- Cellular models of bacteria-antibiotics interaction
- Modelling immune-response to invasion. Viral dynamics
- Multi-agent modelling of epidemic outbreaks in large populations
See the list of collaborators of this group here !
Cellular models of bacteria-antibiotics interaction: a novel cellular model of methycillin-resistant S. Aureus (MRSA) for predicting antibiotic susceptibility
Postdoctoral researcher: Dr. James MURPHY
To view the last poster of James, please follow the link: James Murphy POSTER 08. This poster was presented at Genomes 2008 - Functional Genomics of Microorgansms conference, April 2008, Institut Pasteur, Paris, France.
Project description: The aim of this project is to model the growth and interactions of bacterial cells with antibiotics in culture using an agent-based modelling approach. Rather than taking a high level mathematical model of the system, individual bacteria cells, their properties and the pharmacokinetics of antibiotics are used to determine the model. This “bottom-up” approach allows a fine-grained analysis, connecting local changes at the cellular level to the overall patterns of population growth.
Each bacterial cell of a colony is represented by a software agent with variables storing basic information such as energy state, and rules governing behaviour and interactions with other agents. This approach means that spatial and temporal heterogeneity in a bacterial colony can be explicitly modelled, and the significance of this variability for overall population dynamics can be explored.
Initial testing shows that the model can accurately predict the Minimum Inhibitory Concentration (MIC) of an antibiotic, which is the key measure of antibiotic efficacy used in the clinical setting. Micro-Gen therefore represents a robust tool for facilitating the rapid screening of drug compounds against a wide array of bacterial strains in simulated culture conditions. The project is being developed in collaboration with RCSI Peptide Synthesis Laboratory.
Agent-based immune/HIV modelling
Postdoctoral researcher: Dr. Dimitri PERRIN
Project description: Immunity in the human body is obtained through emerging properties of a very complex system. It involves a multitude of cells and organs, with very specific functions and numerous possible interactions. This complexity often hinders understanding of the range and variety of immune responses. Large-scale effects are easily observed, and microscopic studies give a better insight into the sequence of interactions, but links between these two levels are difficult to establish, in particular if we are looking for a quantitative description.
The situation is pronounced with respect to HIV infection. Mechanisms by which an HIV virion infects an immune cell, and its genetic material is incorporated into the host chromosome, are known, as are processes leading to production and liberation of new virions. Macroscopic progression from initial HIV infection to AIDS onset are equally well described.
However, there are still millions of people living with HIV, and the process by which interactions between the immune system and HIV lead to such variability in individual experience of infection has yet to be fully described. Development of a vaccine is even further down the road, although recent therapeutic efforts have led to better control of disease progression.
To facilitate analysis of this complex system, numerous models have been developed since the 1990s. Early efforts suffered from a relative lack of biomedical data, and from limited computing resources then available. However, a number of these efforts and of subsequent developments were able to match some signatures of HIV, serving as a proof of concept and ensuring continued interest and ongoing efforts in the field of computational immunology.
Recent models are, of course, more refined, employing more sophisticated approaches and computer resources, and offering valuable insights into specific aspects of the system, despite their limitations.
A model is typically selective, and cannot include all parameters and interactions. The advent of large-scale, parallel computing, however, offers opportunity for increased refinement of biological models. In particular, a better understanding of the events behind emergence of individuality is of special interest.
Here, we focus on the cell-mediated response to HIV infection. Using a large-scale agent-based model, we realistically account for cell mobility, a key element of the immune system.
Recent research highlights another feature of the body-wise progression of the disease: there are localised effects, such as those within the gastro-intestinal tract, that require incorporation into computer-based models. The proposed approach, using lymph nodes as a key model element, permits inclusion of this layer of the system, and simulations involving hundreds of nodes and billions of nodes (through advanced parallelisation).
Multi-agent modelling of epidemic outbreaks in large populations
Postdoctoral researcher: Dr. Dimitri PERRIN, Intern Student: Benoit CLAUDE
Project description: Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. However, most of the information is available after the spread itself, and preemptive assessment, however crucial, is far from trivial.
The better understood the spread of a given disease, the more efficient the response can be. The main difficulty, however, is that most of the information on a single epidemic event only becomes available retrospectively. Predictive models are thus crucial tools in terms of estimating level and persistence of threat posed by a specific disease (or strain thereof).
Traditionally, the most advanced epidemic models have been relying on either network-based or agent-based approaches, but are not without limitations. Combining the agent-based and network-based approaches offers considerable potential for a risk and prevention assessment framework applicable to a wide range of diseases.
In the context of this study, since we deal with multiples types of social links, and at different levels, a purpose-built social network generation algorithm is required.
Similarly, detailed implementation of stylised urban areas is required for accurate geographical localisation of the agents and implementation of realistic mobility patterns.
A key outcome of this project will be a detailed and quantitative evaluation of intervention policies during disease outbreaks.