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Hybrid CME-ODE method for efficient simulation of the galactose switch in yeast
David M Bianchi1,2, Joseph R Peterson1, Tyler M Earnest1,2,3
1Department of Chemistry, University of Illinois at Urbana-Champaign, 505 S Mathews Ave, Urbana, USA.
IET Systems Biology
|January 16, 2021
Summary
A new hybrid method speeds up cell simulations by 10-50x. This computational approach accurately models gene expression noise using chemical master equations (CME) and ordinary differential equations (ODE).
Area of Science:
- Computational biology
- Systems biology
- Biophysics
Background:
- Stochasticity in gene expression introduces noise affecting cellular fate.
- The galactose genetic switch in yeast involves complex gene expression, feedback, and transport.
- Traditional chemical master equations (CME) are computationally intensive for simulations.
Purpose of the Study:
- To enhance the computational efficiency of cell simulations for systems with high particle numbers.
- To implement a hybrid stochastic-deterministic (CME-ODE) method for improved simulation speed.
- To integrate this method into the GPU-based Lattice Microbes (LM) software and its Python interface (pyLM).
Main Methods:
- Implementation of a hybrid CME-ODE solver within the Lattice Microbes (LM) software.
- Utilizing GPU acceleration for enhanced computational performance.
- Application of the hybrid method to model the galactose genetic switch in Saccharomyces cerevisiae.
Main Results:
- Achieved a 10-50x speedup in simulations compared to the pure stochastic simulation algorithm (SSA).
- Generated protein distributions and species traces comparable to those from pure SSA CME simulations.
- Demonstrated the efficacy of the hybrid CME-ODE method for complex cellular systems.
Conclusions:
- The hybrid CME-ODE method offers a significant computational advantage for simulating gene expression noise.
- LM and pyLM provide a versatile platform for high-performance simulation of cellular processes.
- This approach enables more efficient study of cellular dynamics in systems with high molecule counts.
Keywords:
GPU-based lattice microbesSaccharomyces cerevisiaebiochemistrybiological techniquesbiology computingcell simulationscellular biophysicschemical master equationscomplex cellular systemsdistinct phenotypesfeedback loopsgalactose genetic switchgalactose switchgene expressiongenetic switchesgeneticshigh particle number systemshigh-performance reaction-diffusion master equations-CME-ODE solvershybrid CME-ODE methodhybrid stochastic-deterministic methodlattice microbes software suiteliving cellmaster equationmicroorganismsmolecular biophysicsprotein distributionsproteinsreaction kinetics theoryreaction-diffusion systemsstochastic processesstochastic simulation algorithmstochasticitysugar particlessugar transportersyeastMore Related Videos
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