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Bone and collagenous soft tissues have, primarily, a mechanical function. I am interested in how the molecular composition and structure of each tissue give rise to the necessary properties, and in the regulation of these tissues by the cells they contain. This regulation fails in diseases such as osteoarthritis or osteoporosis, and sometimes following injury, with consequences for the integrity and repair of these tissues. We cover a broad spectrum of approaches that include: the application of mechanical stimuli (or insults!) to tissues and cells in vitro; the measurement of cellular responses using molecular biological and biochemical methods; the measurement of material and mechanical properties using diffraction and spectroscopic techniques, microscopy and mechanical testing. We are also modelling various aspects of these systems using finite element, statistical and theoretical modelling, which has enabled us to identify key factors regulating material properties and identify features related to clinical outcomes such as fracture.
The musculoskeletal system is being investigated on a variety of levels from the molecular to the mechanical in order to determine how molecular composition and organisation is regulated by the cells to yield mechanical properties matching the changing demands being placed on the tissues and joints. Biological, Physical and Engineering inputs are integrated to enable models to be built to predict the mechanical behaviour of tissues, such as bone and cartilage, and joints such as the hip and the spine.
Please see Department of Orthopaedics and Bone & Musculoskeletal Research Programme websites for more details.
Our research in microbial physiology is based on ionic homeostasis in bacterial cells. We have a long-standing interest in establishing the quantitative basis of the physiological phenomena that we study and this falls directly with the Systems Biology remit. The two principal areas that we are developing using Systems approaches are: (a) metabolite detoxification and its links to channel activation and bacterial pathogenesis and (b) the expression of genes in response to acid stress and their roles in preventing cell death.
My group studies the activities that generate and dissolve fibrin. We have detailed information on many of the interacting components and we continue to improve our methods for defining local activities of these systems. We now aim to develop a model that can use our quantitative analysis to predict the balance between competing local activities.
We are studying the pathobiology of Candida albicans, the major systemic fungal pathogen of humans. We are exploiting genomic technologies (transcript profiling and proteomics ) in combination with molecular and cellular approaches to address the links between C. albicans pathogenicity and four main aspects of the biology of this fungus: (a) yeast-hypha morphogenesis (b) stress responses (c) amino acid starvation responses, and (d) carbon assimilation. Pathogenicity is intimately linked with these fundamental aspects of C. albicans biology within The System.
Links: Transcript Profiling, Proteomics
Research in my laboratory and the Aberdeen Proteome Facility employs a wide range of protein characterisation technologies to the study of global changes in protein synthesis for a number of biological systems. At present key technologies include high resolution 2D gel electrophoresis combined with protein identification by peptide mass mapping and N-terminal amino acid sequencing. MALDI -TOF MS is also used to analyse whole proteins from HPLC fractions. In house research looks at bacterial pathogens and urine analysis for biomarker identification. Collaborative studies via the Aberdeen Proteome Facility have applied these technologies to studies in human disease as well as model biological systems ranging from fish to plants.
Aberdeen Proteome Facility: abdn.ac.uk/proteome
My main research activity is in the field of Model-Based Technology (MBT) and its applications. I have applied the techniques of MBT in the biomedical domain to the modelling and simulation of pharmacokinetic and metabolic systems. Most recently I have been exploring (in collaboration with Prof Ross King at Aberystwyth, and Ashwin Srinivasan, IBM India) the application of Machine Learning and Qualitative Reasoning to identifying the dynamic structure of metabolic systems.
My group is interested in global circuits that link the integrity of the cell wall with any stresses or mutations that damage the fungal cell wall. This integrated circuitry represents a homoeostatic mechanism that restores cell integrity via a range of signalling pathways, after damage. There is a direct relevance of this research to the pharmaceutical industry.
Professor Grebogi did extensive research in the field of plasma physics before his work on chaotic dynamics. Professor Grebogi's research on chaotic dynamical systems combines analytical methods and techniques with extensive computer experiments utilizing state-of-the-art computational facilities. The objective of the research is to establish basic mathematical principles so that scientists and engineers can then apply these principles to understand and analyze the systems they are investigating in their own fields. Using this approach Professor Grebogi has obtained a number of important results e.g.: the establishment of "crises" is the fundamental process by which chaotic attractors undergo sudden changes as a system parameter varies; the mathematical theory and experimental verification of how transient chaos phenomena are likely to manifest themselves in practice; and the work on fractal basin boundaries that pointed out the important practical consequences of this type of boundary. The current research focuses on methods to control chaos, the dynamics of spatio-temporal systems, active processes in chaotic flows, and the rigorous determination of how long actual trajectories of a chaotic process stay near a given numerical trajectory, i.e. the problem of shadowing.
When, where and how much genes are expressed is now recognised as one of the most important factors governing human health. Our lab is exploring the regulatory systems that control the expression of a variety of different neuropeptides and their receptors. To do this we have developed novel predictive bio-informatic approaches (In collaboration with Dr. Andrew Starkey) that can accurately identify enhancers required to drive the expression of a number of neuropeptide genes and have succesfully tested the tissue specific properties of these enhancers using whole animal transgenic analysis. We have also assembled a multidisciplinary team of individuals that include Drs Ian McEwan (expert in DNA-protein interaction), David MacEwan (Signal transduction) and Ruth Ross (ligand-receptor interaction) who will use these unique transgenic models to deduce the components of the regulatory systems that support the expression of many of these genes in the brain and spine. This unique combination of predictive bioinformatics and transgenic analysis places us in an excellent position to deduce how gene regulatory systems function and how their mis-function can contribute to diseases such as inflammatory pain, asthma, arthritis and depression.
People with disability are considerably disadvantaged as a result of inadequate theory driven treatments. In order to better understand disability, we use large-scale multidisciplinary approaches combining different theoretical perspectives with technological advances. Our group explores fundamental research questions from very different perspectives (e.g. neurophysiological measures of muscle activity through to systems level analysis of coordination dynamics).
My work in systems biology consists of modelling the dynamics of biological processes, at the cell level. Both deterministic and stochastic models are used, and mathematical methods from the areas of dynamical systems and statistical physics are employed, including both mathematical analysis and computer simulations. The projects I am currently involved in include: modelling ionic homeostasis in bacteria; studying DNA replication through a stochastic model; modelling the response of pathogenic fungi to multiple stresses; and studying statistical properties of gene expression changes in rats exposed to anaesthetics.
Molecular techniques have enabled characterisation of the vast diversity and complexity of microbes in natural environments, including the sizeable majority that cannot be cultivated in the laboratory. A major challenge is to determine the links between phylogenetic diversity, physiological diversity and ecosystem function. We are addressing this challenge through transcriptomic and proteomic analysis of the response of nitrifying bacteria to environmental change, including pH and substrate concentration, metagenomic analysis of archaeal communities and links between the diversity of complex soil communities and the diversity of organic compounds that they utilise and produce.
We are interested in developing efficient computational approaches to represent and compare the three-dimensional structures of biological macromolecules such as proteins and DNA, and the ligands that bind to them. The work in our group includes using object-oriented programming techniques to build molecular models, using approximate but efficient methods of modelling protein flexibility, and using novel fast Fourier correlation techniques to predict how proteins might interact.
We are studying both the innate and adaptive immune responses in fish and aim to predict which pathways are switched on by the presence of infectious non-self both in vitro and in vivo. Receptor-ligand interactions on the cell surface of cells of the innate and adaptive immune system can trigger intra-cellular signal cascades leading to the release of inter-cellular messengers and effector molecules. Our approach is both at the level of gene identification through comparative genome scans and functional genomics and focuses on cytokine pathways, pattern recognition receptors, and antigen presentation by major histocompatibility complex molecules.
Project Website: Recognition, signal and effector pathways in the immune system
I'm involved in several projects in the genetics of humans and of the fungus Candida albicans. I have been developing software, and using a variety of other people's software, to carry out simulations and analysis of large genetic data sets. I have experience in writing programs in Perl and C++. Using these approaches on Candida albicans, we are investigating the extent to which natural strains are affected by recombination and exchange of chromosomal material, and which of the possible mechanisms (meiotic or mitotic recombination, gene conversion, parasexual reproduction) might be responsible.
We are studying virus – host intereactions, in particular between the antibiotic producing bacteria, Streptomyces and their viruses (called bacteriophages or phages). We have three main projects at the moment (a) the mechanism of a group of DNA recombinases that act at specific sequences (b) the mechanism and biological consequences of a novel type of phage resistance system and (c) studies on a yeast-like glycosylation pathway in Streptomyces and Mycobacteria. We are also interested in phage genomics and taxonomy.
One strand of our work in the Soils Modelling Group of the School of Biological Sciences involves predicting ecosystem, regional, national, continental and global scale carbon and nitrogen fluxes from the soil plant system to the atmosphere. This is very much a systems biology approach, indeed, it even goes beyond biology. The models we use are biophysical models describing processes occurring at the ecosystem scale but these are linked to other specific ecosystem models (e.g. dedicate forestry or cropland models), models describing global climate, macroeconomic models (determining markets and feeding into the land-use models), and land-use / land-allocation models. This approach requires input from many collaborators from a range of disciplines including atmospheric physicists and chemists (climate), economists, social scientists and geographers (macroeconomic and land-use models). A systems level approach is absolutely essential when considering processes at this spatial scale, providing a clear example of systems biology in action.
We have an international reputation in applying process-based ecosystem models at continental to global spatial scales. We also have expertise in Monte Carlo techniques and Bayesian emulation techniques to conduct sensitivity and uncertainty analyses using the same models. Both applications require large computer resources. We plan to use grid-based technologies, and other mechanisms pioneered by The Department of Computing Science to combine our large spatial applications of the models with Monte Carlo and Bayesian emulation techniques, to derive a spatially explicit sensitivity and uncertainty analysis, to be analysed using geostatistical techniques within a geographic information system (GIS). This has never before been attempted, and represents a truly integrated biological systems approach involving collaboration between researchers in the School of Biological Sciences and the Department of Computing Science.
Our research interests seek to develop an understanding of how cellular gene expression is controlled at the level of mRNA translation (the process of protein synthesis by the ribosome). We are using Perl-based programming to analyse whole genome sequence information, with the overall aim of discovering how evolution has shaped genome sequence to optimise both the efficiency and accuracy of translation. We are primarily identifying biases in the composition of genomic sequences to infer the binding and recognition requirements of the proteins that interact with the ribosome during translation. We are also developing agent-based modelling techniques to generate predictive representations of the dynamic translation process.
I am currently investigating the use of novel asset mining techniques in the bioinformatics area. Asset mining is a form of data mining and explores data sets and returns to the user characteristics that can be exploited in some way, and makes use of artificial intelligence techniques and statistics to automate the process. Examples of the use of this technique are in identifying the key genes for stop codon readthrough in yeast (with Ian Stansfield), identification of replication sites in DNA (with Anne Donaldson) and the prediction of functional linkages between regulatory sequences and gene using a combination of phylogenetic footprinting and multi species analysis of conserved synteny blocks (with Alasdair MacKenzie).
My research interests are mainly in the fields of nonlinear dynamics, mathematical modelling and nonlinear time series analysis. I use both analytical and numerical methods, which also involve parallel computing. I apply these mathematical tools to "real world systems" ranging from neurophysics (modeling of large scale brain networks) to physiology (heartbeat interactions of mother and foetus, eye movements, EEGs) and astrophysics (extrasolar planetary systems). I am now developing interests in the modelling of transcription networks and cell wall biosynthesis.
Our interests are in the evolution, ecology and epidemiology of infectious diseases with a particular focus on gastrointestinal zoonoses (E. coli O157, Campylobacter, Cryptosporidium and Salmonella) and the mycobacterial infections of M. paratuberculosis, probably another zoonosis, M. malmoense and the obligate human respiratory pathogen M. tuberculosis.
My research incorporates stochastic simulation, modelling and inference; Nonlinear dynamical systems and control; Reconstructing dynamics from nonlinear time series.
University of Aberdeen, Kings College, Aberdeen, AB24 3FX
Tel: +44 (0) 1224 272000 Fax: +44 (0) 1224 272000
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) Last Updated: 20 Oct 2008