INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION

Le Yu; Steven Watterson; Stephen Marshall; Peter Ghazal

INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION

Le Yu, Steven Watterson, Stephen Marshall, Peter Ghazal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.

Original language	English
Title of host publication	2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009)
Place of Publication	NEW YORK
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	100-103
Number of pages	4
ISBN (Print)	978-1-4244-4531-8
Publication status	Published - 2009
Event	7th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2009) - Minneapolis Duration: 17 May 2009 → 19 May 2009

Conference

Conference	7th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2009)
City	Minneapolis
Period	17/05/09 → 19/05/09

Keywords

GENE REGULATORY NETWORKS
MODEL

Cite this

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title = "INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION",

abstract = "This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.",

keywords = "GENE REGULATORY NETWORKS, MODEL",

author = "Le Yu and Steven Watterson and Stephen Marshall and Peter Ghazal",

year = "2009",

language = "English",

isbn = "978-1-4244-4531-8",

pages = "100--103",

booktitle = "2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "7th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2009) ; Conference date: 17-05-2009 Through 19-05-2009",

}

Yu, L, Watterson, S, Marshall, S & Ghazal, P 2009, INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION. in 2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009). Institute of Electrical and Electronics Engineers (IEEE), NEW YORK, pp. 100-103, 7th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2009), Minneapolis, 17/05/09.

INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION. / Yu, Le; Watterson, Steven; Marshall, Stephen; Ghazal, Peter.

2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009). NEW YORK : Institute of Electrical and Electronics Engineers (IEEE), 2009. p. 100-103.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION

AU - Yu, Le

AU - Watterson, Steven

AU - Marshall, Stephen

AU - Ghazal, Peter

PY - 2009

Y1 - 2009

N2 - This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.

AB - This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states are arrived at by sampling from an underlying sequence of attractors. We apply the resulting algorithm to the interferon regulatory network using gene expression data taken from murine bone-derived macrophage cells infected with cytomegalovirus.

KW - GENE REGULATORY NETWORKS

KW - MODEL

UR - http://www.scopus.com/inward/record.url?scp=70349480930&partnerID=8YFLogxK

M3 - Conference contribution

SN - 978-1-4244-4531-8

SP - 100

EP - 103

BT - 2009 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS 2009)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - NEW YORK

T2 - 7th IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2009)

Y2 - 17 May 2009 through 19 May 2009

ER -

INFERENCE OF TRANSITION PROBABILITIES BETWEEN THE ATTRACTORS IN BOOLEAN NETWORKS WITH PERTURBATION

Abstract

Conference

Keywords

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