Evidence map›Paper›PMID 27325906›Full record

ArticleTheoretical computer science2016

Modular verification of chemical reaction network encodings via serializability analysis.

Matthew R Lakin, Darko Stefanovic, Andrew Phillips

Abstract read
In one paragraph

Article in Theoretical computer science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. A reaction network scheme for hidden Markov model parameter learning.Journal of the Royal Society, Interface · 2023
    Article
  2. 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

3 authors.

Matthew R LakinDepartment of Computer Science, University of New Mexico, Albuquerque, NM, USA.
Darko StefanovicDepartment of Computer Science, University of New Mexico, Albuquerque, NM, USA; Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, USA.
Andrew PhillipsMicrosoft Research, Cambridge, UK.

Funding

Integrative Cancer Nanoscience and Microsystems (IC-NSMS) Training CenterR25CA153825 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI DATYE, ABHAYA KRISHNA, OLIVER, JANET M. · 2010 to 2014
$1.8M
Amplified detection of viral RNA using catalytic DNA logic circuitsR21AI115105 · NIAID · UNIVERSITY OF NEW MEXICO · PI GRAVES, STEVEN W · 2015 to 2016
$378k
NCI NIH HHS R25 CA153825NIAID NIH HHS R21 AI115105
6 · The paper itself

Abstract

Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of

Indexed as

Chemical reaction networksDNA strand displacementModular verificationSerializability

Identifiers

PMID27325906
PMCPMC4911709

What OpenQuestion holds

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Registered trials

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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.