Evidence map›Paper›PMID 28978431›Full record

ArticleBiophysical journal2017

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

Michael L Blinov, James C Schaff, Dan Vasilescu, Ion I Moraru, Judy E Bloom, Leslie M Loew

Abstract read
In one paragraph

Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Article
  2. Article
  3. Protein interactions, calcium, phosphorylation, and cholesterol modulate CFTR cluster formation on membranes.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. bnglViz: online visualization of rule-based models.Bioinformatics (Oxford, England) · 2024
    Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Mechanistic dissection of spatial organization in NF-κB signaling pathways by hybrid simulations.Integrative biology : quantitative biosciences from nano to macro · 2021
    Article
  16. Article
  17. Review
  18. An inactive receptor-G protein complex maintains the dynamic range of agonist-induced signaling.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael L BlinovR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut. Electronic address: blinov@uchc.edu.
James C SchaffR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut.
Dan VasilescuR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut.
Ion I MoraruR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut.
Judy E BloomR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut.
Leslie M LoewR. D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut. Electronic address: les@volt.uchc.edu.

Funding

TR&D4 Spatial Modeling: Modeling Framework for Cell MotilityP41GM103313 · NIGMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI LOEW, LESLIE M · 2012 to 2018
$9.7M
Bringing BioNetGen Rule-Based Modeling to Virtual Cell UsersR01GM095485 · NIGMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI BLINOV, MICHAEL, MORARU, ION I. · 2011 to 2013
$867k
NIGMS NIH HHS P41 GM103313NIGMS NIH HHS R01 GM095485
6 · The paper itself

Abstract

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator.

Indexed as

Cell Physiological PhenomenaComputer SimulationModels, MolecularDiffusionStochastic ProcessesUser-Computer Interface

Identifiers

PMID28978431
PMCPMC5627391

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.