CellModeller

CellModeller models large-scale multi-cellular systems, focusing on microbial biofilms, to simulate three-dimensional biophysical interactions, genetic regulation, and intercellular signaling for studying community organization and engineering synthetic biofilms.


Key Features:

  • Multiscale Modeling: Combines three-dimensional biophysical models of individual cells with genetic regulation and intercellular signaling models to capture dynamics across biological scales.
  • Three-Dimensional Biophysical Modeling: Simulates physical interactions and spatial arrangements of individual cells within biofilms to assess positional effects on community function.
  • Genetic Regulation Models: Incorporates models of transcriptional regulation to simulate how gene expression influences cell behavior and biofilm development over time.
  • Intercellular Signaling: Models signaling pathways that mediate communication between cells and coordinate collective behaviors within the biofilm.
  • High-Performance GPU Scaling: Leverages parallel Graphics Processing Unit (GPU) architectures to scale simulations to >30,000 cells, enabling simulation of a ~100 μm diameter colony in approximately 30 minutes.

Scientific Applications:

  • Synthetic Biofilm Design: Predicts spatial organization and functional outcomes of engineered bacterial communities to optimize yields in industrial biotechnology, including pharmaceutical ingredient production and biofuel generation.
  • Medical Research on Biofilm Infections: Examines spatial organization and physiological cooperation in microbial biofilms to inform strategies for disrupting persistent, biofilm-associated infections.

Methodology:

Integrates three-dimensional biophysical modeling of individual cells, models of transcriptional regulation, intercellular signaling models, and parallel GPU-based computation.

Topics

Collections

Details

Cost:
Free of charge
Programming Languages:
Python
Added:
4/28/2022
Last Updated:
11/24/2024

Operations

Publications

Rudge TJ, Steiner PJ, Phillips A, Haseloff J. Computational Modeling of Synthetic Microbial Biofilms. ACS Synthetic Biology. 2012;1(8):345-352. doi:10.1021/sb300031n. PMID:23651288.

Documentation

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