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Chaos An Interdisciplinary Journal of Nonlinear Science
Article . 2013 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2013
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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The dynamics of hybrid metabolic-genetic oscillators

Authors: Reznik, Ed; Kaper, Tasso J.; Segre, Daniel;

The dynamics of hybrid metabolic-genetic oscillators

Abstract

The synthetic construction of intracellular circuits is frequently hindered by a poor knowledge of appropriate kinetics and precise rate parameters. Here, we use generalized modeling (GM) to study the dynamical behavior of topological models of a family of hybrid metabolic-genetic circuits known as “metabolators.” Under mild assumptions on the kinetics, we use GM to analytically prove that all explicit kinetic models which are topologically analogous to one such circuit, the “core metabolator,” cannot undergo Hopf bifurcations. Then, we examine more detailed models of the metabolator. Inspired by the experimental observation of a Hopf bifurcation in a synthetically constructed circuit related to the core metabolator, we apply GM to identify the critical components of the synthetically constructed metabolator which must be reintroduced in order to recover the Hopf bifurcation. Next, we study the dynamics of a re-wired version of the core metabolator, dubbed the “reverse” metabolator, and show that it exhibits a substantially richer set of dynamical behaviors, including both local and global oscillations. Prompted by the observation of relaxation oscillations in the reverse metabolator, we study the role that a separation of genetic and metabolic time scales may play in its dynamics, and find that widely separated time scales promote stability in the circuit. Our results illustrate a generic pipeline for vetting the potential success of a circuit design, simply by studying the dynamics of the corresponding generalized model.

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United States
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Keywords

Numerical and computational mathematics, Molecular Networks (q-bio.MN), FOS: Physical sciences, Quantitative Biology - Quantitative Methods, Models, Biological, Gene regulatory networks, Models, Oscillometry, mathematical, bacterial, Quantitative Biology - Molecular Networks, Gene Regulatory Networks, applied, Synthetic biology, Quantitative Methods (q-bio.QM), Gene expression regulation, Escherichia coli K12, Models, Genetic, Physics, Energy metabolism, Gene Expression Regulation, Bacterial, Applied mathematics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Physical sciences, Systems Integration, Kinetics, FOS: Biological sciences, Synthetic Biology, Systems integration, Fluids & plasmas, Networks, genetic, Science & technology, Energy Metabolism, Stability, Adaptation and Self-Organizing Systems (nlin.AO), Mathematics, biological

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Average
Average
Top 10%
Green
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