Diversity-based, model-guided construction of synthetic gene networks with predicted functions.

ABSTRACT: Engineering artificial gene networks from modular components is a major goal of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized nonessential sequence) with in silico modeling to guide predictable gene network construction without the need for post hoc tweaking. We demonstrate our approach in Saccharomyces cerevisiae by synthesizing regulatory promoter libraries and using them to construct feed-forward loop networks with different predicted input-output characteristics. We then expand our meth

SUBMITTER: Ellis T 

PROVIDER: S-EPMC2680460 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature