
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.
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
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
| 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). | 16 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
