60 research outputs found
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
An agent-based approach to immune modelling
This study focuses on trying to understand why the range
of experience with respect to HIV infection is so diverse, especially as regards to the latency period. The challenge is to determine what assumptions can be made about the nature of the experience of antigenic invasion and diversity that can be modelled, tested and argued plausibly.
To investigate this, an agent-based approach is used to extract high-level behaviour which cannot be described analytically from the set of interaction rules at the cellular level. A prototype model encompasses local variation in baseline properties contributing to the individual disease experience and is included in a network which mimics the chain of lymphatic nodes. Dealing with massively multi-agent systems requires major computational efforts. However, parallelisation methods are a natural
consequence and advantage of the multi-agent approach. These are implemented using the MPI library
Predicting the outcomes of HIV treatment interruptions using computational modelling
In the past 30 years, HIV infection made a transition from fatal to chronic disease due to the emergence of potent treatment largely suppressing viral replication. However, this medication must be administered life-long on a
regular basis to maintain viral suppression and is not always well tolerated. Any interruption of treatment causes residual virus to be reactivated and infection to progress, where the underlying processes occurring and
consequences for the immune system are still poorly understood. Nonetheless, treatment interruptions are common due to adherence issues or limited access to antiretroviral drugs. Early clinical studies, aiming at
application of treatment interruptions in a structured way, gave contradictory results concerning patient safety, discouraging further trials. In-silico models potentially add to knowledge but a review of the Literature indicates most
current models used for studying treatment interruptions (equation-based), neglect recent clinical findings of collagen formation in lymphatic tissue due to HIV and its crucial role in immune system stability and efficacy. The aim
of this research is the construction and application of so-called ‘Bottom-Up’ models to allow improved assessment of these processes in relation to HIV treatment interruptions. In this regard, a novel computational model based on
2D Cellular Automata for lymphatic tissue depletion and associated damage to the immune system was developed. Hence, (i) using this model, the influence of spatial distribution of collagen formation on HIV infection
progression speed was evaluated while discussing aspects of computational performance. Further, (ii) direct Monte Carlo simulations were employed to explore the accumulation of tissue impairment due to repeated treatment interruptions and consequences for long-term prognosis. Finally, (iii) an inverse Monte Carlo approach was used to reconstruct yet unknown characteristics of patient groups. This is based on sparse data from past
clinical studies on treatment interruptions with the aim of explaining their contradictory results
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
Choices and trade-offs in inference with infectious disease models.
Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi
Stochastic computational modelling of complex drug delivery systems
As modern drug formulations become more advanced, pharmaceutical companies face the need for adequate tools to permit them to model complex requirements and to reduce
unnecessary adsorption rates while increasing the dosage administered. The aim of the research presented here is the development and application of a general stochastic framework with agent-based elements for building drug dissolution models, with a particular focus on
controlled release systems. The utilisation of three dimensional Cellular Automata and Monte Carlo methods, to describe structural compositions and the main physico-chemical mechanisms, is shown to have several key advantages: (i) the bottom up approach simplifies
the definition of complex interactions between underlying phenomena such as diffusion,polymer degradation and hydration, and the dissolution media; (ii) permits straightforward extensibility for drug formulation variations in terms of supporting various geometries
and exploring effects of polymer composition and layering; (iii) facilitates visualisation, affording insight on system structural evolution over time by capturing successive stages of dissolution. The framework has been used to build models simulating several distinct
release scenarios from coated spheres covering single coated erosion and swelling dominated spheres as well as the influence of multiple heterogeneous coatings. High-performance computational optimisation enables precision simulations of the very thin coatings used and allows fast realisation of model state changes. Furthermore, theoretical analysis of the comparative impact of synchronous and asynchronous Cellular Automata and the suitability of their application to pharmaceutical systems is performed. Likely parameter distributions from noisy in vitro data are reconstructed using Inverse Monte Carlo methods and outcomes are reported
Control and surveillance of partially observed stochastic epidemics in a Bayesian framework
This thesis comprises a number of inter-related parts. For most of the thesis we are
concerned with developing a new statistical technique that can enable the identi cation
of the optimal control by comparing competing control strategies for stochastic
epidemic models in real time. In the second part, we develop a novel approach for
modelling the spread of Peste des Petits Ruminants (PPR) virus within a given country
and the risk of introduction to other countries.
The control of highly infectious diseases of agriculture crops, animal and human
diseases is considered as one of the key challenges in epidemiological and ecological
modelling. Previous methods for analysis of epidemics, in which different controls
are compared, do not make full use of the trajectory of the epidemic. Most methods
use the information provided by the model parameters which may consider partial
information on the epidemic trajectory, so for example the same control strategy
may lead to different outcomes when the experiment is repeated. Also, by using
partial information it is observed that it might need more simulated realisations when
comparing two different controls. We introduce a statistical technique that makes full
use of the available information in estimating the effect of competing control strategies
on real-time epidemic outbreaks. The key to this approach lies in identifying a suitable
mechanism to couple epidemics, which could be unaffected by controls. To that end,
we use the Sellke construction as a latent process to link epidemics with different
control strategies.
The method is initially applied on non-spatial processes including SIR and SIS
models assuming that there are no observation data available before moving on to
more complex models that explicitly represent the spatial nature of the epidemic
spread. In the latter case, the analysis is conditioned on some observed data and
inference on the model parameters is performed in Bayesian framework using the
Markov Chain Monte Carlo (MCMC) techniques coupled with the data augmentation
methods. The methodology is applied on various simulated data sets and to citrus
canker data from Florida. Results suggest that the approach leads to highly positively
correlated outcomes of different controls, thus reducing the variability between the
effect of different control strategies, hence providing a more efficient estimator of their
expected differences. Therefore, a reduction of the number of realisations required to compare competing strategies in term of their expected outcomes is obtained.
The main purpose of the final part of this thesis is to develop a novel approach
to modelling the speed of Pest des Petits Ruminants (PPR) within a given country
and to understand the risk of subsequent spread to other countries. We are interested
in constructing models that can be fitted using information on the occurrence
of outbreaks as the information on the susceptible population is not available, and use
these models to estimate the speed of spatial spread of the virus. However, there was
little prior modelling on which the models developed here could be built. We start
by first establishing a spatio-temporal stochastic formulation for the spread of PPR.
This modelling is then used to estimate spatial transmission and speed of spread. To
account for uncertainty on the lack of information on the susceptible population, we
apply ideas from Bayesian modelling and data augmentation by treating the transmission
network as a missing quantity. Lastly, we establish a network model to address
questions regarding the risk of spread in the large-scale network of countries and
introduce the notion of ` first-passage time' using techniques from graph theory and
operational research such as the Bellman-Ford algorithm. The methodology is first
applied to PPR data from Tunisia and on simulated data. We also use simulated
models to investigate the dynamics of spread through a network of countries
Bayesian inference for indirectly observed stochastic processes, applications to epidemic modelling
Stochastic processes are mathematical objects that offer a probabilistic representation of
how some quantities evolve in time. In this thesis we focus on estimating the trajectory and
parameters of dynamical systems in cases where only indirect observations of the driving
stochastic process are available. We have first explored means to use weekly recorded
numbers of cases of Influenza to capture how the frequency and nature of contacts made
with infected individuals evolved in time. The latter was modelled with diffusions and
can be used to quantify the impact of varying drivers of epidemics as holidays, climate,
or prevention interventions. Following this idea, we have estimated how the frequency of
condom use has evolved during the intervention of the Gates Foundation against HIV in
India. In this setting, the available estimates of the proportion of individuals infected with
HIV were not only indirect but also very scarce observations, leading to specific difficulties. At last, we developed a methodology for fractional Brownian motions (fBM), here a
fractional stochastic volatility model, indirectly observed through market prices.
The intractability of the likelihood function, requiring augmentation of the parameter
space with the diffusion path, is ubiquitous in this thesis. We aimed for inference methods
robust to refinements in time discretisations, made necessary to enforce accuracy of Euler
schemes. The particle Marginal Metropolis Hastings (PMMH) algorithm exhibits this mesh
free property. We propose the use of fast approximate filters as a pre-exploration tool to
estimate the shape of the target density, for a quicker and more robust adaptation phase
of the asymptotically exact algorithm. The fBM problem could not be treated with the
PMMH, which required an alternative methodology based on reparameterisation and advanced Hamiltonian Monte Carlo techniques on the diffusion pathspace, that would also
be applicable in the Markovian setting
Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity
Biomolecular simulation is increasingly central to understanding and designing biological molecules and their interactions. Detailed, physics‐based simulation methods are demonstrating rapidly growing impact in areas as diverse as biocatalysis, drug delivery, biomaterials, biotechnology, and drug design. Simulations offer the potential of uniquely detailed, atomic‐level insight into mechanisms, dynamics, and processes, as well as increasingly accurate predictions of molecular properties. Simulations can now be used as computational assays of biological activity, for example, in predictions of drug resistance. Methodological and algorithmic developments, combined with advances in computational hardware, are transforming the scope and range of calculations. Different types of methods are required for different types of problem. Accurate methods and extensive simulations promise quantitative comparison with experiments across biochemistry. Atomistic simulations can now access experimentally relevant timescales for large systems, leading to a fertile interplay of experiment and theory and offering unprecedented opportunities for validating and developing models. Coarse‐grained methods allow studies on larger length‐ and timescales, and theoretical developments are bringing electronic structure calculations into new regimes. Multiscale methods are another key focus for development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology.
This article is categorized under:
Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods
Structure and Mechanism > Computational Biochemistry and Biophysics
Molecular and Statistical Mechanics > Free Energy Method
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