Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm

Kirimasthong, Khwunta; Manorat, Aompilai; Chaijaruwanich, Jeerayut; Prasitwattanaseree, Sukon; Thammarongtham, Chinae

doi:10.1007/978-3-540-73871-8_26

Cited by 3 publications

(1 citation statement)

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“…Constructing qualitative regulatory networks from microarray data has been well studied, and a number of effective approaches have been developed. These approaches exploit statistical correlation [11,23], differential equation [32], Boolean modeling [6,19] and (dynamic) Bayesian network [13,17,35,41] to infer regulatory interactions between regulators and target genes. Werhli et al [39] carried out a comparative evaluation on these methods based on an experimentally supported cellular signaling network in human immune system cells, showing that these methods obtained approximately equal accuracy in learning regulatory networks without any prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Quantifying transcriptional regulatory networks by integrating sequence features and microarray data

Liu

2009

Bioprocess Biosyst Eng

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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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Section: Introductionmentioning

confidence: 99%

Quantifying transcriptional regulatory networks by integrating sequence features and microarray data

Liu

2009

Bioprocess Biosyst Eng

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Thermodynamics-based models of transcriptional regulation with gene sequence

Wang

Shen

2015

Bioprocess Biosyst Eng

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Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

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A quantitative framework of transcriptional dynamics by integrating multiple sources of knowledge

Wang

2011

2011 IEEE International Conference on Systems Biology (ISB)

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Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm

Cited by 3 publications

References 5 publications

Quantifying transcriptional regulatory networks by integrating sequence features and microarray data

Quantifying transcriptional regulatory networks by integrating sequence features and microarray data

Thermodynamics-based models of transcriptional regulation with gene sequence

A quantitative framework of transcriptional dynamics by integrating multiple sources of knowledge

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