14,207 research outputs found
An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models
We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.evolutionary strategy, genetic algorithm, TVECM
Recommended from our members
Chapter 9 Gene Drive Strategies for Population Replacement
Gene drive systems are selfish genetic elements capable of spreading into a population despite a fitness cost. A variety of these systems have been proposed for spreading disease-refractory genes into mosquito populations, thus reducing their ability to transmit diseases such as malaria and dengue fever to humans. Some have also been proposed for suppressing mosquito populations. We assess the alignment of these systems with design criteria for their safety and efficacy. Systems such as homing endonuclease genes, which manipulate inheritance through DNA cleavage and repair, are highly invasive and well-suited to population suppression efforts. Systems such as Medea, which use combinations of toxins and antidotes to favor their own inheritance, are highly stable and suitable for replacing mosquito populations with disease-refractory varieties. These systems offer much promise for future vector-borne disease control
Directional genetic differentiation and asymmetric migration
Understanding the population structure and patterns of gene flow within
species is of fundamental importance to the study of evolution. In the fields
of population and evolutionary genetics, measures of genetic differentiation
are commonly used to gather this information. One potential caveat is that
these measures assume gene flow to be symmetric. However, asymmetric gene flow
is common in nature, especially in systems driven by physical processes such as
wind or water currents. Since information about levels of asymmetric gene flow
among populations is essential for the correct interpretation of the
distribution of contemporary genetic diversity within species, this should not
be overlooked. To obtain information on asymmetric migration patterns from
genetic data, complex models based on maximum likelihood or Bayesian approaches
generally need to be employed, often at great computational cost. Here, a new
simpler and more efficient approach for understanding gene flow patterns is
presented. This approach allows the estimation of directional components of
genetic divergence between pairs of populations at low computational effort,
using any of the classical or modern measures of genetic differentiation. These
directional measures of genetic differentiation can further be used to
calculate directional relative migration and to detect asymmetries in gene flow
patterns. This can be done in a user-friendly web application called
divMigrate-online introduced in this paper. Using simulated data sets with
known gene flow regimes, we demonstrate that the method is capable of resolving
complex migration patterns under a range of study designs.Comment: 25 pages, 8 (+3) figures, 1 tabl
An integrative computational model for intestinal tissue renewal
Objectives\ud
\ud
The luminal surface of the gut is lined with a monolayer of epithelial cells that acts as a nutrient absorptive engine and protective barrier. To maintain its integrity and functionality, the epithelium is renewed every few days. Theoretical models are powerful tools that can be used to test hypotheses concerning the regulation of this renewal process, to investigate how its dysfunction can lead to loss of homeostasis and neoplasia, and to identify potential therapeutic interventions. Here we propose a new multiscale model for crypt dynamics that links phenomena occurring at the subcellular, cellular and tissue levels of organisation.\ud
\ud
Methods\ud
\ud
At the subcellular level, deterministic models characterise molecular networks, such as cell-cycle control and Wnt signalling. The output of these models determines the behaviour of each epithelial cell in response to intra-, inter- and extracellular cues. The modular nature of the model enables us to easily modify individual assumptions and analyse their effects on the system as a whole.\ud
\ud
Results\ud
\ud
We perform virtual microdissection and labelling-index experiments, evaluate the impact of various model extensions, obtain new insight into clonal expansion in the crypt, and compare our predictions with recent mitochondrial DNA mutation data. \ud
\ud
Conclusions\ud
\ud
We demonstrate that relaxing the assumption that stem-cell positions are fixed enables clonal expansion and niche succession to occur. We also predict that the presence of extracellular factors near the base of the crypt alone suffices to explain the observed spatial variation in nuclear beta-catenin levels along the crypt axis
Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics
The Random Forest (RF) algorithm by Leo Breiman has become a
standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research
A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
This paper addresses the problem of Monte Carlo approximation of posterior
probability distributions. In particular, we have considered a recently
proposed technique known as population Monte Carlo (PMC), which is based on an
iterative importance sampling approach. An important drawback of this
methodology is the degeneracy of the importance weights when the dimension of
either the observations or the variables of interest is high. To alleviate this
difficulty, we propose a novel method that performs a nonlinear transformation
on the importance weights. This operation reduces the weight variation, hence
it avoids their degeneracy and increases the efficiency of the importance
sampling scheme, specially when drawing from a proposal functions which are
poorly adapted to the true posterior.
For the sake of illustration, we have applied the proposed algorithm to the
estimation of the parameters of a Gaussian mixture model. This is a very simple
problem that enables us to clearly show and discuss the main features of the
proposed technique. As a practical application, we have also considered the
popular (and challenging) problem of estimating the rate parameters of
stochastic kinetic models (SKM). SKMs are highly multivariate systems that
model molecular interactions in biological and chemical problems. We introduce
a particularization of the proposed algorithm to SKMs and present numerical
results.Comment: 35 pages, 8 figure
Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation
Two-locus sampling probabilities have played a central role in devising an
efficient composite likelihood method for estimating fine-scale recombination
rates. Due to mathematical and computational challenges, these sampling
probabilities are typically computed under the unrealistic assumption of a
constant population size, and simulation studies have shown that resulting
recombination rate estimates can be severely biased in certain cases of
historical population size changes. To alleviate this problem, we develop here
new methods to compute the sampling probability for variable population size
functions that are piecewise constant. Our main theoretical result, implemented
in a new software package called LDpop, is a novel formula for the sampling
probability that can be evaluated by numerically exponentiating a large but
sparse matrix. This formula can handle moderate sample sizes () and
demographic size histories with a large number of epochs (). In addition, LDpop implements an approximate formula for the sampling
probability that is reasonably accurate and scales to hundreds in sample size
(). Finally, LDpop includes an importance sampler for the posterior
distribution of two-locus genealogies, based on a new result for the optimal
proposal distribution in the variable-size setting. Using our methods, we study
how a sharp population bottleneck followed by rapid growth affects the
correlation between partially linked sites. Then, through an extensive
simulation study, we show that accounting for population size changes under
such a demographic model leads to substantial improvements in fine-scale
recombination rate estimation. LDpop is freely available for download at
https://github.com/popgenmethods/ldpopComment: 32 pages, 13 figure
- …