A tunable synthetic mammalian oscillator

Tigges, Marcel; Marquez‐Lago, Tatiana T.; Stelling, Jörg; Fussenegger, Martin

doi:10.1038/nature07616

Cited by 539 publications

(444 citation statements)

References 28 publications

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“…On the contrary, negative autoregulation interactions are absent in robust topologies. Our result for positive autoregulation agrees with earlier studies which also demonstrated numerically (Tsai et al 2008) and experimentally Stricker et al 2008;Tigges et al 2009) the importance of positive autoregulation. In terms of having negative feedback loops in the network, at least one loop is required, with most robust topologies using at least two (Fig.…”

Section: Features Of Robust Oscillatory Topologiessupporting

confidence: 93%

“…4) or by inserting a third component in the positive branch of the feedback loop (Motifs 3, 4). Motif 1 is a well-known circuit, the ''amplified negative feedback oscillator'', where the key characteristic is to obtain significantly faster activator than repressor dynamics (Stricker et al 2008;Tigges et al 2009;Purcell et al 2010;Lomnitz and Savageau 2014). Motif 2, on the other hand, is less common in the literature, and it accounts more for not-so-robust topologies (median Oscillatory robustness is calculated as the fraction of parameter sets that yielded oscillatory responses parameters were randomly sampled 3 Â 10 5 times per topology.…”

Section: Motifs For Oscillatory Behaviormentioning

confidence: 99%

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Design principles for robust oscillatory behavior

2015

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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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Section: Features Of Robust Oscillatory Topologiessupporting

confidence: 93%

Section: Motifs For Oscillatory Behaviormentioning

confidence: 99%

Design principles for robust oscillatory behavior

2015

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“…S1 and S2). The kinetic parameters were selected from literature (21,30). We performed sensitivity analysis of the output node Z protein concentration against the mRNA species of nodes X and Y (thereby emulating RNAi perturbation).…”

Section: Significancementioning

confidence: 99%

Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks. A focal point of systems biology is the reverse engineering of gene regulatory networks (1-5). The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6-10). Despite significant advances, a fundamental reverse engineering bottleneck is the ability to discriminate between direct and indirect connections. In a simple case, assuming three nodes in a cascade formulation, where an input node is activating an intermediary node which in turn is activating an output node, a reverse engineering algorithm may infer an activating edge from the input node to the output, even though there is no direct biological interaction.Unfortunately, the limitation in correctly distinguishing the effects stemming from indirect connectivities is pervasive (11-13) and justifies the urgent need for new and reliable methods to eliminate spurious edges. Importantly, remedies to address this problem should not further muddle the interpretation by removing true network edges (14). A number of theoretical approaches have been proposed to overcome this hurdle (4, 15-18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.The majority of efforts to address the verification issue adopt in silico benchmark suites that are based on biological pathway approximations (19). Although these models do include a number of commonly observed topologies and have provided significant insights, they do not fully capture the complexity of the biological realm and the associated heterogeneity and intrinsic variability. On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, sy...

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“…Le premier réseau de ce type, connu sous le nom de Répressilateur, formé par trois répresseurs organisés de manière cyclique, a été construit voici près de dix ans [42]. Depuis, d'autres réseaux génétiques oscillants ont été synthétisés [43][44][45]. À côté des travaux consacrés aux nombreux rythmes qui scandent la dynamique cellulaire, la biologie synthétique ouvre ainsi des voies prometteuses à la compréhension des rythmes du vivant.…”

Section: Nouveaux Rythmes Nouveaux Oscillateursunclassified