867 research outputs found
Agent-based modelling of viral infection.
The three phases of the macroscopic evolution of the HIV infection are well-known, but it is still difficult to understand how the cellular-level interactions come together to create this characteristic pattern and in particular why there are such differences in individual responses. An āagent-basedā approach is chosen, as a means of inferring high-level behaviour from a small set of interaction rules at the cellular level. Here the emphasis is put on the cell mobility and the viral mutations
Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression
Cite as: Perrin, Dimitri (2008) Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression. PhD thesis, Dublin City University
HIV modelling - parallel implementation strategies
We report on the development of a model to understand why the range of experience with respect to HIV infection is so diverse, especially with respect to the latency period.
To investigate this, an agent-based approach is used to extract highlevel behaviour which cannot be described analytically from the set of interaction rules at the cellular level. A network of independent matrices mimics the chain of lymph nodes. Dealing with massively multi-agent systems requires major computational effort. However, parallelisation methods are a natural consequence and advantage of the multi-agent approach and, using the MPI library, are here implemented, tested and optimized. Our current focus is on the various implementations of the data transfer across the network. Three communications strategies are proposed and tested, showing that the most efficient approach is communication based on the natural lymph-network connectivity
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. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Inference on modern Bayesian Neural Networks (BNNs) often relies on a
variational inference treatment, imposing violated assumptions of independence
and the form of the posterior. Traditional MCMC approaches avoid these
assumptions at the cost of increased computation due to its incompatibility to
subsampling of the likelihood. New Piecewise Deterministic Markov Process
(PDMP) samplers permit subsampling, though introduce a model specific
inhomogenous Poisson Process (IPPs) which is difficult to sample from. This
work introduces a new generic and adaptive thinning scheme for sampling from
these IPPs, and demonstrates how this approach can accelerate the application
of PDMPs for inference in BNNs. Experimentation illustrates how inference with
these methods is computationally feasible, can improve predictive accuracy,
MCMC mixing performance, and provide informative uncertainty measurements when
compared against other approximate inference schemes.Comment: Includes correction to software and corrigendum not
A Multiple Decoder CNN for Inverse Consistent 3D Image Registration
The recent application of deep learning technologies in medical image
registration has exponentially decreased the registration time and gradually
increased registration accuracy when compared to their traditional
counterparts. Most of the learning-based registration approaches considers this
task as a one directional problem. As a result, only correspondence from the
moving image to the target image is considered. However, in some medical
procedures bidirectional registration is required to be performed. Unlike other
learning-based registration, we propose a registration framework with inverse
consistency. The proposed method simultaneously learns forward transformation
and backward transformation in an unsupervised manner. We perform training and
testing of the method on the publicly available LPBA40 MRI dataset and
demonstrate strong performance than baseline registration methods
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