Evidence map›Paper›PMID 37160165›Full record

ReviewJournal of the Royal Society, Interface2023

Verifiable biology.

Savas Konur, Marian Gheorghe, Natalio Krasnogor

Abstract readReview
In one paragraph

Review in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Verifiable biology.Journal of the Royal Society, Interface · 2023
    Review
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

3 authors.

Savas KonurDepartment of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK.ORCID 0000-0002-0642-9452
Marian GheorgheDepartment of Computer Science, University of Bradford, Richmond Building, Bradford BD7 1DP, UK.
Natalio KrasnogorSchool of Computing Science, Newcastle University, Science Square, Newcastle upon Tyne NE4 5TG, UK.ORCID 0000-0002-2651-4320

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.

Indexed as

Computational BiologySoftwareComputer Simulationbiochemical networksbiological systemscomputational biologymodellingsynthetic biologyverification

Identifiers

PMID37160165
PMCPMC10169095

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.