Computational modeling of synthetic microbial biofilms

Area of Science:

• Microbiology
• Computational Biology
• Biophysics

Background:

• Microbial biofilms are self-organized bacterial communities with enhanced metabolic efficiency and resistance.
• Biofilms are valuable for chemical production (pharmaceuticals, biofuels) but also cause persistent infections.
• Designing synthetic biofilms is challenging due to complex interactions between genetic regulation, signaling, and cell physics.

Purpose of the Study:

• To develop a computational method for modeling synthetic microbial biofilms.
• To overcome limitations in multiscale modeling for realistic cell numbers.
• To provide a tool for predicting synthetic biofilm behavior before construction.

Main Methods:

• A computational method combining 3D biophysical models of individual cells with genetic regulation and intercellular signaling models.
• Implementation as a software tool named CellModeller.
• Utilizing parallel Graphics Processing Unit (GPU) architectures for scalability.

Main Results:

• The CellModeller software can simulate biofilms with over 30,000 cells (100 μm diameter colony).
• Simulations are completed in approximately 30 minutes of computation time.
• The method successfully integrates biophysical, genetic, and signaling aspects of biofilm formation.

Conclusions:

• The developed computational method and CellModeller software enable multiscale modeling of synthetic biofilms.
• This tool can accelerate the engineering of biofilms for industrial applications and the development of strategies to combat biofilm-related infections.
• The approach addresses the challenge of predicting synthetic biofilm behavior by simulating complex cellular interactions at scale.