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The core regulation module of stress-responsive regulatory networks in yeast.

ABSTRACT: How does a cell respond to numerous external stresses with a limited number of internal molecular components? It has been observed that there are some common responses of yeast to various stresses, but most observations were based on gene-expression profiles and only some part of the common responses were intensively investigated. So far there has been no system-level analysis to identify commonly responsive or regulated genes against various stresses. In this study, we identified a core regulation module (CRM), a commonly involved regulation structure in the regulatory networks of yeast, which cells reuse in response to an array of environmental stresses. We found that regulators in the CRM constitute a hierarchical backbone of the yeast regulatory network and that the CRM is evolutionarily well conserved, stable against genetic variations and crucial for cell growth. All these findings were consistently held up to considerable noise levels that we introduced to address experimental noise and the resulting false positives of regulatory interactions. We conclude that the CRM of yeast might be an evolutionarily conserved information processing unit that endows a cell with enhanced robustness and efficiency in dealing with numerous environmental stresses with a limited number of internal elements.

SUBMITTER: Kim D

PROVIDER: S-EPMC3467048 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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The core regulation module of stress-responsive regulatory networks in yeast.

Kim Dongsan D Kim Man-Sun MS Cho Kwang-Hyun KH

Nucleic acids research 20120710 18

How does a cell respond to numerous external stresses with a limited number of internal molecular components? It has been observed that there are some common responses of yeast to various stresses, but most observations were based on gene-expression profiles and only some part of the common responses were intensively investigated. So far there has been no system-level analysis to identify commonly responsive or regulated genes against various stresses. In this study, we identified a core regulat ...[more]

PMID: 22784859

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Project description:Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their regulators can successfully be modeled using ANNs with a similar configuration.

Dataset Information

The core regulation module of stress-responsive regulatory networks in yeast.

Publications

The core regulation module of stress-responsive regulatory networks in yeast.

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