Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

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.

Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae

“…Additionally, Cbf1 showed significantly decreased transcription in the R238K strain at hyperosmotic stress condition, but significantly increased transcription in the S118L strain at normal condition. Besides the above highly commonly clustered TFs, Rap1, Sok2, Rpn4, Pdr1, Cst6 and Ixr1, which are involved in stress response [ 8 , 63 , 70 , 71 ], were also observed to be clustered in regulating SDEGs of both the stress-tolerant and sensitive strains, although they had no significant transcriptional changes (Fig. 6 b, c).…”

Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae

“…In addition, four other well-known stressrelated TFs including Fhl1, Hsf1, Cin5, and Ume6 were speci cally clustered in SDEGs of the L214V strain (Fig. 6b) [31,69,71].…”

“…Additionally, Cbf1 showed signi cantly decreased transcription in the R238K strain at hyperosmotic stress condition, but signi cantly increased transcription in the S118L strain at normal condition. Besides the above highly commonly clustered TFs, Rap1, Sok2, Rpn4, Pdr1, Cst6 and Ixr1, which are involved in stress response [8,62,69,70], were also observed to be clustered in regulating SDEGs of both the stress tolerant and sensitive strains, although they had no signi cant transcriptional changes (Fig. 6b, c).…”

Essential O2-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions

“…14 When time shifts among a combination of several TFs were considered, more plausible results can be obtained as demonstrated by us and others. 35 However, we do not have repeated time-series measurements on any of the two transitions and consequently cannot make a reliable conclusion for a single transition. We therefore utilized binding motifs search approaches to examine the promoter area of all the EORGs from two inverted transitions for 39 TFs with experimentallyverified binding motifs (Table S3, ESI †).…”