Synthetic biology - a combinatorial optimisation problem
Douglas B. Kell
Manchester Interdisciplinary Biocentre, University of Manchester/BBSRC
Most of our bioanalytical projects are at the interface between postgenomic biological systems, quantitative analytical chemistry and machine learning, with a special emphasis on evolutionary computing and systems biology.
Email: University of Manchester & BBSRC
Websites: DBK Group & BBSRC
The ability to synthesise essentially any DNA molecule involves questions of both ‘how’ (with what technologies) and ‘why’ (to what purpose), and BBSRC recognises and would wish to support work in both these areas and others (including continuing the successful ‘synthetic biology dialogue’ that we have supported in collaboration with EPSRC).
Deciding what might best be synthesised is a combinatorial optimisation problem1 in that even a small aptamer of just 30 bases has 430 possibilities (c. 1018, an amount as 5 µm spots that would cover ~ 29 km2!)2. A small protein with just 100 amino acids can have 20100 ~10130 sequences (and the lifetime of the Universe is c. 1017 s). We have used evolutionary computing methods to navigate a variety of large search spaces in the synthesis of aptamers (e.g. 2,3). Synthetic biology approaches have also proved useful in the design and exploitation of ‘Qconcats’ in quantitative proteomics4.
1. Kell DB. 2012. Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments? Bioessays 34: 236-244.
2. Knight CG, Platt M, Rowe W, Wedge DC, Khan F, Day P, McShea A, Knowles J, Kell DB. 2009. Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape. Nucleic Acids Res 37: e6.
3. Rowe W, Platt M, Wedge D, Day PJ, Kell DB, Knowles J. 2010. Analysis of a complete DNA-protein affinity landscape. J R Soc Interface 7: 397-408.
4. Carroll KM, Simpson DM, Eyers CE, Knight CG, Brownridge P, Dunn W, Winder CL, Lanthaler K, Pir P, Malys N, Kell DB, Oliver SG, Gaskell SJ, Beynon RJ. 2011. Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach. Mol Cell Proteomics 10: M111 007633.
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