Bioinformatics for the synthetic biology of natural products: integrating across the Design–Build–Test cycle

  title={Bioinformatics for the synthetic biology of natural products: integrating across the Design–Build–Test cycle},
  author={Pablo Carbonell and Andrew Currin and Adrian J. Jervis and Nicholas J. W. Rattray and Neil Swainston and Cunyu Yan and Eriko Takano and Rainer Breitling},
  journal={Natural Product Reports},
  pages={925 - 932}
Bioinformatics tools facilitate and accelerate all steps along the Design–Build–Test cycle of synthetic biology, for the enhanced production of natural products in engineered microbes. 

Figures and Tables from this paper

Integrative Multi-Omics Through Bioinformatics.
  • H. Goh
  • Biology
    Advances in experimental medicine and biology
  • 2018
How bioinformatics helps to integrate omics data derived from various studies described in previous chapters for a holistic understanding of secondary metabolite production in P. minus is concluded.
Computational Studies and Biosynthesis of Natural Products with Promising Anticancer Properties
We present an overview of computational approaches for the prediction of metabolic pathways by which plants biosynthesise compounds, with a focus on selected very prom‐ ising anticancer secondary
Mapping the patent landscape of synthetic biology for fine chemical production pathways
An assessment of pathways as potential targets for chemical production across the full catalogue of reachable chemicals in the extended metabolic space of chassis organisms, as computed by the retrosynthesis‐based algorithm RetroPath is performed.
Selenzyme: Enzyme selection tool for pathway design
Selenzyme is a free online enzyme selection tool for metabolic pathway design that provides bespoke sequence selection for automated workflows in biofoundries and is integrated as part of the pathway design stage into the design-build-test-learn SYNBIOCHEM pipeline.
The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes.
The Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes is described and how it can be used for the discovery and production of novel specialized metabolites.
An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals
An automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals is presented and application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.
Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism
The updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces and enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.
Gsmodutils: a python based framework for test-driven genome scale metabolic model development
The gsmodutils modelling framework is developed, placing an emphasis on test-driven design of models through defined test cases, allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.


Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently
Improving enzymes by directed evolution requires the navigation of very large search spaces; we survey how to do this intelligently.
Computational tools for the synthetic design of biochemical pathways
Key existing tools for de novo design of biosynthetic pathways and suggestions for how informatics can help to shape the future of synthetic microbiology are offered.
Building biological foundries for next-generation synthetic biology
This review summarizes the state-of-the-art technologies for synthetic biology and discusses the challenges to establish such biological foundries.
XTMS: pathway design in an eXTended metabolic space
XTMS, a web-based pathway analysis platform available at, provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework.
Metabolic tinker: an online tool for guiding the design of synthetic metabolic pathways
An online tool called Metabolic Tinker, which aims to guide the design of synthetic metabolic pathways between any two desired compounds using a tailored heuristic search strategy, and provides for the first time thermodynamic feasibility information for the discovered paths.
Construction and optimization of synthetic pathways in metabolic engineering.
FindPath: a Matlab solution for in silico design of synthetic metabolic pathways
FindPath is a unified system to predict and rank possible pathways according to their metabolic efficiency in the cellular system, which uses a chemical reaction database to generate possible metabolic pathways and exploits constraint-based models (CBMs) to identify the most efficient synthetic pathway.
RobOKoD: microbial strain design for (over)production of target compounds
RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets, provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.