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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 small network of Transcr.Degr. type repressing mRNA Oct4.All expression profiles are over days 0–6. Top profiles: miRNA miR-186 for WT and GCNF-KO data. Middle profiles: transcription factors of Oct4 for WT and GCNF-KO data. Blue solid line = protein Oct4. Green dash line = protein Nanog. Red dotted-solid line = protein GCNF. Bottom profiles: mRNA Oct4 for WT and GCNF-KO data. Blue line = recorded levels. Red dash line = predicted levels. “prediction error” is the model global relative error of prediction; std is the relative standard deviation of recorded levels.
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pone-0023263-g004: Example of small network of Transcr.Degr. type repressing mRNA Oct4.All expression profiles are over days 0–6. Top profiles: miRNA miR-186 for WT and GCNF-KO data. Middle profiles: transcription factors of Oct4 for WT and GCNF-KO data. Blue solid line = protein Oct4. Green dash line = protein Nanog. Red dotted-solid line = protein GCNF. Bottom profiles: mRNA Oct4 for WT and GCNF-KO data. Blue line = recorded levels. Red dash line = predicted levels. “prediction error” is the model global relative error of prediction; std is the relative standard deviation of recorded levels.

Mentions: Figure 4 displays expression profiles corresponding to one of these 5 validated Transcr.Degr. model, with upstream transcription repressors mmu-miR-186, GCNF, Oct4, Nanog, and with transcription activators (protein Oct4, protein Nanog).


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 small network of Transcr.Degr. type repressing mRNA Oct4.All expression profiles are over days 0–6. Top profiles: miRNA miR-186 for WT and GCNF-KO data. Middle profiles: transcription factors of Oct4 for WT and GCNF-KO data. Blue solid line = protein Oct4. Green dash line = protein Nanog. Red dotted-solid line = protein GCNF. Bottom profiles: mRNA Oct4 for WT and GCNF-KO data. Blue line = recorded levels. Red dash line = predicted levels. “prediction error” is the model global relative error of prediction; std is the relative standard deviation of recorded levels.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3198445&req=5

pone-0023263-g004: Example of small network of Transcr.Degr. type repressing mRNA Oct4.All expression profiles are over days 0–6. Top profiles: miRNA miR-186 for WT and GCNF-KO data. Middle profiles: transcription factors of Oct4 for WT and GCNF-KO data. Blue solid line = protein Oct4. Green dash line = protein Nanog. Red dotted-solid line = protein GCNF. Bottom profiles: mRNA Oct4 for WT and GCNF-KO data. Blue line = recorded levels. Red dash line = predicted levels. “prediction error” is the model global relative error of prediction; std is the relative standard deviation of recorded levels.
Mentions: Figure 4 displays expression profiles corresponding to one of these 5 validated Transcr.Degr. model, with upstream transcription repressors mmu-miR-186, GCNF, Oct4, Nanog, and with transcription activators (protein Oct4, protein Nanog).

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