Markov Chain modeling of pyelonephritis-associated pili expression in uropathogenic Escherichia coli

Area of Science:

• Microbiology
• Computational Biology
• Genetics

Background:

• Uropathogenic Escherichia coli (UPEC) utilizes pyelonephritis-associated pili (Pap) for infection.
• Pap expression is controlled by a complex phase variation mechanism involving leucine-responsive regulatory protein (Lrp) and DNA adenine methylase (Dam).
• Existing stochastic models like the Gillespie algorithm are computationally intensive for detailed simulations.

Purpose of the Study:

• To develop a computationally efficient Markov Chain model for simulating Pap expression dynamics in UPEC.
• To analytically derive the model from the known molecular mechanisms of Pap phase variation.
• To validate the model against existing methods and experimental data.

Main Methods:

• Development of a novel Markov Chain model based on the molecular mechanism of Pap phase variation.
• Analytical derivation of the model equations.
• Comparison of simulation results with the Gillespie algorithm and experimental data.

Main Results:

• The Markov Chain model accurately reproduces results obtained using the Gillespie method.
• The model runs more efficiently by incorporating regulatory information prior to simulation.
• Model predictions align with experimental data, confirming initial-state dependency of Pap expression.
• A population reaches an equilibrium level of Pap expression within 50-100 generations without environmental stimuli.
• Transient time to equilibrium is influenced by PapI stability and Lrp/Dam copy numbers.

Conclusions:

• The Markov Chain model effectively captures the essential dynamics of Pap phase variation in UPEC.
• This model offers a significant computational simplification for studying UPEC population dynamics.
• The model facilitates further investigation into regulatory features influencing Pap expression.