Eirini Tsirvouli

This study investigates synergistic effects of kinase inhibitor combinations by analyzing phosphoproteomics and transcriptomics data to identify molecular mechanisms and pathway interactions.

TabularQual provides a spreadsheet-based format for annotating logical models, with tools to convert between spreadsheets and SBML-qual, enhancing model reuse and interoperability.

TRAFIKK predicts and explains anticancer drug synergies by simulating drug effects in cell-specific models, achieving high recall and revealing both conserved and context-dependent mechanisms.

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

BooLEVARD quantifies paths leading to node activation or repression in Boolean models, enhancing signal strength analysis in signaling networks.

CollecTRI-derived signed regulons, spanning 1,183 transcription factors (TFs) and 45,856 TF-gene links, outperform DoRothEA, ChEA3, RegNetwork and Pathway Commons at identifying perturbed TFs in KnockTF data.

The paper develops a dynamic Boolean model to simulate psoriasis development and identifies potential drug targets by predicting interventions that mimic current treatments.

The paper uses gene expression data and logical models to simulate patient-specific responses to psoriatic stimuli and identify treatment strategies.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper models cytokine and eicosanoid signaling in psoriatic keratinocytes to identify potential therapeutic targets and mechanisms of disease.