RaTrav

RaTrav computes mean first-passage times (MFPTs) in Markovian network systems to analyze stochastic transition dynamics between initial and final states.

Key Features:

• Mean First-Passage Time (MFPT) computation: Computes MFPTs for network-like systems to quantify stochastic transition times between initial and final states.
• Markovian dynamics: Models Markovian transitions between initial states and one or more final states.
• Methodological approaches: Implements the combinatorial Hill technique and Monte Carlo simulation method, with method choice influenced by network topology and computational efficiency.
• Network input specification: Accepts a text file describing biochemical network structure including transition probabilities, waiting probabilities, and local times or edge weights.
• Cross-disciplinary relevance: Applies to problems in theoretical physics, chemistry, and biology where MFPT calculations are relevant.

Scientific Applications:

• Protein-protein docking funnel analysis: Calculates MFPTs to identify favorable binding pathways within protein-protein docking funnels for analysis of molecular interactions and informing drug design.
• Free energy transduction interpretation: Analyzes MFPTs between enzyme conformational states to interpret free energy transduction and quantify the degree of coupling in coupled enzymatic reactions.

Methodology:

Implements the combinatorial Hill technique and Monte Carlo simulation method for MFPT estimation on Markovian networks, using user-supplied text files specifying transition probabilities, waiting probabilities, and local times or edge weights, with method selection guided by network topology-dependent computational efficiency.