ArticleIET systems biology2018
Hybrid CME-ODE method for efficient simulation of the galactose switch in yeast.
Article in IET systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Predicting human mRNA isoform levels from site-specific splicing kineticsbioRxiv : the preprint server for biology · 2026Article
- Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell.The journal of physical chemistry. B · 2026Article
- Dynamics of chromosome organization in a minimal bacterial cell.Frontiers in cell and developmental biology · 2023Article
- Toward the Complete Functional Characterization of a Minimal Bacterial Proteome.The journal of physical chemistry. B · 2022Article
- The correlation between cell and nucleus size is explained by an eukaryotic cell growth model.PLoS computational biology · 2022Article
- Article
- Generating Chromosome Geometries in a Minimal Cell From Cryo-Electron Tomograms and Chromosome Conformation Capture Maps.Frontiers in molecular biosciences · 2021Article
- An in-silico human cell model reveals the influence of spatial organization on RNA splicing.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.