Evidence map›Paper›PMID 28681552›Full record

ArticleCPT: pharmacometrics & systems pharmacology2017

Logic Modeling in Quantitative Systems Pharmacology.

Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026
    Review
  2. Review
  3. Article
  4. Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer.International journal of molecular sciences · 2021
    Review
  10. Review
  11. Article
  12. Article
  13. Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018
    Review
  14. 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

5 authors.

Pauline TraynardInstitut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.
Luis TobalinaRWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.
Federica EduatiEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK.
Laurence CalzoneInstitut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.
Julio Saez-RodriguezRWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

Indexed as

Models, TheoreticalHumansPharmaceutical PreparationsPharmacologySignal TransductionWorkflowPharmaceutical Preparations

Identifiers

PMID28681552
PMCPMC5572374

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.