Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Kim, Shinuk; Kim, Junil; Cho, Kwang-Hyun

doi:10.1016/j.compbiolchem.2007.03.013

Cited by 52 publications

(37 citation statements)

References 13 publications

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“…The model GRN modeling is considered as a non-linear identification problem with the presence of numerous interacting genes in the network (Cantone et al 2009;Kim et al 2007). A promising non-linear model, the S-system model (Savageau 1976) is capable of capturing the dynamics of various complex regulations.…”

Section: Stochastic Modeling Of Gene Regulatory Networkmentioning

confidence: 99%

Stochastic S-system modeling of gene regulatory network

2015

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Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.

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Section: Stochastic Modeling Of Gene Regulatory Networkmentioning

confidence: 99%

Stochastic S-system modeling of gene regulatory network

2015

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“…18,19 Reverse-engineering algorithms based on ordinary di®erential equations yield directed graphs from time-series expression pro¯les. 20,21 1.3.2. Graphical models A graphical model describes the relationships among control and controlled genes as directed edges between nodes that represent the genes.…”

Section: Ordinary Di®erential Equationsmentioning

confidence: 99%

Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

Takenaka

Seno

Matsuda

2015

J. Bioinform. Comput. Biol.

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Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary di®erential equations or graphical models based on time-course gene expression pro¯les. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The e®ectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the¯rst corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression pro¯le, it can potentially infer hidden GRNs from time-course experiments.

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“…The longitudinal expression data usually contained few subjects sampled at multiple times, or more subjects sampled at only two times. As a result, the best solution of gene network inference was to assume stationarity in the expression data [12][13][14]; i.e., the interaction patterns among genes do not vary over time, although genes in reality interact dynamically. When the microarray technologies nowadays become cheaper and more popular, more studies are able to collect expression data of more subjects at multiple times.…”

Section: Introductionmentioning

confidence: 99%

“…The former describes the molecular kinetic reactions taking place in a cell, and the latter quantizes expression levels as binary variables that are linked to each other through logical relationships. The framework of dynamic Bayesian networks (DBNs) has merits over these approaches thanks to its capability of handling noisy expression measurements, modeling expression levels by continuous variables, and making prediction based on the inferred networks [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Inferring Nonstationary Gene Networks from Longitudinal Gene Expression Microarrays

Chang

Ramoni

2011

J Sign Process Syst

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Inferring gene networks from longitudinal gene expression microarrays is a crucial step towards the study of gene regulatory mechanisms. A decade ago, expensive microarray technology restricted the number of samples undergoing gene expression profiling in single studies, leading the inference algorithms that assume stationary gene networks to the best solution. Thanks to decreasing cost of modern microarray technologies, more gene expression profiles can be assessed in single studies. With more samples available, we can relax the stationarity assumption and develop a method to infer dynamic gene networks, which can reflect more realistic biology where genes adaptively orchestrate each other. This paper applied the framework of dynamic Bayesian networks to infer adaptive gene interactions by identifying individual transition networks between pairs of consecutive times. Due to high computational burden of inferring the interconnection patterns among all genes over time, we designed a parallelizable inference algorithm to make feasible the task. We validated our approach by two clinical studies: yellow fever vaccination and mechanical periodontal therapy. The inferred dynamic networks achieved more than 90% predictive accuracy, a significant improvement when compared to stationary models (p<0.05). The adaptive models can help explain the induction of innate immunology in greater details after yellow fever vaccination and interpret the anti-inflammatory effect of mechanical periodontal therapy.

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Inferring gene regulatory networks from temporal expression profiles under time-delay and noise

Cited by 52 publications

References 13 publications

Stochastic S-system modeling of gene regulatory network

Stochastic S-system modeling of gene regulatory network

Detecting shifts in gene regulatory networks during time-course experiments at single-time-point temporal resolution

Inferring Nonstationary Gene Networks from Longitudinal Gene Expression Microarrays

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