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Cancer Systems Biology

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Systems Biology in Drug Discovery and Development

Part of the book series: Methods in Molecular Biology ((MIMB,volume 662))

Abstract

Cancer is a complex and heterogeneous disease, not only at a genetic and biochemical level, but also at a tissue, organism, and population level. Multiple data streams, from reductionist biochemistry in vitro to high-throughput “-omics” from clinical material, have been generated with the hope that they encode useful information about phenotype and, ultimately, tumour behaviour in response to drugs. While these data stand alone in terms of the biology they represent, there is the enticing prospect that if incorporated into systems biology models, they can help understand complex systems behaviour and provide a predictive framework as an additional tool in understanding how tumours change and respond to treatment over time. Since these biological data are heterogeneous and frequently qualitative rather than quantitative, at the present time a single systems biology approach is unlikely to be effective; instead, different computational and mathematical approaches should be tailored to different types of data, and to each other, in order to test and re-test hypotheses. In time, these models might converge and result in usable tractable models which accurately represent human cancer. Likewise, biologists and clinicians need to understand what the requirements of systems biology are so that compatible data are produced for computational modelling. In this review, we describe some theoretical approaches (data-driven and process-driven) and experimental methodologies which are being used in cancer research and the clinical context where they might be applied.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-1-60761-800-3_17

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Authors and Affiliations

  1. Breakthrough Research Unit, Centre for Research in Informatics and Systems Pathology (CRISP), University of Edinburgh, Edinburgh, UK

    Dana Faratian

  2. University of Edinburgh, Edinburgh, UK

    James L. Bown

  3. University of Abertay Dundee, Dundee, UK

    V. Anne Smith

  4. University of St. Andrews, St. Andrews, UK

    Simon P. Langdon & David J. Harrison

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  1. Dana Faratian
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  2. James L. Bown
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  3. V. Anne Smith
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  4. Simon P. Langdon
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  5. David J. Harrison
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Correspondence to Dana Faratian .

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  1. PharmTao, Santa Clara, California, USA

    Qing Yan

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Faratian, D., Bown, J.L., Smith, V.A., Langdon, S.P., Harrison, D.J. (2010). Cancer Systems Biology. In: Yan, Q. (eds) Systems Biology in Drug Discovery and Development. Methods in Molecular Biology, vol 662. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-800-3_12

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  • DOI: https://doi.org/10.1007/978-1-60761-800-3_12

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  • Publisher Name: Humana Press, Totowa, NJ

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