Using predictive models to engineer biology: a case study in codon optimization

Authors:
Alexey A. Gritsenko;Marcel J. T. Reinders;Dick de Ridder
Affiliations:
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands,Platform Green Synthetic Biology, Delft, The Netherlands,Kluyver Centre for ...;The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands,Platform Green Synthetic Biology, Delft, The Netherlands,Kluyver Centre for ...;The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands,Platform Green Synthetic Biology, Delft, The Netherlands,Kluyver Centre for ...
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

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Abstract

Given recent advances in synthetic biology and DNA synthesis, there is an increasing need for carefully engineered biological parts (e.g. genes, promoter sequences or enzymes) and circuits. However, forward engineering approaches are thus far rarely used in biology due to lack of detailed knowledge of the biological mechanisms. We describe a framework that enables forward engineering in biology by constructing models predictive of properties of interest, then inverting and using these models to design biological parts. We demonstrate the applicability of the proposed framework on the problem of codon optimization, concerned with optimizing gene coding sequences for efficient translation. Results suggest that our data-driven codon optimization (DECODON) method simultaneously considers the effects multiple translation mechanisms to produce optimal sequences, in contrast to existing codon optimization techniques.