NOSQL design for analytical workloads: Variability matters

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft

Connecting protein and mRNA burst distributions for stochastic models of gene expression

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression

Among-site variability in the stochastic dynamics of East African coral reefs

Coral reefs are dynamic systems whose composition is highly influenced by unpredictable biotic and abiotic factors. Understanding the spatial scale at which long-term predictions of reef composition can be made will be crucial for guiding conservation efforts. Using a 22-year time series of benthic composition data from 20 reefs on the Kenyan and Tanzanian coast, we studied the long-term behaviour of Bayesian vector autoregressive state-space models for reef dynamics, incorporating among-site variability. We estimate that if there were no among-site variability, the total long-term variability would be approximately one third of its current value. Thus among-site variability contributes more to long-term variability in reef composition than does temporal variability. Individual sites are more predictable than previously thought, and predictions based on current snapshots are informative about long-term properties. Our approach allowed us to identify a subset of possible climate refugia sites with high conservation value, where the long-term probability of coral cover <= 0.1 was very low. Analytical results show that this probability is most strongly influenced by among-site variability and by interactions among benthic components within sites. These findings suggest that conservation initiatives might be successful at the site scale as well as the regional scale.Comment: 97 pages, 49 figure

Bayesian hierarchical reconstruction of protein profiles including a digestion model

Introduction : Mass spectrometry approaches are very attractive to detect protein panels in a sensitive and high speed way. MS can be coupled to many proteomic separation techniques. However, controlling technological variability on these analytical chains is a critical point. Adequate information processing is mandatory for data analysis to take into account the complexity of the analysed mixture, to improve the measurement reliability and to make the technology user friendly. Therefore we develop a hierarchical parametric probabilistic model of the LC-MS analytical chain including the technological variability. We introduce a Bayesian reconstruction methodology to recover the protein biomarkers content in a robust way. We will focus on the digestion step since it brings a major contribution to technological variability. Method : In this communication, we introduce a hierarchical model of the LC-MS analytical chain. Such a chain is a cascade of molecular events depicted by a graph structure, each node being associated to a molecular state such as protein, peptide and ion and each branch to a molecular processing such as digestion, ionisation and LC-MS separation. This molecular graph defines a hierarchical mixture model. We extend the Bayesian statistical framework we have introduced previously [1] to this hierarchical description. As an example, we will consider the digestion step. We describe the digestion process on a pair of peptides within the targeted protein as a Bernoulli random process associated with a cleavage probability controlled by the digestion kinetic law.Comment: pr\'esentation orale; 59th American Society for Mass Spectrometry Conference, Dallas : France (2011

Evolutionary Fitness in Variable Environments

One essential ingredient of evolutionary theory is the concept of fitness as a measure for a species' success in its living conditions. Here, we quantify the effect of environmental fluctuations onto fitness by analytical calculations on a general evolutionary model and by studying corresponding individual-based microscopic models. We demonstrate that not only larger growth rates and viabilities, but also reduced sensitivity to environmental variability substantially increases the fitness. Even for neutral evolution, variability in the growth rates plays the crucial role of strongly reducing the expected fixation times. Thereby, environmental fluctuations constitute a mechanism to account for the effective population sizes inferred from genetic data that often are much smaller than the census population size.Comment: main: 5 pages, 4 figures; supplement: 7 pages, 7 figue

Stability and structure of analytical MHD jet formation models with a finite outer disk radius

(Abridged) Finite radius accretion disks are a strong candidate for launching astrophysical jets from their inner parts and disk-winds are considered as the basic component of such magnetically collimated outflows. The only available analytical MHD solutions for describing disk-driven jets are those characterized by the symmetry of radial self-similarity. Radially self-similar MHD models, in general, have two geometrical shortcomings, a singularity at the jet axis and the non-existence of an intrinsic radial scale, i.e. the jets formally extend to radial infinity. Hence, numerical simulations are necessary to extend the analytical solutions towards the axis and impose a physical boundary at finite radial distance. We focus here on studying the effects of imposing an outer radius of the underlying accreting disk (and thus also of the outflow) on the topology, structure and variability of a radially self-similar analytical MHD solution. The initial condition consists of a hybrid of an unchanged and a scaled-down analytical solution, one for the jet and the other for its environment. In all studied cases, we find at the end steady two-component solutions.Comment: 14 pages, 15 figures, accepted for publication in A &

A New Pathway for Communicating the 11-year Solar Cycle Signal to the QBO

[1] The response of the equatorial quasi-biennial oscillation (QBO) to zonal-mean ozone perturbations consistent with the 11-year solar cycle is examined using a 2 1/2 dimensional model of the tropical stratosphere. Unique to this model are wave-ozone feedbacks, which provide a new, nonlinear pathway for communicating solar variability effects to the QBO. Model simulations show that for zonal-mean ozone perturbations representative of solar maximum (minimum), the diabatic heating due to the wave-ozone feedbacks is primarily responsible for driving a slightly stronger (weaker) QBO circulation and producing a slightly shorter (longer) QBO period. These results, which are explained via an analytical analysis of the divergence of Eliassen-palm flux, are in general agreement with observations of quasi-decadal variability of the QBO

Environmental Noise Variability in Population Dynamics Matrix Models

The impact of environmental variability on population size growth rate in dynamic models is a recurrent issue in the theoretical ecology literature. In the scalar case, R. Lande pointed out that results are ambiguous depending on whether the noise is added at arithmetic or logarithmic scale, while the matrix case has been investigated by S. Tuljapurkar. Our contribution consists first in introducing another notion of variability than the widely used variance or coefficient of variation, namely the so-called convex orders. Second, in population dynamics matrix models, we focus on how matrix components depend functionaly on uncertain environmental factors. In the log-convex case, we show that, in a sense, environmental variability increases both mean population size and mean log-population size and makes them more variable. Our main result is that specific analytical dependence coupled with appropriate notion of variability lead to wide generic results, valid for all times and not only asymptotically, and requiring no assumptions of stationarity, of normality, of independency, etc. Though the approach is different, our conclusions are consistent with previous results in the literature. However, they make it clear that the analytical dependence on environmental factors cannot be overlooked when trying to tackle the influence of variability.Comment: 9 page

Obscuration model of Variability in AGN

There are strong suggestions that the disk-like accretion flow onto massive black hole in AGN is disrupted in its innermost part (10-100 Rg), possibly due to the radiation pressure instability. It may form a hot optically thin quasi spherical (ADAF) flow surrounded by or containing denser clouds due to the disruption of the disk. Such clouds might be optically thick, with a Thompson depth of order of 10 or more. Within the frame of this cloud scenario (Collin-Souffrin et al. 1996, Czerny & Dumont 1998), obscuration events are expected and the effect would be seen as a variability. We consider expected random variability due to statistical dispersion in location of clouds along the line of sight for a constant covering factor. We discuss a simple analytical toy model which provides us with the estimates of the mean spectral properties and variability amplitude of AGN, and we support them with radiative transfer computations done with the use of TITAN code of Dumont, Abrassart & Collin (1999) and NOAR code of Abrassart (1999).Comment: to appear in Proc. of 5th Compton Symposium on Gamma-Ray Astronomy and Astrophysic