Model 5 39;3

[Marthe Bernskoetter]

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Communication between the 5' and 3' ends of mature eukaryotic mRNAs lies at the heart of gene regulation, likely arising at the same time as the eukaryotic lineage itself. Our view of how and why it occurs has been shaped by elegant experiments that led to nearly universal acceptance of the "closed-loop model." However, new observations suggest that this classic model needs to be reexamined, revised, and expanded. Here, we address fundamental questions about the closed-loop model and discuss how a growing understanding of mRNA structure, dynamics, and intermolecular interactions presents new experimental opportunities. We anticipate that the application of emerging methods will lead to expanded models that include the role of intrinsic mRNA structure and quantitative dynamic descriptions of 5'-3' proximity linked to the functional status of an mRNA and will better reflect the messy realities of the crowded and rapidly changing cellular environment.

Mark Eglon is theFashionSpot's Editor-in-Chief. After starting as a freelance writer, Mark has over 3000 published articles to his name. A magazine enthusiastic since circa 2005, Mark enjoys collecting both current and past issues and partaking in discussions on theFashionSpot's forums. Anything with models Gisele Bndchen, Kate Moss or Natasha Poly and he's sold - immediately.

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Nondimensional fluxes of total energy (top) and potential enstrophy (bottom), time-averaged at equilibrium, for the 129-km case. Thin solid curve in the bottom panel is energy flux times m2. Very thin horizontal line indicates zero flux.

Vertically integrated components of energy tendency, based on transient departures from seasonal means, multipled by total spherical wavenumber n and plotted vs log n. Solid (winter) and short-dashed (summer) curves are due to nonlinear interactions, and dotted (winter) and dot-dashed (summer) curves are due to the combination of baroclinic conversion, dissipation, vertical fluxes, and analysis increments. [Reproduced with permission from Straus and Ditlevsen (1999)]

The picture that emerges for the energy spectrum of atmospheric turbulence from a few kilometers to tens of thousands of kilometers is actually quite simple. The potential energy of the mean flow, which is derived from solar heating with no scale dependence, is transferred selectively to the long synoptic scales of motion via the mechanism of (nonlinear) baroclinic instability. The injected energy moves both upscale, to the planetary waves where it is damped by Ekman damping, and also downscale, through the short synoptic waves, through the mesoscales, to the short mesoscales, where it can be damped by viscous dissipation. There is no need for dynamics other than QG to produce the spectrum. (However, the present work cannot be used to rule out other explanations, such as gravity wave generation, or a separate energy source at the small scales.)

The observed features of the spectrum are simulated here numerically with a simple two-level QG model, which does not contain gravity waves. The two-level model appears to be the simplest model possessing what we believe is the relevant mechanism in the atmosphere for energy and enstrophy injection into the synoptic scales of motion: self-excited baroclinic instability drawing on the available potential energy of the mean flow, which in turn is forced by the thermal energy of the sun. As the amplifying baroclinic waves saturate nonlinearly (see Welch and Tung 1998b), a large fraction of the injected energy moves upscale, contributing to the large-scale variability of the planetary waves. A remaining small part moves downscale. In the atmosphere the downscale transfer of energy probably involves the mechanism of frontogenesis (Hoskins and Bretherton 1972), as the horizontal scales of a maturing baroclinic wave collapse near the surface and the tropopause, as well as the process of gravity wave generation by QG flows ( la Yuan and Hamilton 1994). It is likely that these small scales are then dissipated. None of these processes at the small scales can be represented by the present model, first because of the QG scaling and second because of the coarse vertical resolution. Instead we use subgrid hyperdiffusion near the scale of numerical truncation to include a crude sink at the smallest scales. As a result of this small energy sink there is a small flux of energy downscale in the model. This flux, though small, is crucial in explaining the two-sloped shape of the spectrum.

Figure 3 shows a typical energy spectrum (thick solid curve) from our numerical model at 107-km resolution. (The thick dashed curve is discussed below.) The subgrid dissipation is chosen to be 10 times Ekman damping at the last scale before truncation. This is close to the smallest value of subgrid dissipation that we can use before we encounter numerical problems.

The large-scale low-frequency variability seen in Fig. 3, which exists for runs with the same resolution, is also seen in Fig. 4 at the planetary scales. It should not be taken as a problem with numerical convergence.

The relationship between η and ϵ is determined by the energy and enstrophy injection and dissipation mechanisms. The consistency between our dissipation rates and observed dissipation rates is discussed in section 4f.

The small-scale end of the mesoscale range is actually a dissipation range, for that is where the subgrid damping becomes important. This can be seen from Fig. 5, bottom panel, to encompass the last 10 or so wavenumbers for the 129-km case (and similarly for other resolutions). Thus the last part of each spectrum in Figs. 3 and 4 is dynamically separate from the rest of the mesoscales.

For comparison, we present in Fig. 8 energetics from Straus and Ditlevsen (1999) using reanalyzed ECMWF data. The observed data agree with our model's energetics of Figs. 5 and 6, showing different balances in the different wavenumber ranges: nonlinear gain and dissipative loss in the planetary scales; baroclinic gain and nonlinear loss in the synoptic scales; and nonlinear gain and dissipative loss in the subsynoptic and mesoscales. Figure 10a of Straus and Ditlevsen (1999) also shows downscale energy flux in the mesoscales, and Fig. 10b downscale enstrophy flux. Thus our model simulations with only QG dynamics are reproducing the energetics seen in observations.

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