Atlas

Atlas transforms genome graphs and biological networks into dynamic rule-based models to enable simulation and analysis of gene regulatory, interaction, and metabolic network dynamics.

Key Features:

• Dynamic Modeling: Converts static gene regulatory networks (GRNs) into dynamic representations and accounts for stochastic dynamics and interactions with other cellular processes.
• Rule-Based Framework: Implements rule-based modeling languages within the PySB framework using Python to represent gene interactions and regulatory mechanisms.
• Divide-and-Conquer Strategy: Decomposes complex networks into manageable sub-models that are later integrated into comprehensive ensemble models.
• Versatility Across Organisms: Applicable to modeling natural and synthetic networks across bacterial species, with specific examples for Escherichia coli.
• In Silico Experimentation: Enables simulation-based evaluation of genetic modifications such as gene knockouts and the insertion of promoters and terminators.

Scientific Applications:

• Gene Regulatory Network Analysis: Simulates gene expression dynamics to study transcriptional regulation and regulatory interactions.
• Metabolic Pathway Exploration: Uses dynamic models to investigate metabolic processes and predict cellular responses to environmental changes.
• Synthetic Biology: Supports design and in silico testing of synthetic biological networks prior to experimental implementation.

Methodology:

Transforms genome graphs and associated biological networks into rule-based models; decomposes complex networks into sub-models via a divide-and-conquer approach; integrates sub-models into ensemble models; conducts in silico simulations to evaluate genetic modifications; implements models using rule-based modeling languages within the PySB framework in Python.