Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle

Chen, Hong-Chu; Lee, Hsiao-Ching; Lin, Tsai-Yun; Li, Wen-Hsiung; Chen, Bor‐Sen

doi:10.1093/bioinformatics/bth178

Cited by 83 publications

(94 citation statements)

References 48 publications

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“…It has been shown that TF binding usually affects gene expression in a nonlinear fashion: below some level it has no effect, while above a certain level the effect may become saturated [29,30]. Thus, the regulation function z j [ t ] was modeled as the sigmoid function, which is one kind of Hill function, of y j [ t ] (the protein activity profiles of transcription factor j ) [3,16,30-33]…”

Section: Resultsmentioning

confidence: 99%

“…Though many studies have focused on gene regulatory networks [3-6] and on signaling transduction pathways [7-9], few works have combined these two kinds of networks. Yeger-Lotem and Margalit [10] used classical graph algorithms to integrate the cellular networks of protein-protein and protein-DNA interactions.…”

Section: Introductionmentioning

confidence: 99%

“…A dynamic model, which is typically represented by difference equations or differential equations, is often used in many fields to describe a complex and kinetic system. Recently, systems biology and computational biology methods have widely employed the dynamic model to describe the biological functions from the dynamic system point of view [3,16,17]. The advantage of using dynamic models to construct an integrated cellular network is not only that it can provide quantitative descriptions of the network, but it also can predict the behavior of the network under different conditions, i.e., gene knockout, treatment with an external agent, etc [18].…”

Section: Introductionmentioning

confidence: 99%

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Integrated cellular network of transcription regulations and protein-protein interactions

Wang

Chen

2010

BMC Syst Biol

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BackgroundWith the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.ResultsIn this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.ConclusionsWe successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated cellular network of transcription regulations and protein-protein interactions

Wang

Chen

2010

BMC Syst Biol

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“…Chen et al (2004) used the nonlinear additive model (2) to model the transcription rate of a target gene as a combination of a set of regulatory functions from relevant regulators. Here the response Y is the transcription rate of the target gene; the covariate X j is the gene expression of the j -th regulator; the intercept β 0 is the basal expression of the target gene; and the coefficient β j is the regulatory capability from the j -th regulator.…”

Section: Application To Identify Gene Regulationsmentioning

confidence: 99%

“…The usage of the sigmoid function model to describe a GRN has been discussed in literature, for example, Mestl et al (1995) and Chen et al (2004) among others. An example of the simple sigmoid function is…”

Section: Introductionmentioning

confidence: 99%

Variable selection for sparse high-dimensional nonlinear regression models by combining nonnegative garrote and sure independence screening

Han

et al. 2014

STAT SINICA

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In many regression problems, the relations between the covariates and the response may be nonlinear. Motivated by the application of reconstructing a gene regulatory network, we consider a sparse high-dimensional additive model with the additive components being some known nonlinear functions with unknown parameters. To identify the subset of important covariates, we propose a new method for simultaneous variable selection and parameter estimation by iteratively combining a large-scale variable screening (the nonlinear independence screening, NLIS) and a moderate-scale model selection (the nonnegative garrote, NNG) for the nonlinear additive regressions. We have shown that the NLIS procedure possesses the sure screening property and it is able to handle problems with non-polynomial dimensionality; and for finite dimension problems, the NNG for the nonlinear additive regressions has selection consistency for the unimportant covariates and also estimation consistency for the parameter estimates of the important covariates. The proposed method is applied to simulated data and a real data example for identifying gene regulations to illustrate its numerical performance.

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References

2009

Biomolecular Networks

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Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle

Abstract: Matlab code and test data are available at http://www.ee.nthu.edu.tw/~bschen/quantitative/regulatory_network.htm

Cited by 83 publications

References 48 publications

Integrated cellular network of transcription regulations and protein-protein interactions

Integrated cellular network of transcription regulations and protein-protein interactions

Variable selection for sparse high-dimensional nonlinear regression models by combining nonnegative garrote and sure independence screening

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