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Introduction |
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Chemical genomics is an emerging discipline that typically brings together
diversity-oriented chemical libraries and high-information-content cellular
assays, along with the informatics and mining tools necessary for storing and
analysing the data so generated. It is a complement to classical genetics but
has at least two advantages: it can be used in genetically non-tractable
organisms, and individual chemicals can influence single activities of gene
products with multiple roles in a way that gene knockouts and knockins cannot.
It is considered especially useful in providing novel tools for the manipulation
of cellular activities. Current work is directed to the
development and exploitation of these and related methods for the purposes of
functional genomics and in mode-of-action studies,
concentrating in particular at the level of metabolism.
As with many things, progress is seen as an iterative
cycle between molecular targets and cellular assays, where here we distinguish
forward and reverse chemical genetics:

In Forward
Chemical Genomics the direction is screen a library to obtain an activity so as
to discover a target (this is somewhat like classical drug
discovery). In Reverse Chemical Genomics we start with a purified target and
assess binding activity whence we can test such compounds in vivo (this
is somewhat more akin to modern HTS activities).
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Links |
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Here are some chemical genomics-related links:
CHEMICAL AND DRUG STRUCTURES AND CHEMICAL GENOMICS
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Publications |
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Here are some of our recent chemical genomics publications:
- Kell, D.B. (2006) Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells. The FEBS Journal 273, 873–894.
- Kell, D.B., Brown, M., Davey, H.M., Dunn, W.B., Spasic, I. & Oliver, S.G. (2005) Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol, 3, 557-565.
- Brown, M., Dunn, W.B., Ellis, D.I., Goodacre, R., Handl, J., Knowles, J.D., O'Hagan, S., Spasic, I. & Kell, D.B. (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1(1), 39-51.
- Allen, J., Davey, H.M., Broadhurst, D., Rowland, J.J., Oliver, S.G. & Kell, D.B. (2004) Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Applied and Environmental Microbiology 70(10), 6157–6165.
- Kell, D. B. (2004) Metabolomics and systems biology: making sense of the soup. Current Opinion in Microbiology 7(3), 296-307.
- Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G. & Kell, D.B. (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends. Biotechnol. 22, 245-252.
- Allen, J. K., Davey, H. M., Broadhurst, D., Heald, J. K., Rowland, J. J., Oliver, S. G. & Kell, D. B. (2003) High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnol. 21, 692-696.
- Kell, D.B. (2002) Metabolomics and machine learning: explanatory analysis
of complex metabolome data using genetic programming to produce simple, robust rules. Mol. Biol. Rep. 29,
237-242. Full paper
- Kell, D.B., Darby, R.M. & Draper, J. (2001) Genomic computing:
explanatory analysis of plant expression profiling data using machine learning.
Plant Physiology 126, 943-951.
Abstract - Full paper at Plant Physiology
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Last update: 17 April 2006
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