Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities

Pei Gao; Antti Honkela; Magnus Rattray; Neil D. Lawrence

doi:10.1093/bioinformatics/btn278

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Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities

Pei Gao, Antti Honkela, Magnus Rattray, Neil D. Lawrence

Bioinformatics, 24:i70-i75, 2008.

Abstract

Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.

Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.

Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from http://inverseprobability.com/gpsim

Contact: Neil Lawrence

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Cite this Paper

BibTeX


@Article{Gao-latent08,
  title = 	 {{G}aussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities},
  author = 	 {Gao, Pei and Honkela, Antti and Rattray, Magnus and Lawrence, Neil D.},
  journal =      {Bioinformatics},
  pages = 	 {i70--i75},
  year = 	 {2008},
  volume = 	 {24},
  doi = 	 {10.1093/bioinformatics/btn278},
  pdf = 	 {http://bioinformatics.oxfordjournals.org/cgi/reprint/24/16/i70.pdf?ijkey=FauSn114lAUC1Ey&keytype=ref},
  url = 	 {/publications/gao-latent08.html},
  abstract = 	 {Motivation: Inference of latent chemical species in
biochemical interaction networks is a key problem in estimation of
the structure and parameters of the genetic, metabolic and protein
interaction networks that underpin all biological processes. We
present a framework for Bayesian marginalisation of these latent
chemical species through Gaussian process priors.



Results: We demonstrate our general approach on three
different biological examples of single input motifs, including both
activation and repression of transcription. We focus in particular
on the problem of inferring transcription factor activity when the
concentration of active protein cannot easily be measured.  We show
how the uncertainty in the inferred transcription factor activity
can be integrated out in order to derive a likelihood function that
can be used for the estimation of regulatory model parameters. An
advantage of our approach is that we avoid the use of a
coarse-grained discretization of continuous-time functions, which
would lead to a large number of additional parameters to be
estimated. We develop efficient exact and approximate inference
schemes, which are much more efficient than competing sampling-based
schemes and therefore provide us with a practical toolkit for
model-based inference.



Availability: The software and data for recreating all the
experiments in this paper is available in MATLAB from




Contact: Neil Lawrence
}
}

Endnote

%0 Journal Article %T Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities %A Pei Gao %A Antti Honkela %A Magnus Rattray %A Neil D. Lawrence %J Bioinformatics %D 2008 %F Gao-latent08 %P i70--i75 %R 10.1093/bioinformatics/btn278 %U /publications/gao-latent08.html %V 24 %X Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors. Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference. Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from Contact: Neil Lawrence

RIS

TY - JOUR TI - Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities AU - Pei Gao AU - Antti Honkela AU - Magnus Rattray AU - Neil D. Lawrence DA - 2008/08/15 ID - Gao-latent08 VL - 24 SP - i70 EP - i75 DO - 10.1093/bioinformatics/btn278 L1 - http://bioinformatics.oxfordjournals.org/cgi/reprint/24/16/i70.pdf?ijkey=FauSn114lAUC1Ey&keytype=ref UR - /publications/gao-latent08.html AB - Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors. Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference. Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from Contact: Neil Lawrence ER -

APA

Gao, P., Honkela, A., Rattray, M. & Lawrence, N.D.. (2008). Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities. Bioinformatics 24:i70-i75 doi:10.1093/bioinformatics/btn278 Available from /publications/gao-latent08.html.