Inferring gene regulatory networks from time series data using the minimum description length principle

Zhao, Wentao; Serpedin, Erchin; Dougherty, Edward R.

doi:10.1093/bioinformatics/btl364

Cited by 134 publications

(141 citation statements)

References 17 publications

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“…The expression levels of 4028 genes of wild-type Drosophila were measured by Arbeitman et al (2002) at 67 time-points throughout their life-cycle. We have here restricted our attention to eleven genes involved in wing muscle development and previously studied by Zhao et al (2006) and Dondelinger et al (2013). The expectation was that our approach would find change-points corresponding to the four different stages of development observed for Drosophila melanogaster.…”

Section: Drosophila Life Cycle Microarray Datamentioning

confidence: 99%

Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

Schwaller

Robin

2016

Stat Comput

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To cite this version:Loic Schwaller, Stephane Robin. Exact Bayesian inference for off-line change-point detection in treestructured graphical models. Statistics and Computing, Springer Verlag (Germany), 2017, 27 (5) Abstract We consider the problem of change-point detection in multivariate time-series. The multivariate distribution of the observations is supposed to follow a graphical model, whose graph and parameters are affected by abrupt changes throughout time. We demonstrate that it is possible to perform exact Bayesian inference whenever one considers a simple class of undirected graphs called spanning trees as possible structures. We are then able to integrate on the graph and segmentation spaces at the same time by combining classical dynamic programming with algebraic results pertaining to spanning trees. In particular, we show that quantities such as posterior distributions for change-points or posterior edge probabilities over time can efficiently be obtained. We illustrate our results on both synthetic and experimental data arising from biology and neuroscience.

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Section: Drosophila Life Cycle Microarray Datamentioning

confidence: 99%

Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

Schwaller

Robin

2016

Stat Comput

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“…The three main morphogenetical transitions occur at time points t = 31 (embryonic to larval), t = 41 (larval to pupal), and t = 59 (pupal to adult) (Arbeitman et al (2002)). Like other researchers (Zhao et al (2006); Robinson and Hartemink (2009)) we focus our analysis on N = 11 genes involved in growth and muscle development: EVE, GFL, TWI, MLC1, SLS, MHC, PRM, ACTN, UP, MYP61F, and MSP300. The data set is available from Robinson and Hartemink (2009), and we standardized the observations of each gene independently with a z-score transformation.…”

Section: Gene Expression Network Data From Morphogenesis Inmentioning

confidence: 99%

A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

Grzegorczyk¹,

Husmeier²

2012

Statistical Applications in Genetics and Molecular Biology

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Grzegorczyk, M., and Husmeier, D. (2012) A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology. Statistical Applications in Genetics and Molecular Biology, 11 (4 AbstractAn important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

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“…Reverse engineering of gene regulatory networks from expression data have been handled by various approaches [29,16,14,34]. Most of them are only static.…”

Section: Introductionmentioning

confidence: 99%

Inference of Delayed Biological Regulatory Networks from Time Series Data

Abdallah

Ribeiro

Magnin

et al. 2016

Computational Methods in Systems Biology

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Abstract. The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. But with the development of highthroughput data, there is a growing need for methods that automatically generate admissible models. Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences amoung genes. In this paper, we propose a new methodology for models expressed through a timed extension of the Automata Networks [22] (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. The originality of our work consists in the integration of quantitative time delays directly in our learning approach. We show the benefits of such automatic approach on dynamical biological models, the DREAM4 datasets, a popular reverse-engineering challenge, in order to discuss the precision and the computational performances of our algorithm.

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Inferring gene regulatory networks from time series data using the minimum description length principle

Abstract: Available from the authors upon request.

Cited by 134 publications

References 17 publications

Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

Exact Bayesian inference for off-line change-point detection in tree-structured graphical models

A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

Inference of Delayed Biological Regulatory Networks from Time Series Data

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