Evidence map›Paper›PMID 25976626›Full record

ArticleScientific reports2015

Notes on stochastic (bio)-logic gates: computing with allosteric cooperativity.

Elena Agliari, Matteo Altavilla, Adriano Barra, Lorenzo Dello Schiavo, Evgeny Katz

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Review
  2. Catalyst-based biomolecular logic gates.Catalysts (Basel, Switzerland) · 2022
    Article
  3. Combinatorial Control through Allostery.The journal of physical chemistry. B · 2019
    Article
  4. 3D models in the new era of immune oncology: focus on T cells, CAF and ECM.Journal of experimental & clinical cancer research : CR · 2019
    Review
  5. Article
  6. 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.

Elena AgliariDipartimento di Fisica, Sapienza Università di Roma, Italy.
Matteo AltavillaDipartimento di Matematica, Sapienza Università di Roma, Italy.
Adriano BarraDipartimento di Fisica, Sapienza Università di Roma, Italy.
Lorenzo Dello SchiavoDipartimento di Matematica, Sapienza Università di Roma, Italy.
Evgeny KatzDepartment of Chemistry and Biomolecular Science, Clarkson University, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called enzyme based logic) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. However, as biochemical systems are always affected by the presence of noise (e.g. thermal), standard logic is not the correct theoretical reference framework, rather we show that statistical mechanics can work for this scope: here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express noisy logical operators and/or perform stochastic logical operations. Mixing statistical mechanics with logics, and testing quantitatively the resulting findings on the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity).

Indexed as

LogicModels, StatisticalStochastic ProcessesAlgorithmsAllosteric RegulationComputers, MolecularKinetics

Identifiers

PMID25976626
PMCPMC5386197

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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.