433 research outputs found

    Inverse energy cascade in ocean macroscopic turbulence: Kolmogorov self-similarity in surface drifter observations and Richardson-Obhukov constant

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    We combine two point velocity and position data from surface drifter observations in the Benguela upwelling region off the coast of Namibia. The compensated third order longitudinal velocity structure function ⟹Δuℓ3⟩/s\left\langle{\Delta u_{\ell}^{\rm 3}}\right\rangle/s shows a positive plateau for inertial separations ss roughly between 9 km9~\rm{km} and 120 km120~\rm{km} revealing an inverse energy cascade with energy transfer rate Δ≃1.2±0.1⋅10−7m3/s2\varepsilon\simeq 1.2 \pm 0.1 \cdot 10^{-7} m^3/s^2. Deviations from Gaussianity of the corresponding probability distribution P(Δuℓ∣s)P(\Delta u_{\ell} |s) of two-point velocity increments Δuℓ\Delta u_{\ell} for given pair separation ss show up in the nth^{th} antisymetric structure functions S−(n)(r)=∫un(P(u)−P(−u)duS_{-}^{(n)}(r)=\int u^n(P(u)-P(-u)d u, which scale in agreement with Kolmogorov's prediction, S−(n)(r)∌r(n/3)S_{-}^{(n)}(r)\sim r^{(n/3)}, for n=2,4,6n=2,4,6. The combination of Δ\varepsilon with Richardson dispersion ⟹s2(t)⟩=gΔt3\left\langle s^2(t)\right\rangle=g\varepsilon t^3, where ⟹s2(t)⟩\left\langle s^2(t)\right\rangle is mean squared pair separation at time t t, reveals a Richardson-Obhukov constant of g≃0.11±0.03g\simeq 0.11\pm 0.03.Comment: 6 pages, 5 figure

    Standard Anatomical and Visual Space for the Mouse Retina: Computational Reconstruction and Transformation of Flattened Retinae with the Retistruct Package

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    The concept of topographic mapping is central to the understanding of the visual system at many levels, from the developmental to the computational. It is important to be able to relate different coordinate systems, e.g. maps of the visual field and maps of the retina. Retinal maps are frequently based on flat-mount preparations. These use dissection and relaxing cuts to render the quasi-spherical retina into a 2D preparation. The variable nature of relaxing cuts and associated tears limits quantitative cross-animal comparisons. We present an algorithm, "Retistruct," that reconstructs retinal flat-mounts by mapping them into a standard, spherical retinal space. This is achieved by: stitching the marked-up cuts of the flat-mount outline; dividing the stitched outline into a mesh whose vertices then are mapped onto a curtailed sphere; and finally moving the vertices so as to minimise a physically-inspired deformation energy function. Our validation studies indicate that the algorithm can estimate the position of a point on the intact adult retina to within 8° of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. Retistruct is used to investigate the organisation of the adult mouse visual system. We orient the retina relative to the nictitating membrane and compare this to eye muscle insertions. To align the retinotopic and visuotopic coordinate systems in the mouse, we utilised the geometry of binocular vision. In standard retinal space, the composite decussation line for the uncrossed retinal projection is located 64° away from the retinal pole. Projecting anatomically defined uncrossed retinal projections into visual space gives binocular congruence if the optical axis of the mouse eye is oriented at 64° azimuth and 22° elevation, in concordance with previous results. Moreover, using these coordinates, the dorsoventral boundary for S-opsin expressing cones closely matches the horizontal meridian

    BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

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    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data

    Critical dimensions for random walks on random-walk chains

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    The probability distribution of random walks on linear structures generated by random walks in dd-dimensional space, Pd(r,t)P_d(r,t), is analytically studied for the case ÎŸâ‰Ąr/t1/4â‰Ș1\xi\equiv r/t^{1/4}\ll1. It is shown to obey the scaling form Pd(r,t)=ρ(r)t−1/2Ο−2fd(Ο)P_d(r,t)=\rho(r) t^{-1/2} \xi^{-2} f_d(\xi), where ρ(r)∌r2−d\rho(r)\sim r^{2-d} is the density of the chain. Expanding fd(Ο)f_d(\xi) in powers of Ο\xi, we find that there exists an infinite hierarchy of critical dimensions, dc=2,6,10,
d_c=2,6,10,\ldots, each one characterized by a logarithmic correction in fd(Ο)f_d(\xi). Namely, for d=2d=2, f2(Ο)≃a2Ο2lnâĄÎŸ+b2Ο2f_2(\xi)\simeq a_2\xi^2\ln\xi+b_2\xi^2; for 3≀d≀53\le d\le 5, fd(Ο)≃adΟ2+bdΟdf_d(\xi)\simeq a_d\xi^2+b_d\xi^d; for d=6d=6, f6(Ο)≃a6Ο2+b6Ο6lnâĄÎŸf_6(\xi)\simeq a_6\xi^2+b_6\xi^6\ln\xi; for 7≀d≀97\le d\le 9, fd(Ο)≃adΟ2+bdΟ6+cdΟdf_d(\xi)\simeq a_d\xi^2+b_d\xi^6+c_d\xi^d; for d=10d=10, f10(Ο)≃a10Ο2+b10Ο6+c10Ο10lnâĄÎŸf_{10}(\xi)\simeq a_{10}\xi^2+b_{10}\xi^6+c_{10}\xi^{10}\ln\xi, {\it etc.\/} In particular, for d=2d=2, this implies that the temporal dependence of the probability density of being close to the origin Q2(r,t)≡P2(r,t)/ρ(r)≃t−1/2ln⁥tQ_2(r,t)\equiv P_2(r,t)/\rho(r)\simeq t^{-1/2}\ln t.Comment: LATeX, 10 pages, no figures submitted for publication in PR

    Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

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    BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches

    Characterization of physical properties of a coastal upwelling filament with evidence of enhanced submesoscale activity and transition from balanced to unbalanced motions in the Benguela upwelling region

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    We combine high-resolution in situ data (acoustic Doppler current profiler (ADCP), Scanfish, and surface drifters) and remote sensing to investigate the physical characteristics of a major filament observed in the Benguela upwelling region. The 30–50 km wide and about 400 km long filament persisted for at least 40 d. Mixed-layer depths were less than 40 m in the filament and over 60 m outside of it. Observations of the Rossby number Ro from the various platforms provide the spatial distribution of Ro for different resolutions. Remote sensing focuses on geostrophic motions of the region related to the mesoscale eddies that drive the filament formation and thereby reveals |Ro|&lt;0.1. Ship-based measurements in the surface mixed layer reveal 0.5&lt;|Ro|&lt;1, indicating the presence of unbalanced, ageostrophic motions. Time series of Ro from triplets of surface drifters trapped within the filament confirm these relatively large Ro values and show a high variability along the filament. A scale-dependent analysis of Ro, which relies on the second-order velocity structure function, was applied to the latter drifter group and to another drifter group released in the upwelling zone. The two releases explored the area nearly distinctly and simultaneously and reveal that at small scales (&lt;15 km) Ro values are twice as large in the filament in comparison to its environment with Ro&gt;1 for scales smaller than ∌500 m. This suggests that filaments are hotspots of ageostrophic dynamics, pointing to the presence of a forward energy cascade. The different dynamics indicated by our Ro analysis are confirmed by horizontal kinetic energy wavenumber spectra, which exhibit a power law k−α with α∌5/3 for wavelengths 2π/k smaller than a transition scale of 15 km, supporting significant submesoscale energy at scales smaller than the first baroclinic Rossby radius (Ro1∌30 km). The detected transition scale is smaller than those found in regions with less mesoscale eddy energy, consistent with previous studies. We found evidence for the processes which drive the energy transfer to turbulent scales. Positive Rossby numbers (1) associated with cyclonic motion inhibit the occurrence of positive Ertel potential vorticity (EPV) and stabilize the water column. However, where the baroclinic component of EPV dominates, submesoscale instability analysis suggests that mostly gravitational instabilities occur and that symmetric instabilities may be important at the filament edges.</p

    SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks

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    <p>Abstract</p> <p>Background</p> <p>The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language). This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation). The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations.</p> <p>Results</p> <p>SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules) as well as the regulatory relations (activation, inhibition or other modulations) of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format.</p> <p>Conclusion</p> <p>SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for each reaction, only applicable kinetic formulas are considered. This modeling scheme creates kinetics in accordance with the diagrammatic representation. In contrast most previously published tools have relied on the stoichiometry and generic modulators of a reaction, thus ignoring and potentially conflicting with the information expressed through the process diagram. Additional material and the source code can be found at the project homepage (URL found in the Availability and requirements section).</p
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