How neurons migrate: a dynamic in-silico model of neuronal migration in the developing cortex.

BACKGROUND: Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process. RESULTS: The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration. CONCLUSIONS: We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.

Independent component analysis of microarray data in the study of endometrial cancer.

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.

The analysis of stochastic EMC problems using the concept of diffuse field reciprocity

There has been much progress in recent years in the analysis of complex random vibro-acoustic systems, and general analysis methods have been developed which are based on the properties of diffuse wave fields. It is shown in the present paper that such methods can also be applied to high frequency EMC problems, avoiding the need for costly full wave solutions to Maxwell's equations in complex cavities. The theory behind the approach is outlined and then applied to the relatively simple case of a wiring system which is subject to reverberant electromagnetic wave excitation. © 2011 IEEE.

Pressure data analysis of formula one racing engines

The measured time-history of the cylinder pressure is the principal diagnostic in the analysis of processes within the combustion chamber. This paper defines, implements and tests a pressure analysis algorithm for a Formula One racing engine in MATLAB1. Evaluation of the software on real data is presented. The sensitivity of the model to the variability of burn parameter estimates is also discussed. Copyright © 1997 Society of Automotive Engineers, Inc.

Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis.

Cytosine methylation is important for transposon silencing and epigenetic regulation of endogenous genes, although the extent to which this DNA modification functions to regulate the genome is still unknown. Here we report the first comprehensive DNA methylation map of an entire genome, at 35 base pair resolution, using the flowering plant Arabidopsis thaliana as a model. We find that pericentromeric heterochromatin, repetitive sequences, and regions producing small interfering RNAs are heavily methylated. Unexpectedly, over one-third of expressed genes contain methylation within transcribed regions, whereas only approximately 5% of genes show methylation within promoter regions. Interestingly, genes methylated in transcribed regions are highly expressed and constitutively active, whereas promoter-methylated genes show a greater degree of tissue-specific expression. Whole-genome tiling-array transcriptional profiling of DNA methyltransferase null mutants identified hundreds of genes and intergenic noncoding RNAs with altered expression levels, many of which may be epigenetically controlled by DNA methylation.