PHENSIM

PHENSIM predicts changes in cellular phenotypes by simulating perturbations of signaling pathways to assess effects of drug administration, gene knockdowns, gene transductions, and exposure to exosomal cargo.

Key Features:

• Systems Biology Approach: Integrates complex biological data to simulate and predict cellular phenotype changes based on pathway-level interactions.
• Perturbation Types Supported: Models effects of pharmacological interventions, gene knockdowns, gene transductions, and exosomal cargo on cellular states.
• Signaling Pathway Integration: Uses signaling pathway information to infer cellular responses to specified perturbations.
• Model Organism Pathway Maps: Includes curated pathway maps from three different model organisms for comparative analyses.
• Benchmarking and Validation: Validated against transcriptomics datasets from NCBI GEO with four case studies demonstrating high prediction accuracy.

Scientific Applications:

• Predicting pharmacological outcomes: Forecasts cellular responses to drug administration for hypothesis generation in drug studies.
• Simulating genetic modifications: Evaluates phenotypic consequences of gene knockdown and gene transduction experiments.
• Assessing exosomal effects: Predicts cellular responses to exposure to exosomal cargo.
• Experimental design and hypothesis testing: Supports design and prioritization of cell biology experiments by providing in silico phenotype predictions.
• Drug discovery and disease research: Aids investigation of disease mechanisms and candidate interventions at the pathway and cellular-phenotype levels.

Methodology:

Simulations integrate signaling pathway information and curated pathway maps from three model organisms and are benchmarked against transcriptomics datasets from NCBI GEO, with performance evaluated via four case studies.