A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

Chen, Kuang-Chi; Wang, Tse−Yi; Tseng, Huei-Hun; Huang, Chi Ying F.; Kao, Cheng−Yan

doi:10.1093/bioinformatics/bti415

Cited by 146 publications

(123 citation statements)

References 17 publications

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“…While construction of gene regulatory networks using dynamic model has been successfully shown to characterize the biological system from a system point of view [3,16,17,31,32,34], to the best of our knowledge, no research has used dynamic model to infer a signaling regulatory pathway. Thus, we focused on validating the protein interaction subnetwork of the integrated cellular network.…”

Section: Resultsmentioning

confidence: 99%

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%

Integrated cellular network of transcription regulations and protein-protein interactions

Wang

Chen

2010

BMC Syst Biol

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“…In [7] stochastic differential equations have been used to fit time-courses of protein concentration levels in the yeast cell-cycle network.…”

Section: Introductionmentioning

confidence: 99%

Superstability of the yeast cell-cycle dynamics: Ensuring causality in the presence of biochemical stochasticity

Braunewell

Bornholdt

2007

Journal of Theoretical Biology

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Gene regulatory dynamics is governed by molecular processes and therefore exhibits an inherent stochasticity. However, for the survival of an organism it is a strict necessity that this intrinsic noise does not prevent robust functioning of the system. It is still an open question how dynamical stability is achieved in biological systems despite the omnipresent fluctuations. In this paper we investigate the cell-cycle of the budding yeast Saccharomyces cerevisiae as an example of a wellstudied organism.We study a genetic network model of eleven genes that coordinate the cell-cycle dynamics using a modeling framework which generalizes the concept of discrete threshold dynamics. By allowing for fluctuations in the transcription/translation times, we introduce noise in the model, accounting for the effects of biochemical stochasticity. We study the dynamical attractor of the cell cycle and find a remarkable robustness against fluctuations of this kind. We identify mechanisms that ensure reliability in spite of fluctuations: 'Catcher' states and persistence of activity levels contribute significantly to the stability of the yeast cell cycle despite the inherent stochasticity.

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“…Many approaches have been applied to identify functional modules based on DNA microarray, such as Boolean network methods, differential equationbased network methods [2], Bayesian network method, clustering methods [3], and co-expression network methods [4,5]. The co-expression network technique is widely adopted because it can manage the nature of microarray datasets: typical noise and high dimension.…”

Section: Introductionmentioning

confidence: 99%

Identifying Functional Modules of Diffuse Large B-cell Lymphoma Gene Co-expression networks by Hierarchical Clustering Method Based on Random Matrix Theory

Chen¹,

Zhang²,

Zhong³

2011

Nano BioMed ENG

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In this work, we propose an original method, the Random matrix theory (RMT)-based hierarchical clustering method, to identify functional gene networks of diffuse large B-cell Lymphoma (DLBCL) gene co-expression networks. Comparing topological approach, the RMT-based hierarchical clustering method is effective in representing not only the strong correlations between genes inside the modules (the modularity and independency of networks), but also the weak correlations between different modules (the hierarchy of networks). We show that missing expression values among microarray dataset should not be neglected, and different imputation methods result in different performances. We suggest LLS to estimate missing values for better performance in accuracy and stability. Based on the RMT, the random noises are separated from DLBCL gene expression data. We use normalized root mean squared error (NRMSE) ratio method to identify a transition zone of NNSDs, and for DLBCL networks it is [0.71, 0.84]. Keywords:

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A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae

Abstract: http://www.csie.ntu.edu.tw/~b89x035/yeast/

Cited by 146 publications

References 17 publications

Integrated cellular network of transcription regulations and protein-protein interactions

Integrated cellular network of transcription regulations and protein-protein interactions

Superstability of the yeast cell-cycle dynamics: Ensuring causality in the presence of biochemical stochasticity

Identifying Functional Modules of Diffuse Large B-cell Lymphoma Gene Co-expression networks by Hierarchical Clustering Method Based on Random Matrix Theory

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