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• W2000262953 abstract "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." @default.
• W2000262953 created "2016-06-24" @default.
• W2000262953 creator A5076745249 @default.
• W2000262953 creator A5088598187 @default.
• W2000262953 date "2013-01-01" @default.
• W2000262953 modified "2023-10-15" @default.
• W2000262953 title "Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response" @default.
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• W2000262953 doi "https://doi.org/10.3389/fgene.2013.00110" @default.
• W2000262953 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3687159" @default.
• W2000262953 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23802010" @default.
• W2000262953 hasPublicationYear "2013" @default.
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