Hybrid CME–ODE method for efficient simulation of the galactose switch in yeast

• Published in: IET systems biology Vol. 12; no. 4; pp. 170 - 176
• Main Authors: Bianchi, David M, Peterson, Joseph R, Earnest, Tyler M, Hallock, Michael J, Luthey-Schulten, Zaida
• Format: Journal Article
• Language: English
• Published: England The Institution of Engineering and Technology 01.08.2018; Wiley
• Subjects: biochemistry; biological techniques; biology computing; cell simulations; cellular biophysics; chemical master equations; complex cellular systems; distinct phenotypes; feedback loops; galactose genetic switch; galactose switch; gene expression; genetic switches; genetics; GPU‐based lattice microbes; high particle number systems; high‐performance reaction‐diffusion master equations‐CME‐ODE solvers; hybrid CME‐ODE method; hybrid stochastic‐deterministic method; lattice microbes software suite; living cell; master equation; microorganisms; molecular biophysics; protein distributions; proteins; reaction kinetics theory; reaction‐diffusion systems; Research Article; Saccharomyces cerevisiae; stochastic processes; stochastic simulation algorithm; stochasticity; sugar particles; sugar transporters; yeast; protein distributions; cellular biophysics; hybrid CME-ODE method; high-performance reaction-diffusion master equations-CME-ODE solvers; reaction kinetics theory; hybrid stochastic-deterministic method; lattice microbes software suite; genetics; biology computing; proteins; galactose genetic switch; biological techniques; living cell; GPU-based lattice microbes; stochastic processes; distinct phenotypes; genetic switches; stochastic simulation algorithm; feedback loops; reaction-diffusion systems; cell simulations; complex cellular systems; yeast; sugar particles; master equation; microorganisms; biochemistry; stochasticity; sugar transporters; chemical master equations; molecular biophysics; Saccharomyces cerevisiae; galactose switch; gene expression; high particle number systems
• ISSN: 1751-8849; 1751-8857; 1751-8857
• DOI: 10.1049/iet-syb.2017.0070

It is well known that stochasticity in gene expression is an important source of noise that can have profound effects on the fate of a living cell. In the galactose genetic switch in yeast, the unbinding of a transcription repressor is induced by high concentrations of sugar particles activating gene expression of sugar transporters. This response results in high propensity for all reactions involving interactions with the metabolite. The reactions for gene expression, feedback loops and transport are typically described by chemical master equations (CME). Sampling the CME using the stochastic simulation algorithm (SSA) results in large computational costs as each reaction event is evaluated explicitly. To improve the computational efficiency of cell simulations involving high particle number systems, the authors have implemented a hybrid stochastic–deterministic (CME–ODE) method into the publically available, GPU-based lattice microbes (LM) software suite and its python interface pyLM. LM and pyLM provide a convenient way to simulate complex cellular systems and interface with high-performance RDME/CME/ODE solvers. As a test of the implementation, the authors apply the hybrid CME-ODE method to the galactose switch in Saccharomyces cerevisiae, gaining a 10–50× speedup while yielding protein distributions and species traces similar to the pure SSA CME.