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| RESEARCH
> BioScience Computing
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PLEASE NOTE: The BioScience Computing Interest Group is no longer active
and this website will be removed at some unspecified time in the future without further warning.
The BioScience Computing Interest Group brings
together researchers from different areas of expertise and from
different institutions, primarily in London. The focus of our interest is inter-disciplinary
collaborative research in the following two domains:
- Computational Biology (Systems Biology) - the application of computer science
techniques to improve our knowledge and understanding of complete
biological systems. The "Systems Biology"
journal describes Systems Biology as involving "modelling
and simulating the complex dynamic interactions between genes,
transcripts, proteins, metabolites and cells using integrated
systems-based approaches. Encompassing proteomics,
transcriptomics, metabolomics and functional genomics, systems
biology uses computational and mathematical models to analyse
and simulate networks, pathways and the spatial and temporal
relationships that give rise to cause and effect in living
systems." Examples of the complete systems of
interest are:
- a cell (e.g. a leukocyte)
- a single-celled organism (e.g. a diatom)
- a simple multi-celled organism
- an organ (e.g. the human liver)
- a complex organism
- an ecosystem
- Computational Medicine ("Systems Medicine") - the application of computer science
techniques (often the results of Systems Biology research) to improve our knowledge and understanding of complete systems
relating to disease, injury, treatment and public health.
For example:
- malaria parasite motility and erythrocyte penetration
- cancer tumour morphology - tumour dynamics and
angiogenesis
- pharmacokinetics of tumour chemotherapy
- understanding of disease mechanisms
- pharmaceutical drug discovery
- drug target validation
- drug delivery
- predictive modeling of pharmacokinetics and
pharmacodynamics
- predictive modeling of disease and treatment
- automated clinical diagnosis
- modeling expected patient response to therapy
- automated remote monitoring of actual patient response
- modeling and monitoring public health
Our work
exploits and enhances a range of computing
techniques inspired both for and by the above topics in the
BioMedical Sciences. Particular research interests for BioScience
Computing are:
- Predictive modeling and simulation - especially the
modeling and simulation of self-organising adaptive response and the
modeling and simulation of systems where spatial and proximal
information is of paramount importance.
- Artificial Life ("ALife") - the study
of life as an organisational principle.
Relevant computing techniques are drawn
from all five of the current research groups in the Department of
Computer Science:
- intelligent systems (classification, learning, natural
computation, data fusion, data mining)
- vision, imaging and virtual environments (image segmentation,
visualisation)
- software engineering (data warehousing and curation,
ontologies, health informatics, grid computing)
- bioinformatics (classical modeling, protein modeling, genome
analysis)
- networks (telemedicine, grid computing)
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