An automated pipeline for engineering many-enzyme pathways: Computational sequence design, pathway expression-flux mapping, and scalable pathway optimization

Sean M. Halper, Daniel P. Cetnar, Howard M. Salis

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational–experimental pipeline that identifies a pathway’s optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway’s optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway’s sequence–expression–activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages39-61
Number of pages23
DOIs
StatePublished - Jan 1 2018

Publication series

NameMethods in Molecular Biology
Volume1671
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

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