17,245 research outputs found
Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect
Aggregates of misfolded proteins are a hallmark of many age-related diseases.
Recently, they have been linked to aging of Escherichia coli (E. coli) where
protein aggregates accumulate at the old pole region of the aging bacterium.
Because of the potential of E. coli as a model organism, elucidating aging and
protein aggregation in this bacterium may pave the way to significant advances
in our global understanding of aging. A first obstacle along this path is to
decipher the mechanisms by which protein aggregates are targeted to specific
intercellular locations. Here, using an integrated approach based on
individual-based modeling, time-lapse fluorescence microscopy and automated
image analysis, we show that the movement of aging-related protein aggregates
in E. coli is purely diffusive (Brownian). Using single-particle tracking of
protein aggregates in live E. coli cells, we estimated the average size and
diffusion constant of the aggregates. Our results evidence that the aggregates
passively diffuse within the cell, with diffusion constants that depend on
their size in agreement with the Stokes-Einstein law. However, the aggregate
displacements along the cell long axis are confined to a region that roughly
corresponds to the nucleoid-free space in the cell pole, thus confirming the
importance of increased macromolecular crowding in the nucleoids. We thus used
3d individual-based modeling to show that these three ingredients (diffusion,
aggregation and diffusion hindrance in the nucleoids) are sufficient and
necessary to reproduce the available experimental data on aggregate
localization in the cells. Taken together, our results strongly support the
hypothesis that the localization of aging-related protein aggregates in the
poles of E. coli results from the coupling of passive diffusion- aggregation
with spatially non-homogeneous macromolecular crowding. They further support
the importance of "soft" intracellular structuring (based on macromolecular
crowding) in diffusion-based protein localization in E. coli.Comment: PLoS Computational Biology (2013
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
Numerical Simulation of Nonoptimal Dynamic Equilibrium Models
In this paper we present a recursive method for the computation of dynamic competitive equilibria in models with heterogeneous agents and market frictions. This method is based upon a convergent operator over an expanded set of state variables. The fixed point of this operator defines the set of all Markovian equilibria. We study approximation properties of the operator as well as the convergence of the moments of simulated sample paths. We apply our numerical algorithm to two growth models, an overlapping generations economy with money, and an asset pricing model with financial frictions.Heterogeneous agents, taxes, externalities, financial frictions, competitive equilibrium, computation, simulation
Kernel-based aggregation of marker-level genetic association tests involving copy-number variation
Genetic association tests involving copy-number variants (CNVs) are
complicated by the fact that CNVs span multiple markers at which measurements
are taken. The power of an association test at a single marker is typically
low, and it is desirable to pool information across the markers spanned by the
CNV. However, CNV boundaries are not known in advance, and the best way to
proceed with this pooling is unclear. In this article, we propose a
kernel-based method for aggregation of marker-level tests and explore several
aspects of its implementation. In addition, we explore some of the theoretical
aspects of marker-level test aggregation, proposing a permutation-based
approach that preserves the family-wise error rate of the testing procedure,
while demonstrating that several simpler alternatives fail to do so. The
empirical power of the approach is studied in a number of simulations
constructed from real data involving a pharmacogenomic study of gemcitabine,
and compares favorably with several competing approaches
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