Connecting quantitative regulatory-network models to the genome

Pan, Yue; Durfee, Tim; Bockhorst, Joseph; Craven, Mark

doi:10.1093/bioinformatics/btm228

Cited by 13 publications

(9 citation statements)

References 30 publications

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“…Gene regulation involves a large number of biochemical events. Although kinetic models can be developed for gene regulation [12] , [27] , [28] , they involve many kinetic parameters that are difficult to be estimated from gene expression data with small number of samples. An alternative model of gene expression is a state-space model.…”

Section: Resultsmentioning

confidence: 99%

Differential Dynamic Properties of Scleroderma Fibroblasts in Response to Perturbation of Environmental Stimuli

Xiong

Arnett²,

Xiong³

et al. 2008

PLoS ONE

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Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-β pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-β pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

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Section: Resultsmentioning

confidence: 99%

Differential Dynamic Properties of Scleroderma Fibroblasts in Response to Perturbation of Environmental Stimuli

Xiong

Arnett²,

Xiong³

et al. 2008

PLoS ONE

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“…By integrating promoter sequences explicitly into the regulation model [ 19 ] (rather than in post hoc validation) we can overcome some of the limitations of the current approach. Such an integration can incorporate promoter composition as hard or soft constraints to the regulatory network structure, or it can iterate between learning of these two phases [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

BRNI: Modular analysis of transcriptional regulatory programs

Nachman

Regev

2009

BMC Bioinformatics

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Background: Transcriptional responses often consist of regulatory modules -sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.

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“…More general forms are given in Nachman et al 69 Consider the scenario that transcription can only start when a set of R regulators are all present in the promoter region of a gene,

s_{i} = α \frac{\prod_{j = 1}^{R} {false(\frac{r_{j}}{c_{i j}} false)}^{β_{i j}}}{1 + \prod_{j = 1}^{R} {false(\frac{r_{j}}{c_{i j}} false)}^{β_{i j}}},

where c ij is an association constant for binding between gene i and TF j and β ij measures the cooperativity in binding. Pan et al 70 modelled binding as a function of sequences.…”

Section: Methods Based On Combined Data Analysismentioning

confidence: 99%

Reconstructing transcriptional regulatory networks through genomics data

Sun

Zhao

2009

Stat Methods Med Res

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One central problem in biology is to understand how gene expression is regulated under different conditions. Microarray gene expression data and other high throughput data have made it possible to dissect transcriptional regulatory networks at the genomics level. Owing to the very large number of genes that need to be studied, the relatively small number of data sets available, the noise in the data and the different natures of the distinct data types, network inference presents great challenges. In this article, we review statistical and computational methods that have been developed in the last decade in response to genomics data for inferring transcriptional regulatory networks.

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Connecting quantitative regulatory-network models to the genome

Cited by 13 publications

References 30 publications

Differential Dynamic Properties of Scleroderma Fibroblasts in Response to Perturbation of Environmental Stimuli

Differential Dynamic Properties of Scleroderma Fibroblasts in Response to Perturbation of Environmental Stimuli

BRNI: Modular analysis of transcriptional regulatory programs

Reconstructing transcriptional regulatory networks through genomics data

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