Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments.

ABSTRACT: Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engine

SUBMITTER: Tepper N 

PROVIDER: S-EPMC3025009 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature