OLYMPUS

OLYMPUS integrates Differential Evolution (DE) with Fuzzy Short Time Series (FSTS) to cluster short time series microarray gene expression data and characterize phases of dynamic cellular processes.

Key Features:

• DE integration: Integrates Differential Evolution (DE) into Fuzzy Short Time Series (FSTS) clustering for short time series microarray gene expression analysis.
• Multi-functionality of genes: Accounts for genes that perform multiple biological functions to enable comprehensive pathway-level analysis.
• Relative change evaluation: Measures similarity by relative changes in amplitude over time rather than absolute expression values.
• Adaptive clustering: Dynamically determines the number of clusters to avoid arbitrary cluster assignments.

Scientific Applications:

• Influenza A (H1N1) host response analysis: Applied to study host response mechanisms during Influenza A (H1N1) infection using synthetic and experimental datasets.
• Pathway timeline inference: Identified timelines for pathways such as cell cycle regulation and homeostasis from short time series data.
• Antibody response and B cell dynamics: Detected sustained antibody-related activity up to 60 days post-infection, informing B cell dynamics during viral infection.
• Kinetic modeling support: Provides temporal phase information useful for kinetic modeling of biological processes with previously uncharacterized timelines.

Methodology:

Combines Differential Evolution (DE) and Fuzzy Short Time Series (FSTS) algorithms to optimize clustering of short time series microarray gene expression data and identify distinct phases in dynamic cellular processes.