRNAs (miRNAs) play an important role in gene regulation for Embryonic Stem cells (ES cells), where they either down-regulate target mRNA genes by degradation or repress protein expression of these mRNA genes by inhibiting translation. Well known tables TargetScan and miRanda may predict quite long lists of potential miRNAs inhibitors for each mRNA gene, and one of our goals was to strongly narrow down the list of mRNA targets potentially repressed by a known large list of 400 miRNAs. Our paper focuses on algorithmic analysis of ES cells microarray data to reliably detect repressive interactions between miRNAs and mRNAs. We model, by chemical kinetics equations, the interaction architectures implementing the two basic silencing processes of miRNAs, namely "direct degradation" or "translation inhibition" of targeted mRNAs. For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data. When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction. This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

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Example of Transl.Inhib. architecture repressing Oct4.All expression profiles are over days 0–6. Upper 6 profiles: miRNAs miR-542-3p, miR-464 and miR-138 for WT and GCNF-KO. Bottom 2 profiles: Blue line = recorded level. Red dash line = predicted levels.
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pone-0023263-g005: Example of Transl.Inhib. architecture repressing Oct4.All expression profiles are over days 0–6. Upper 6 profiles: miRNAs miR-542-3p, miR-464 and miR-138 for WT and GCNF-KO. Bottom 2 profiles: Blue line = recorded level. Red dash line = predicted levels.

Mentions: Figure 5 displays the expression profiles for one example of validated network inhibiting the translation of Oct4 through 3 upstream miRNA repressors (mmu-miR-542-3p, mmu-miR-484, mmu-miR-138).


miRNA regulatory circuits in ES cells differentiation: a chemical kinetics modeling approach.

Luo Z, Xu X, Gu P, Lonard D, Gunaratne PH, Cooney AJ, Azencott R - PLoS ONE (2011)

Example of Transl.Inhib. architecture repressing Oct4.All expression profiles are over days 0–6. Upper 6 profiles: miRNAs miR-542-3p, miR-464 and miR-138 for WT and GCNF-KO. Bottom 2 profiles: Blue line = recorded level. Red dash line = predicted levels.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198445&req=5

pone-0023263-g005: Example of Transl.Inhib. architecture repressing Oct4.All expression profiles are over days 0–6. Upper 6 profiles: miRNAs miR-542-3p, miR-464 and miR-138 for WT and GCNF-KO. Bottom 2 profiles: Blue line = recorded level. Red dash line = predicted levels.
Mentions: Figure 5 displays the expression profiles for one example of validated network inhibiting the translation of Oct4 through 3 upstream miRNA repressors (mmu-miR-542-3p, mmu-miR-484, mmu-miR-138).

Bottom Line: For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data.When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction.This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Houston, Houston, Texas, United States of America. boluomiduo1@gmail.com

ABSTRACT
MicroRNAs (miRNAs) play an important role in gene regulation for Embryonic Stem cells (ES cells), where they either down-regulate target mRNA genes by degradation or repress protein expression of these mRNA genes by inhibiting translation. Well known tables TargetScan and miRanda may predict quite long lists of potential miRNAs inhibitors for each mRNA gene, and one of our goals was to strongly narrow down the list of mRNA targets potentially repressed by a known large list of 400 miRNAs. Our paper focuses on algorithmic analysis of ES cells microarray data to reliably detect repressive interactions between miRNAs and mRNAs. We model, by chemical kinetics equations, the interaction architectures implementing the two basic silencing processes of miRNAs, namely "direct degradation" or "translation inhibition" of targeted mRNAs. For each pair (M,G) of potentially interacting miRMA gene M and mRNA gene G, we parameterize our associated kinetic equations by optimizing their fit with microarray data. When this fit is high enough, we validate the pair (M,G) as a highly probable repressive interaction. This approach leads to the computation of a highly selective and drastically reduced list of repressive pairs (M,G) involved in ES cells differentiation.

Show MeSH