This area includes biorelated topics, with modelling systems at their microscopic level:

1. Modelling immune response to invasion/Viral Dynamics

2. Drug dissolution modelling, prediction and pharmacokinetics

3. Multi-agent Pathogenic Cellular Models of Bacteria and Antibiotic

4. Microarray Data Analysis

5. Bioinformatics and Molecular Evolution

6. Epigenetics Modelling and Biomedical Resources

1. Modelling immune response to invasion/Viral Dynamics

This project on modelling immune response to viral invasion, (specifically to HIV, retrovirus associated with Human Acquired Immuno-Deficiency Syndrome), seeks to explore the population dynamics for different cell types, based on what is understood or conjectured about cellular mechanisms. The initial focus described macroscopic latency in terms of the microscopic or cell base, quantifying the stages of helper T-cell decline, in order to identify crucial crossover points and thresholds for viral population explosion. Additionally, this project sought to improve biological reality by incorporating features from models which attempted detailed descriptions of all cell types involved in the viral invasion/ immune response reaction, (e.g. the Seiden and Celada model, developed 1992-2002). Intra - and inter-cellular interactions have been investigated in detail, to explore cell survival characteristics and to quantify the influence of additional cell types on disease progression. The viability of adapting some of these ideas to modelling features of other immuno-suppressive disorders has also been explored. Recent developments of the project have included building agent-based approaches to model individual variation, network communication and intervention potential. (Present/past collaborators include USM, U.S., NUIM, and ITT). A novel project Epigenetics, which will exploit modelling principles developed here (and will overlap the Bioinformatics work of the group) has just received national funding.

2. Drug dissolution modelling, prediction and pharmacokinetics

A computational pharmacokinetics approach was initially used to develop a package for cheap, reliable and fast prediction of drug absorption by the body from a localised source. The precise control of drug input to the body by different routes is now possible using a variety of sophisticated delivery systems. However, most drugs are still given as conventional oral dosage forms or simple injections, - methods of drug delivery that can be variable and unpredictable. Of particular interest in the design of effective drug delivery systems is the rate of drug release into the human body from the tablet or implant used to deliver the dosage. A more effective drug delivery system would release the drug more gradually over time, using materials, which maintain a constant surface area of the drug. The initial project investigated the role of computational pharmacokinetics for the development of more accurate drug dissolution models in vitro and in vivo and the use of these models in the simulation of drug transport. This research involved liaison with NICB research on anticancer drug assays and the Bioassays and Bioanalysis Core Facility.  More recent developments have included use of (i) Direct Monte Carlo techniques to explore complex internal morphology of compacts and non-predictable dissolution profiles, and (ii) Inverse Monte Carlo designed to exploit Bayesian principles in retrieving detailed dynamic distributional knowledge about a dissolving particulate system from limited data. Model extensions to a large-scale collaborative project, involving development of therapeutic implants with controlled drug release targeted to regeneration of severely damaged tissue, are currently underway with Hospital for Special Surgery, NY.

3. Multi-agent Pathogenic Cellular Models of Bacteria and Antibiotic

In recent years there has been a rapid growth in the understanding of the basic cellular processes of individual bacterial cells through advances in genomics and proteomics research. However, this has introduced a demand to understand how the interactions between the individual system components contribute to the overall population dynamics. A useful theoretical approach for relating information at the individual cellular/molecular level with emergent population characteristics is the agent-based modelling approach.

The agent-based modelling approach involves assigning predefined rules and parameters to each individual component (e.g. the bacterial cell) of the population. Therefore, the emergent behaviour of the population as a whole can be examined without the need for population-level laws. This allows the inherent heterogeneity of a population to be accounted for in the model. An agent-based model of bacterial population growth, called Micro-Gen, has been developed to provide a theoretical framework for investigating the interactions between antibiotics and bacterial cells in culture.

To read more about this work click here. 

See the list of the collaborators of this group here !

4. Microarray Data Analysis

The availability of time series mRNA expression data sets has spurred the race to infer Genetic Regulatory Networks (GRNs) to explain trend and causal relationships among genes measured in microarray experiments.  As a complement to such techniques, Dimension Reduction Techniques, borrowing from experience on financial time series can be used to add further insight to such complex datasets.  Finally, Novel Database Models can be developed to make use of the natural inter-relationships between data arising from microarray experiments carried out according to, for example, the MIAME standard. Currently, the group has a mainstream project in clustering, (specifically bi-clustering techniques) for gene expression data and is extending previous work on database standards. Liaison with colleagues in the NICB is also a feature of this work.

To read more about current projects in Microarray Data Analysis, click here.

5. Bioinformatics and Molecular Evolution

The focus of this group is (i) to understand evolutionary processes in relation to human health issues (ii) to model host and pathogen protein evolution, and (iii), to resolve contentious genetic and evolutionary debates on organism/tissue/system origin and evolution. Each of these projects has huge requirements in terms of computational loads. Our analyses have been running on ICHEC and on a Bioinformatic HPC at NUIM in conjunction with our collaborators there. For example, our Paleobiochemistry analyses, accepted for publication this week in BMC Evolutionary Biology, required in total approx. 12 weeks of computation on a shared 64 node cluster along with approx. 6 weeks of computation on ICHEC. These figures are high due to the computational tasks – optimising models of evolution and phylogeny reconstruction using heterogeneous likelihood - and also because the number of other users running processes at the same time varies. Our computational requirements are not consistently this high but go through phases of high and low/very low requirement depending on the stage of the project. We are currently embarking on a new - and in terms of the projected outcomes for human health – a very exciting project, therefore our requirements for computational power will increase in the future. The School of Computing’s HPC offers us the opportunity to see these projects through to completion sooner but importantly it also opens up many more opportunities for future projects, collaborations and extensions to current projects which have not been possible to date due to computational limitations.

See current projects here !

See the list of the collaborators of this group here !

6. Epigenetics Modelling and Biomedical Resources

Epigenetic mechanisms involve heritable alterations in chromatin structure, in turn regulating gene expression, but not involving changes in DNA sequence. While information within the genetic material, is not changed, instructions for its assembly and interpretation may be. Dealing with this meta-genetic information is the role of regulation. There are a number of important mechanisms of epigenetic inheritance, but probably the best understood example of stable epigenetic phenomena is DNA methylation, with changes closely related to progress of disease.

Alterations in DNA methylation, imprinting and chromatin are common in cancer and links to epigenetic changes have been established in several cases, e.g. in Wilm's tumour and colon cancer,, both of which involve Loss of Heterozygocity, (LOH), (silencing specific genes on a parent lineage). In the latter, this also appears to predate the tumours, with LOH in surrounding tissue - i.e. an environmental or "field effect."

Objectives of our work are to understand and construct computational representations of epigenetic events in the dynamics of the progression from normal to abnormal cells. This involves a series of interconnecting projects, looking, respectively at: epigenetic data generation and storage, mechanisms of DNA methylation, together with prevalence of alternative epigenetic signatures and the building of simple phenomenological models of early stage changes.

Read more on our current work on epigenetics here !

You can begin a fascinating journey in epigenetics reading the book Epigenetics edited by C. D. Allis, T. Jenuwein, D. Reinberg and M.-L. Caparros.

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