Engineering stability in gene networks by autoregulation

Becskei, Attila; Serrano, Luís

doi:10.1038/35014651

Cited by 1,392 publications

(1,038 citation statements)

References 22 publications

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“…In this case, an increase of the nonlinear damping can lead to a decrease of the linewidth, known as noise suppression due to nonlinear feedback [66,67] which has been widely observed in various fields such as optics [68], mechanics [69], and biology [70]. While this nonlinear feedback typically requires an external feedback element, in spin-valves it is inherent.…”

Section: Spin Valvesmentioning

confidence: 99%

Self-consistent calculation of spin transport and magnetization dynamics

et al. 2013

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A spin-polarized current transfers its spin-angular momentum to a local magnetization, exciting various types of current-induced magnetization dynamics. So far, most studies in this field have focused on the direct effect of spin transport on magnetization dynamics, but ignored the feedback from the magnetization dynamics to the spin transport and back to the magnetization dynamics. Although the feedback is usually weak, there are situations when it can play an important role in the dynamics. In such situations, simultaneous, self-consistent calculations of the magnetization dynamics and the spin transport can accurately describe the feedback. This review describes in detail the feedback mechanisms, and presents recent progress in self-consistent calculations of the coupled dynamics. We pay special attention to three representative examples, where the feedback generates non-local effective interactions for the magnetization after the spin accumulation has been integrated out. Possibly the most dramatic feedback example is the dynamic instability in magnetic nanopillars with a single magnetic layer. This instability does not occur without non-local feedback. We demonstrate that full self-consistent calculations generate simulation results in much better agreement with experiments than previous calculations that addressed the feedback effect approximately.The next example is for more typical spin valve nanopillars. Although the effect of feedback is less dramatic because even without feedback the current can make stationary states unstable and induce magnetization oscillation, the feedback can still have important consequences. For instance, we show that the feedback can reduce the linewidth of oscillations, in agreement with experimental observations. A key aspect of this reduction is the suppression of the excitation of short wave length spin waves by the non-local feedback.Finally, we consider nonadiabatic electron transport in narrow domain walls. The non-local feedback in these systems leads to a significant renormalization of the effective nonadiabatic 3 spin transfer torque. These examples show that the self-consistent treatment of spin transport and magnetization dynamics is important for understanding the physics of the coupled dynamics and for providing a bridge between the ongoing research fields of current-induced magnetization dynamics and the newly emerging fields of magnetization-dynamics-induced generation of charge and spin currents.

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Section: Spin Valvesmentioning

confidence: 99%

Self-consistent calculation of spin transport and magnetization dynamics

et al. 2013

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“…Such negative feedback loops occur commonly in many cellular genes (Alon 2007) and have been hypothesized as a mechanism to reduce stochastic fluctuations in protein levels (Becskei & Serrano 2000). A negative feedback can be easily implemented in the above SHS model by assuming that the promoter is more likely to transition to the OFF state if the protein count increases within the cell.…”

Section: (A) Modelling Gene Regulatory Networkmentioning

confidence: 99%

Stochastic hybrid systems for studying biochemical processes

Singh

Hespanha

2010

Phil. Trans. R. Soc. A.

129

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Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently developed techniques that allow fast computations of the statistical moments of the population count, without having to run computationally expensive Monte Carlo simulations of the biochemical reactions. Finally, we review different examples from the literature that illustrate the benefits of using SHSs for modelling biochemical processes.

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“…A powerful approach to test our understanding gene regulatory networks is to modify existing networks or to build new networks from scratch in an approach called synthetic biology. Several small systems have been designed and tested in vivo in Escherichia coli, yeast and other organisms, see for example work by Becskei & Serrano (2000), Gardner et al (2000), Kobayashi et al (2004), and the reviews by Ball (2004) and Kaern et al (2003).…”

Section: Indirect Effectsmentioning

confidence: 99%

Modelling in molecular biology: describing transcription regulatory networks at different scales

Schlitt

Brāzma

2006

Phil. Trans. R. Soc. B

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Approaches to describe gene regulation networks can be categorized by increasing detail, as network parts lists, network topology models, network control logic models or dynamic models. We discuss the current state of the art for each of these approaches. We study the relationship between different topology models, and give examples how they can be used to infer functional annotations for genes of unknown function. We introduce a new simple way of describing dynamic models called finite state linear model (FSLM). We discuss the gap between the parts list and topology models on one hand, and network logic and dynamic models, on the other hand. The first two classes of models have reached a genome-wide scale, while for the other model classes high-throughput technologies are yet to make a major impact.

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Engineering stability in gene networks by autoregulation

Cited by 1,392 publications

References 22 publications

Self-consistent calculation of spin transport and magnetization dynamics

Self-consistent calculation of spin transport and magnetization dynamics

Stochastic hybrid systems for studying biochemical processes

Modelling in molecular biology: describing transcription regulatory networks at different scales

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