ArticleIEEE life sciences letters2016
GillesPy: A Python Package for Stochastic Model Building and Simulation.
Article in IEEE life sciences letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- DelaySSA: stochastic simulation of biochemical systems and gene regulatory networks with or without time delays.PLoS computational biology · 2025Article
- Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway.NPJ systems biology and applications · 2024Article
- Catalyst: Fast and flexible modeling of reaction networks.PLoS computational biology · 2023Article
- Cooperativity boosts affinity and specificity of proteins with multiple RNA-binding domains.NAR genomics and bioinformatics · 2023Article
- GillesPy2: A Biochemical Modeling Framework for Simulation Driven Biological Discovery.Letters in biomathematics · 2023Article
- Estimating the transfer rates of bacterial plasmids with an adapted Luria-Delbrück fluctuation analysis.PLoS biology · 2022Article
- Cheater suppression and stochastic clearance through quorum sensing.PLoS computational biology · 2022Article
- Saving the Devils Is in the Details: Tasmanian Devil Facial Tumor Disease Can Be Eliminated with Interventions.Letters in biomathematics · 2022Article
- Article
- A multiscale compartment-based model of stochastic gene regulatory networks using hitting-time analysis.The Journal of chemical physics · 2021Article
- Beyond Deterministic Models in Drug Discovery and Development.Trends in pharmacological sciences · 2020Review
- Comparison of solitary and collective foraging strategies ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning.Bioinformatics (Oxford, England) · 2019Article
- Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths.PloS one · 2019Article
- Approaches to Computational Strain Design in the Multiomics Era.Frontiers in microbiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community.
Indexed as
Identifiers
What OpenQuestion holds
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.