Evidence map›Paper›PMID 33109021›Full record

ArticleJournal of the Royal Society, Interface2020

A hidden integral structure endows absolute concentration robust systems with resilience to dynamical concentration disturbances.

Daniele Cappelletti, Ankit Gupta, Mustafa Khammash

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Daniele CappellettiDepartment of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26 4058 Basel, Switzerland.
Ankit GuptaDepartment of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26 4058 Basel, Switzerland.
Mustafa KhammashDepartment of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26 4058 Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biochemical systems that express certain chemical species of interest at the same level at any positive steady state are called 'absolute concentration robust' (ACR). These species behave in a stable, predictable way, in the sense that their expression is robust with respect to sudden changes in the species concentration, provided that the system reaches a (potentially new) positive steady state. Such a property has been proven to be of importance in certain gene regulatory networks and signaling systems. In the present paper, we mathematically prove that a well-known class of ACR systems studied by Shinar and Feinberg in 2010 hides an internal integral structure. This structure confers these systems with a higher degree of robustness than was previously known. In particular, disturbances much more general than sudden changes in the species concentrations can be rejected, and robust perfect adaptation is achieved. Significantly, we show that these properties are maintained when the system is interconnected with other chemical reaction networks. This key feature enables the design of insulator devices that are able to buffer the loading effect from downstream systems-a crucial requirement for modular circuit design in synthetic biology. We further note that while the best performance of the insulators are achieved when these act at a faster timescale than the upstream module (as typically required), it is not necessary for them to act on a faster timescale than the downstream module in our construction.

Indexed as

Gene Regulatory NetworksModels, BiologicalAdaptation, PhysiologicalSignal TransductionSynthetic Biologyabsolute concentration robustnessinsulatorsintegral feedbackreaction networks

Identifiers

PMID33109021
PMCPMC7653391

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.