8,068 research outputs found
A phenomenological approach to the simulation of metabolism and proliferation dynamics of large tumour cell populations
A major goal of modern computational biology is to simulate the collective
behaviour of large cell populations starting from the intricate web of
molecular interactions occurring at the microscopic level. In this paper we
describe a simplified model of cell metabolism, growth and proliferation,
suitable for inclusion in a multicell simulator, now under development
(Chignola R and Milotti E 2004 Physica A 338 261-6). Nutrients regulate the
proliferation dynamics of tumor cells which adapt their behaviour to respond to
changes in the biochemical composition of the environment. This modeling of
nutrient metabolism and cell cycle at a mesoscopic scale level leads to a
continuous flow of information between the two disparate spatiotemporal scales
of molecular and cellular dynamics that can be simulated with modern computers
and tested experimentally.Comment: 58 pages, 7 figures, 3 tables, pdf onl
Gold nanorods as molecular contrast agents in photoacoustic imaging: the promises and the caveats\ud
Rod-shaped gold nanoparticles exhibit intense and narrow absorption peaks for light in the far-red and near-infrared wavelength regions, owing to the excitation of longitudinal plasmons. Light absorption is followed predominantly by non radiative de-excitation, and the released heat and subsequent temperature rise cause strong photoacoustic (optoacoustic) signals to be produced. This feature combined with the relative inertness of gold, and its favorable surface chemistry, which permits affinity biomolecule coupling, has seen gold nanorods (AuNR) attracting much attention as contrast agents and molecular probes for photoacoustic imaging. In this article we provide an short overview of the current status of the use of AuNR in molecular imaging using photoacoustics. We further examine the state of the art in various chemical, physical and biochemical phenomena that have implications for the future photoacoustic applications of these particles. We cover the route through fine-tuning of AuNR synthetic procedures, toxicity reduction by appropriate coatings, in vitro cellular interactions of AuNRs, attachment of targeting antibodies, in vivo fate of the particles and the effects of certain light interactions with the AuN
Boosting for high-dimensional linear models
We prove that boosting with the squared error loss, Boosting, is
consistent for very high-dimensional linear models, where the number of
predictor variables is allowed to grow essentially as fast as (exp(sample
size)), assuming that the true underlying regression function is sparse in
terms of the -norm of the regression coefficients. In the language of
signal processing, this means consistency for de-noising using a strongly
overcomplete dictionary if the underlying signal is sparse in terms of the
-norm. We also propose here an -based method for tuning,
namely for choosing the number of boosting iterations. This makes Boosting
computationally attractive since it is not required to run the algorithm
multiple times for cross-validation as commonly used so far. We demonstrate
Boosting for simulated data, in particular where the predictor dimension
is large in comparison to sample size, and for a difficult tumor-classification
problem with gene expression microarray data.Comment: Published at http://dx.doi.org/10.1214/009053606000000092 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Hypoxic Cell Waves around Necrotic Cores in Glioblastoma: A Biomathematical Model and its Therapeutic Implications
Glioblastoma is a rapidly evolving high-grade astrocytoma that is
distinguished pathologically from lower grade gliomas by the presence of
necrosis and microvascular hiperplasia. Necrotic areas are typically surrounded
by hypercellular regions known as "pseudopalisades" originated by local tumor
vessel occlusions that induce collective cellular migration events. This leads
to the formation of waves of tumor cells actively migrating away from central
hypoxia. We present a mathematical model that incorporates the interplay among
two tumor cell phenotypes, a necrotic core and the oxygen distribution. Our
simulations reveal the formation of a traveling wave of tumor cells that
reproduces the observed histologic patterns of pseudopalisades. Additional
simulations of the model equations show that preventing the collapse of tumor
microvessels leads to slower glioma invasion, a fact that might be exploited
for therapeutic purposes.Comment: 29 pages, 9 figure
Flow-correlated dilution of a regular network leads to a percolating network during tumor induced angiogenesis
We study a simplified stochastic model for the vascularization of a growing
tumor, incorporating the formation of new blood vessels at the tumor periphery
as well as their regression in the tumor center. The resulting morphology of
the tumor vasculature differs drastically from the original one. We demonstrate
that the probabilistic vessel collapse has to be correlated with the blood
shear force in order to yield percolating network structures. The resulting
tumor vasculature displays fractal properties. Fractal dimension, microvascular
density (MVD), blood flow and shear force has been computed for a wide range of
parameters.Comment: 15 pages, 12 figure
Variable selection for the multicategory SVM via adaptive sup-norm regularization
The Support Vector Machine (SVM) is a popular classification paradigm in
machine learning and has achieved great success in real applications. However,
the standard SVM can not select variables automatically and therefore its
solution typically utilizes all the input variables without discrimination.
This makes it difficult to identify important predictor variables, which is
often one of the primary goals in data analysis. In this paper, we propose two
novel types of regularization in the context of the multicategory SVM (MSVM)
for simultaneous classification and variable selection. The MSVM generally
requires estimation of multiple discriminating functions and applies the argmax
rule for prediction. For each individual variable, we propose to characterize
its importance by the supnorm of its coefficient vector associated with
different functions, and then minimize the MSVM hinge loss function subject to
a penalty on the sum of supnorms. To further improve the supnorm penalty, we
propose the adaptive regularization, which allows different weights imposed on
different variables according to their relative importance. Both types of
regularization automate variable selection in the process of building
classifiers, and lead to sparse multi-classifiers with enhanced
interpretability and improved accuracy, especially for high dimensional low
sample size data. One big advantage of the supnorm penalty is its easy
implementation via standard linear programming. Several simulated examples and
one real gene data analysis demonstrate the outstanding performance of the
adaptive supnorm penalty in various data settings.Comment: Published in at http://dx.doi.org/10.1214/08-EJS122 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Multi-scale cellular engineering: From molecules to organ-on-a-chip.
Recent technological advances in cellular and molecular engineering have provided new insights into biology and enabled the design, manufacturing, and manipulation of complex living systems. Here, we summarize the state of advances at the molecular, cellular, and multi-cellular levels using experimental and computational tools. The areas of focus include intrinsically disordered proteins, synthetic proteins, spatiotemporally dynamic extracellular matrices, organ-on-a-chip approaches, and computational modeling, which all have tremendous potential for advancing fundamental and translational science. Perspectives on the current limitations and future directions are also described, with the goal of stimulating interest to overcome these hurdles using multi-disciplinary approaches
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