Assessing the impact of HIV treatment interruptions using stochastic cellular Automata.

Chronic HIV infection causes a progressive decrease in the ability to maintain homeostasis resulting, after some time, in eventual break down of immune functions. Recent clinical research has shed light on a significant contribution of the lymphatic tissues, where HIV causes accumulation of collagen, (fibrosis). Specifically, where tissue is populated by certain types of functional stromal cells designated Fibroblastic Reticular Cells (FRCs), these have been found to play a crucial role in balancing out apoptosis and regeneration of naïve T-cells through 2-way cellular signaling.

Computational Modeling and Analysis of Microarray Data: New Horizons.

High-throughput microarray technologies have long been a source of data for a wide range of biomedical investigations. Over the decades, variants have been developed and sophistication of measurements has improved, with generated data providing both valuable insight and considerable analytical challenge. The cost-effectiveness of microarrays, as well as their fundamental applicability, made them a first choice for much early genomic research and efforts to improve accessibility, quality and interpretation have continued unabated.

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data.

Theoretical cross-comparative analysis on dynamics of small intestine and colon crypts during cancer initiation.

Epigenetics is emerging as a fundamentally important area of biological and medical research that has implications for our understanding of human diseases including cancer, autoimmune and neuropsychiatric disorders. In the context of recent efforts on personalised medicine, a novel research direction is concerned with identification of intra-individual epigenetic variation linked to disease predisposition and development, i.e.

Comparative Correlation Structure of Colon Cancer Locus Specific Methylation: Characterisation of Patient Profiles and Potential Markers across 3 Array-Based Datasets.

Abnormal DNA-methylation is well known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Recent years have seen the increased use of large-scale technologies, (such as methylation microarray assays or specific sequencing of methylated DNA), to determine whole genome profiles of CpG island methylation in tissue samples. Comprehensive study of methylation array data from transcriptome high-throughput platforms permits determination of gene methylation markers, important for cancer profiling.

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications.

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples.

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate.

A computational model for genetic and epigenetic signals in colon cancer.

Cancer, a class of diseases, characterized by abnormal cell growth, has one of the highest overall death rates world-wide. Its development has been linked to aberrant genetic and epigenetic events, affecting the regulation of key genes that control cellular mechanisms. However, a major issue in cancer research is the lack of precise information on tumour pathways; therefore, the delineation of these and of the processes underlying disease proliferation is an important area of investigation.

RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter's disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified.

The landscape for epigenetic/epigenomic biomedical resources.

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges.

Colon cancer associated genes exhibit signatures of positive selection at functionally significant positions.

Background: Cancer, much like most human disease, is routinely studied by utilizing model organisms. Of these model organisms, mice are often dominant. However, our assumptions of functional equivalence fail to consider the opportunity for divergence conferred by ~180 Million Years (MY) of independent evolution between these species.

Integrating heterogeneous gene expression data for gene regulatory network modelling.

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes).

Computational micromodel for epigenetic mechanisms.

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation.

Cross-platform microarray data normalisation for regulatory network inference.

Background: Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

Model refinement through high-performance computing: an agent-based HIV example.

Background: Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model.

Comparison of microarray preprocessing methods.

Data preprocessing in microarray technology is a crucial initial step before data analysis is performed. Many preprocessing methods have been proposed but none has proved to be ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further experimentation while data are yet restricted.

Cell type-dependent, infection-induced, aberrant DNA methylation in gastric cancer.

Epigenetic changes correspond to heritable modifications of the chromatin structure, which do not involve any alteration of the DNA sequence but nonetheless affect gene expression. These mechanisms play an important role in cell differentiation, but aberrant occurrences are also associated with a number of diseases, including cancer and neural development disorders. In particular, aberrant DNA methylation induced by H.

Comparison of evolutionary algorithms in gene regulatory network model inference.

Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.

Seizure characterisation using frequency-dependent multivariate dynamics.

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies.

3D Multi-agent models for protein release from PLGA spherical particles with complex inner morphologies.

In order to better understand and predict the release of proteins from bioerodible microspheres or nanospheres, it is important to know the influences of different initial factors on the release mechanisms, though often it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte Carlo (MC) and cellular automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins.

Quantitative multi-agent models for simulating protein release from PLGA bioerodible nano- and microspheres.

Using poly(lactide-co-glycolide) (PLGA) particles for drug encapsulation and delivery has recently gained considerable popularity for a number of reasons. An advantage in one sense, but a drawback of PLGA use in another, is that drug delivery systems made of this material can provide a wide range of dissolution profiles, due to their internal structure and properties related to particles' manufacture. The advantages of enriching particulate drug design experimentation with computer models, are evident with simulations used to predict and optimize design, as well as indicate choice of best manufacturing parameters.

Diversity emergence and dynamics during primary immune response: a shape space, physical space model.

A computational model of the dynamics of diversity among T-cell receptors and MHC: peptide complex molecules is presented. We propose a method by which individual immune systems may evolve effcient or ineffcient states as a result of T-cell receptor crossreactivity as well as genetic variation among pathogens. By combining shape space and physical space models, valuable insight is obtained into how immune system-wide state is, in large part, determined by localised space dynamics.

Techniques for clustering gene expression data.

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile.

Optimisation and parallelisation strategies for Monte Carlo simulation of HIV infection.

In recent years, the study of immune response behaviour through mathematical and computational models has attracted considerable efforts. The dynamics of key cell types, and their interactions, has been a primary focus in terms of building a picture of how the immune system responds to a threat. Discrete methods, based on lattice Monte-Carlo (MC) models, with their flexibility and relative simplicity have previously been used to model the immune system behaviour.