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For any enquiries related to the Biomolecular Medicine research area, please contact

Fiona Pereira
CSM Research Manager
csm@imperial.ac.uk

+44 (0)20 7594 3197

Data-driven approaches for systems biology

The complex interactions that influence human health are a challenging yet fundamental part of modern medicine. Understanding such complexity is therefore great importance to our ability to advance clinical practice, public health, drug development and environmental assessment.

Top-down systems biology approaches have been proposed as an efficient means of gaining a better understanding of how these various factors interact by considering global profiles that describe various level of biological organisation an complexity, including genes, proteins and metabolites.

By studying systemic responses, these top-down approaches can efficiently provide an overview of complex, multi-stage processes that are displaced in time and/or space.


Find out more about our key focus areas:

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Personalised Healthcare

Many pharmaceuticals have idiosyncratic action when administered. The concept that healthcare solutions can be tailored to the individual is one that is attractive as it potentially allows a better match of patient and drug. Identifying signatures indicative of treatment outcome are key to personalising medicine. Top-down systems biology offers an opportunity to help predict drug efficiacy and avoid adverse reactions. Providing optimised healthcare on an individual basis will benefit both patients and clinicians through improved drug choice, efficacy and reduced costs. From the work we have conducted using large scales molecular epidemiology studies using metabolic phenotyping, it is clearer than ever before that a one-size-fits-all solution to drug therapy is not a sustainable or desirable model. Given the diversity of human biochemistry, such phenotypes are important in personalising medicine as they provide clues as to the influences of a variety of factors including underlying genetics, environmental stress, nutritional status and gut microbial activity.

We have shown that is possible to use predose metabolic profiles to predict xenobiotic metabolic outcomes in both experimental animals and humans in what has been termed “pharmacometabonomics” :

“the prediction of the drug metabolism and toxicity in an individual using a mathematical model of preintervention metabolite signatures”

Clayton et al. (2006).

This has huge implications for optimising drug treatment as it will allow patient stratification and the identification of non- and hyper-responsive individuals, and provides a complementary approach to that of pharmacogenomics.

Strat Med

Molecular Phenotyping

Metabonomic approaches have major application in epidemiological research. that open up the possibility of testing epidemiologically generated hypotheses at the cellular and physiological level, with discovery of novel metabolic biomarkers that link to environmental exposures and phenotypic traits. Integrating individual-level molecular profiles within small-area and large-scale population epidemiological studies promises to provide new insight into the environmental and lifestyle factors that influences high-priority diseases such as hypertension, diabetes and cancer. Metabolic profiling provides the opportunity to identify biomarkers of exposure, early effect, early onset and disease progression, and to generate hypotheses about how physiological measurements routinely collected in epidemiological studies are mechanistically related to metabolic phenotype. CSM researchers have used large-scale epidemiological sample sets to clearly demonstrate the metabolic phenotypic clustering by geographical location, and that variation within populations can be meaningfully related to physiological measurements (Holmes et al. 2008). This work has helped derive novel associations between urinary metabolites and blood pressure that has provided additional evidence for public health decision making and hypotheses to test relating to the underlying mechanisms of the observed relationships. Such approaches are also potentially useful in verifying epidemiological questionnaire data (i.e. does reported usage relate well to observed excretion profile) and for investigating the underlying causes of idiosyncratic drug efficacy and toxicity. The analysis of these data have greatly been helped by the continued development and application of novel statistical correlation approaches that can facilitate rapid structure elucidation using spectroscopic profile data (Holmes et al. 2007). Furthermore, these resources present the opportunity to begin relating xenobiotic and endogenous metabolites in a way that can report on common biochemical mechanisms and highlight populations that may have a heightened risk of adverse consequences to exposure due to lifestyle or other factors.

CSM researchers have used large-scale epidemiological sample sets to clearly demonstrate the metabolic phenotypic clustering by geographical location, and that variation within populations can be meaningfully related to physiological measurements (Holmes et al. 2008). This work has helped derive novel associations between urinary metabolites and blood pressure that has provided additional evidence for public health decision making and hypotheses to test relating to the underlying mechanisms of the observed relationships. Such approaches are also potentially useful in verifying epidemiological questionnaire data (i.e. does reported usage relate well to observed excretion profile) and for investigating the underlying causes of idiosyncratic drug efficacy and toxicity. The analysis of these data have greatly been helped by the continued development and application of novel statistical correlation approaches that can facilitate rapid structure elucidation using spectroscopic profile data (Holmes et al. 2007). Furthermore, these resources present the opportunity to begin relating xenobiotic and endogenous metabolites in a way that can report on common biochemical mechanisms and highlight populations that may have a heightened risk of adverse consequences to exposure due to lifestyle or other factors.

SampleJet

Metabolic profiling provides the opportunity to identify biomarkers of exposure, early effect, early onset and disease progression, and to generate hypotheses about how physiological measurements routinely collected in epidemiological studies are mechanistically related to metabolic phenotype. e have used large-scale epidemiological sample sets to clearly demonstrate the metabolic phenotypic clustering by geographical location, and that variation within populations can be meaningfully related to physiological measurements (Holmes et al. 2008). This work has helped derive novel associations between urinary metabolites and blood pressure that has provided additional evidence for public health decision making and hypotheses to test relating to the underlying mechanisms of the observed relationships. Such approaches are also potentially useful in verifying epidemiological questionnaire data (i.e. does reported usage relate well to observed excretion profile) and for investigating the underlying causes of idiosyncratic drug efficacy and toxicity. The analysis of these data have greatly been helped by the continued development and application of novel statistical correlation approaches that can facilitate rapid structure elucidation using spectroscopic profile data (Holmes et al. 2007). Furthermore, these resources present the opportunity to begin relating xenobiotic and endogenous metabolites in a way that can report on common biochemical mechanisms and highlight populations that may have a heightened risk of adverse consequences to exposure due to lifestyle or other factors.

This work has helped derive novel associations between urinary metabolites and blood pressure that has provided additional evidence for public health decision making and hypotheses to test relating to the underlying mechanisms of the observed relationships. Such approaches are also potentially useful in verifying epidemiological questionnaire data (i.e. does reported usage relate well to observed excretion profile) and for investigating the underlying causes of idiosyncratic drug efficacy and toxicity. The analysis of these data have greatly been helped by the continued development and application of novel statistical correlation approaches that can facilitate rapid structure elucidation using spectroscopic profile data (Holmes et al. 2007). Furthermore, these resources present the opportunity to begin relating xenobiotic and endogenous metabolites in a way that can report on common biochemical mechanisms and highlight populations that may have a heightened risk of adverse consequences to exposure due to lifestyle or other factors.

Tubes

We have used large-scale epidemiological sample sets to clearly demonstrate the metabolic phenotypic clustering by geographical location, and that variation within populations can be meaningfully related to physiological measurements (Holmes et al. 2008). This work has helped derive novel associations between urinary metabolites and blood pressure that has provided additional evidence for public health decision making and hypotheses to test relating to the underlying mechanisms of the observed relationships.Such approaches are also potentially useful in verifying epidemiological questionnaire data (i.e. does reported usage relate well to observed excretion profile) and for investigating the underlying causes of idiosyncratic drug efficacy and toxicity. 

The analysis of these data have greatly been helped by the continued development and application of novel statistical correlation approaches that can facilitate rapid structure elucidation using spectroscopic profile data (Holmes et al. 2007). Furthermore, these resources present the opportunity to begin relating xenobiotic and endogenous metabolites in a way that can report on common biochemical mechanisms and highlight populations that may have a heightened risk of adverse consequences to exposure due to lifestyle or other factors.

Key members within Biomolecular Medicine

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