GENAVOS

GENAVOS models and analyzes gene regulatory networks using delayed nonlinear variable order fractional systems to study dynamic behaviors in normal and cancer cells.

Key Features:

• Delayed Nonlinear Variable Order Fractional Systems: Implements fractional-calculus models with delays and variable orders to capture temporal complexity in GRNs.
• Integration of Gene Expression Time Series: Incorporates real gene expression time series data for system identification and parameter estimation.
• Dynamic System Analysis: Analyzes behaviors such as chaos, periodicity, and quasi-periodicity by manipulating delay, degradation, and synthesis rates.
• Cellular Memory Insights: Links time-varying orders in GRNs to potential cellular memory mechanisms influenced by epigenetic and environmental factors.
• Normal vs. Cancer Behavior Comparison: Simulates shifts from normal to cancer-like dynamics by adjusting delay parameters and the variable order function.
• miR-17-92 Cluster Role: Highlights the regulatory influence of the miR-17-92 microRNA cluster on the cancer cell cycle.

Scientific Applications:

• Understanding Disease Mechanisms: Investigates how alterations in GRNs contribute to cancer onset and progression.
• Identifying Therapeutic Targets: Supports identification of regulatory elements, such as the miR-17-92 cluster, as potential intervention points.
• Simulating Cellular Behaviors: Predicts cellular responses to genetic or environmental changes through dynamic simulations of GRNs.

Methodology:

Models GRNs using delayed nonlinear variable order fractional systems, performs parameter optimization using gene expression time series, and analyzes dynamic behaviors (chaos, periodicity, quasi-periodicity) by varying delay, degradation, and synthesis rates.