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Quantifying transcriptional regulatory networks by integrating sequence features and microarray data

Abstract

Genome-wide transcriptional regulatory networks (TRNs) specify the interactions between transcription factors (TFs) and their target genes. Many methods have been proposed to reconstruct regulatory networks from gene expression datasets and/or genome sequences, but most of them can only infer qualitative regulation relationships. Thus, developing a quantitative model that can estimate the kinetic parameters of transcriptional regulatory functions is an urgent and important task. In this paper I propose REMBE, a regulatory model based on binding energy, to quantify transcriptional regulatory networks. My model combines multiple kinetic quantities, including binding strength, TF-DNA’s binding energy, transcription productivity with respect to each binding state, and hidden TFs' concentration, into a general learning model. Experimental results show that my model can effectively learn these kinetic parameters and TFs’ concentration from genome sequences and gene expression data. Moreover, these learned parameters and TFs' concentration provide more informative biological senses than merely qualitative regulatory relationships can do.

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Notes

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    http://www.geneontology.org

Abbreviations

H:

TF concentration

γ :

Binding strength

α :

Transcriptional productivity

λ :

Indicator of interaction among TFs

s :

Binding state

β :

Maximal transcription rate

t :

Time point

j :

Gene

i :

Transcription factor

B ij :

Binding site of TF i on the promoter of gene j

E ij :

Average binding energy between TF i and gene j

b j :

Background expression level of gene j

c ij :

Indicator of regulation type

μ :

Regulation function

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Author information

Correspondence to Hui Liu.

Additional information

The source codes and supplementary materials are available at http://admis.fudan.edu.cn/quantifyTRN/.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 513 kb)

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Liu, H. Quantifying transcriptional regulatory networks by integrating sequence features and microarray data. Bioprocess Biosyst Eng 33, 495–505 (2010). https://doi.org/10.1007/s00449-009-0358-1

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Keywords

  • Transcription regulation
  • Quantitative model
  • Transcription rate