Modelling transcriptional regulation using Gaussian Processes

Neil D. LawrenceGuido SanguinettiMagnus Rattray
,  19:785-792, 2007.

Abstract

Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian Process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-transcriptionalgp06, title = {Modelling transcriptional regulation using Gaussian Processes}, author = {Neil D. Lawrence and Guido Sanguinetti and Magnus Rattray}, pages = {785--792}, year = {}, editor = {}, volume = {19}, address = {Cambridge, MA}, url = {http://inverseprobability.com/publications/lawrence-transcriptionalgp06.html}, abstract = {Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian Process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.} }
Endnote
%0 Conference Paper %T Modelling transcriptional regulation using Gaussian Processes %A Neil D. Lawrence %A Guido Sanguinetti %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-transcriptionalgp06 %I PMLR %J Proceedings of Machine Learning Research %P 785--792 %U http://inverseprobability.com %V %W PMLR %X Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian Process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.
RIS
TY - CPAPER TI - Modelling transcriptional regulation using Gaussian Processes AU - Neil D. Lawrence AU - Guido Sanguinetti AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-lawrence-transcriptionalgp06 PB - PMLR SP - 785 DP - PMLR EP - 792 L1 - UR - http://inverseprobability.com/publications/lawrence-transcriptionalgp06.html AB - Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian Process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies. ER -
APA
Lawrence, N.D., Sanguinetti, G. & Rattray, M.. (). Modelling transcriptional regulation using Gaussian Processes. , in PMLR :785-792

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