ArticleFrontiers in physiology2018
DSGRN: Examining the Dynamics of Families of Logical Models.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Genetic network structure and dynamics: identifying simple negative feedback loops.Interface focus · 2025Article
- Identification of models describing gene expression data leveraging machine learning methods.Interface focus · 2025Article
- A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets.Mathematical biosciences · 2024Article
- Oscillator death in coupled biochemical oscillators.Mathematics of control, signals, and systems : MCSS · 2023Article
- Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.Journal of visualized experiments : JoVE · 2021Article
- Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits.Methods in molecular biology (Clifton, N.J.) · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a computational tool DSGRN for exploring the dynamics of a network by computing summaries of the dynamics of switching models compatible with the network across all parameters. The network can arise directly from a biological problem, or indirectly as the interaction graph of a Boolean model. This tool computes a finite decomposition of parameter space such that for each region, the state transition graph that describes the coarse dynamical behavior of a network is the same. Each of these parameter regions corresponds to a different logical description of the network dynamics. The comparison of dynamics across parameters with experimental data allows the rejection of parameter regimes or entire networks as viable models for representing the underlying regulatory mechanisms. This in turn allows a search through the space of perturbations of a given network for networks that robustly fit the data. These are the first steps toward discovering a network that optimally matches the observed dynamics by searching through the space of networks.
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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.