ArticlePLoS computational biology2016
Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Exploring the mono-/bistability range of positively autoregulated signaling systems in the presence of competing transcription factor binding sites.PLoS computational biology · 2022Article
- Dynamics and Sensitivity of Signaling Pathways.Current pathobiology reports · 2022Article
- Article
- Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach.BMC genomics · 2018Article
- Enzyme sequestration by the substrate: An analysis in the deterministic and stochastic domains.PLoS computational biology · 2018Article
- Sharing of Phosphatases Promotes Response Plasticity in Phosphorylation Cascades.Biophysical journal · 2018Article
- Designing synthetic networks in silico: a generalised evolutionary algorithm approach.BMC systems biology · 2017Article
- Dynamical predictors of an imminent phenotypic switch in bacteria.Physical biology · 2017Article
- Core signalling motif displaying multistability through multi-state enzymes.Journal of the Royal Society, Interface · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.