Abstract

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. Synthetic biological systems can range in size and complexity from metabolic pathways to entire genomes.Our capacity to assemble DNA sequences is not matched by an ability to predictively engineer novel biological functions because of the overwhelming complexity of biological systems.Adaptive laboratory evolution (ALE) allows systems-biology approaches to be used to discover the genetic and physiological basis of evolved phenotypes, thereby informing rational design.If ALE could be applied to evolve microbes for the production of target metabolites, then many of the bottlenecks that currently limit rational engineering in synthetic biology could be overcome.Metabolite biosensors connect the intracellular concentration of a target molecule to a survival output. Genetically diverse populations can then be screened for superior producers that have novel genomic architectures.

LanguageEnglish
Pages371-381
Number of pages11
JournalTrends in Biotechnology
Volume34
Issue number5
DOIs
Publication statusPublished - 1 May 2016

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Synthetic Biology
Biosensing Techniques
Biological systems
Biosensors
Systems Biology
Genes
Productivity
Metabolites
Metabolic Networks and Pathways
Genome
Engineers
DNA sequences
DNA
Phenotype
Molecules
Growth
Population

Cite this

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title = "Synthetic Evolution of Metabolic Productivity Using Biosensors",
abstract = "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. Synthetic biological systems can range in size and complexity from metabolic pathways to entire genomes.Our capacity to assemble DNA sequences is not matched by an ability to predictively engineer novel biological functions because of the overwhelming complexity of biological systems.Adaptive laboratory evolution (ALE) allows systems-biology approaches to be used to discover the genetic and physiological basis of evolved phenotypes, thereby informing rational design.If ALE could be applied to evolve microbes for the production of target metabolites, then many of the bottlenecks that currently limit rational engineering in synthetic biology could be overcome.Metabolite biosensors connect the intracellular concentration of a target molecule to a survival output. Genetically diverse populations can then be screened for superior producers that have novel genomic architectures.",
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Synthetic Evolution of Metabolic Productivity Using Biosensors. / Williams, Thomas C.; Pretorius, Isak S.; Paulsen, Ian T.

In: Trends in Biotechnology, Vol. 34, No. 5, 01.05.2016, p. 371-381.

Research output: Contribution to journalReview articleResearchpeer-review

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