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Linking the signaling cascades and dynamic regulatory networks controlling stress responses.

ABSTRACT: Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. Detailed case studies of stress responses in budding yeast demonstrate the predictive power of our method. Our method correctly identifies the core signaling proteins and transcription factors of the response programs. It further predicts the involvement of additional transcription factors and other proteins not previously implicated in the response pathways. We experimentally verify several of these predictions for the osmotic stress response network. Our approach requires little condition-specific data: only a partial set of upstream initiators and time-series gene expression data, which are readily available for many conditions and species. Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species.

SUBMITTER: Gitter A

PROVIDER: S-EPMC3561877 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Linking the signaling cascades and dynamic regulatory networks controlling stress responses.

Gitter Anthony A Carmi Miri M Barkai Naama N Bar-Joseph Ziv Z

Genome research 20121011 2

Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and ...[more]

PMID: 23064748

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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.

Project description:Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for "fast" reactions and incorporates "slow" reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporated kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here.

Dataset Information

Linking the signaling cascades and dynamic regulatory networks controlling stress responses.

Publications

Linking the signaling cascades and dynamic regulatory networks controlling stress responses.

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