ArticleSystems and synthetic biology2015
Design principles for robust oscillatory behavior.
Article in Systems and synthetic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The Topological Characteristics of Biological Ratio-Sensing Networks.Life (Basel, Switzerland) · 2023Article
- Article
- Constructing network topologies for multiple signal-encoding functions.BMC systems biology · 2019Article
- Systems and synthetic biology approaches in understanding biological oscillators.Quantitative biology (Beijing, China) · 2018Article
- Incoherent Inputs Enhance the Robustness of Biological Oscillators.Cell systems · 2017Article
- Robust network topologies for generating oscillations with temperature-independent periods.PloS one · 2017Article
- A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators.ACS synthetic biology · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.
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