This work demonstrates the application of probabilistic and semi-analytical methods to in vitro drug dissolution for a wider variety of drug delivery devices and conditions.
Focus now is to extend the model to the design of Therapeutic Implants in collaboration with Dr. Suzanne Maher, (HSS). Our role is to simulate dissolution and cellular ingress in the implant, with resulting changes in implant mechanical properties, offering possibilities for micro level targeted treatment.
With the introduction of Bayesian Inference, direct and inverse Monte Carlo and other probabilistic numerical methods, we believe that the results show enormous potential for the simulation of in vivo targeted drug delivery simulation, a ‘holy grail’ of drug development and something that would be of potentially huge potential to the Pharmaceutical Industry.
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The aim of this project on modelling immune response to viral invasion, specifically Human Acquired Immuno-Deficiency Syndrome (associated with HIV infection), is to explore the population dynamics for different cell types, based on what is understood or conjectured about cellular mechanisms.
Intra- and inter-cellular interactions are 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 is also the subject of exploration. At present there are three project strands in Immune Response Modelling: Individual Variation in HIV, Mathematical Models in HIV, and Cell-level Models of HIV.
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- World-Wide-Mind project (web)
No easy system exists whereby a working mind can be made from the components of two or more laboratories.
This system aims to change that, and accelerate the growth of Artificial Intelligence, once the requirement that a single laboratory understand the entire system is removed.
Improved AI systems can be constucted as a hierarchy of individual systems working together at multiple levels.
This work includes collaborations with Mark Humphries (DCU), Ciaran O’Leary (DIT), Dave O’ Connor (SUN).
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The long-term objective of this project is to develop a model, realised in network form and for which metrics can be obtained, to permit exploration of mechanisms of cancer initiation and progression by epigenetic events. A key target is a plausible phenomenological model of very early epigenetic changes, corresponding to early stages of disease.
For the project, which follows on from a number of successful taught Masters practicum projects, DCU provides a local source of data, (through affiliation to NICB), and maintains contacts with AECOM's highly-active Bioinformatics unit.
This study could, we believe, in the long term provide a basis for assessment of very early epigenetic changes.
The project is the subject of a collaboration with our colleague, Dr. Padraig Doolan, NICB, DCU, on "Application of Novel Database and Dimension Reduction Techniques to Microarray Data". It involves the application of novel database and dimension reduction techniques to Microarray data from Cancerous cells.
In the past year the work in the group on Time Series Analysis has been expanded to apply sophisticated data analysis techniques (e.g. wavelets, detrended fluctuation analysis) to EEG datasets for the diagnosis of epilepsy and sleep apnea. Although the project is at an early stage, the group believe it to hold great promise, as little work has been done in the area of applying statistical physics methods to such time series, (nationally or internationally).
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