Synthetic Evolution of Metabolic Productivity Using Biosensors

Synthetic biology has progressed to the point where genes that encode whole metabolic pathways and even genomes can be manufactured and brought to life. This impressive ability to synthesise and assemble DNA is not yet matched by an ability to predictively engineer biology. These difficulties exist...

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Bibliographic Details
Published in:Trends in biotechnology (Regular ed.) 2016-05, Vol.34 (5), p.371-381
Main Authors: Williams, Thomas C, Pretorius, Isak S, Paulsen, Ian T
Format: Article
Language:English
Subjects:
adaptive laboratory evolution
Antibiotics
Biological
Biology
biosensing
Biosensing Techniques
Biosensors
Cell cycle
Deoxyribonucleic acid
DNA
E coli
Engineering
Enzymes
Escherichia coli - genetics
Escherichia coli - metabolism
Evolution
Gene expression
Genes
genome engineering
Genomes
Genotype & phenotype
Harnesses
Internal Medicine
Laboratories
metabolic engineering
Metabolic Engineering - methods
Metabolic Networks and Pathways
Metabolism
Metabolites
Microorganisms
Mutation
Productivity
Proteins
Saccharomyces cerevisiae
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae - metabolism
Synthetic biology
Synthetic Biology - methods
Systems Biology - methods
yeast
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Summary:Synthetic biology has progressed to the point where genes that encode whole metabolic pathways and even genomes can be manufactured and brought to life. This impressive ability to synthesise and assemble DNA is not yet matched by an ability to predictively engineer biology. These difficulties exist because biological systems are often overwhelmingly complex, having evolved to facilitate growth and survival rather than specific engineering objectives such as the optimisation of biochemical production. A promising and revolutionary solution to this problem is to harness the process of evolution to create microbial strains with desired properties. The tools of systems biology can then be applied to understand the principles of biological design, bringing synthetic biology closer to becoming a predictive engineering discipline.
ISSN:0167-7799
1879-3096
DOI:10.1016/j.tibtech.2016.02.002