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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alper H et al (2005) Tuning genetic control through promoter engineering. Proc Natl Acad Sci U S A 102(36):12678–12683
Mutalik VK et al (2013) Precise and reliable gene expression via standard transcription and translation initiation elements. Nat Methods 10(4):354–360
Salis HM (2011) The ribosome binding site calculator. Methods Enzymol 498:19–42
Salis HM, Mirsky EA, Voigt CA (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol 27(10):946–950
Carrier T, Jones KL, Keasling JD (1998) mRNA stability and plasmid copy number effects on gene expression from an inducible promoter system. Biotechnol Bioeng 59(6):666–672
Chen Y et al (2012) Enhancing the copy number of episomal plasmids in Saccharomyces cerevisiae for improved protein production. FEMS Yeast Res 12(5):598. LP-607
Jack BR et al (2015) Predicting the genetic stability of engineered DNA sequences with the EFM calculator. ACS Synth Biol 4(8):939–943
Sleight SC et al (2010) Designing and engineering evolutionary robust genetic circuits. J Biol Eng 4(1):1–20
Skancke J et al (2015) Sequence-dependent promoter escape efficiency is strongly influenced by bias for the pretranslocated state during initial transcription. Biochemistry 54(28):4267–4275
Espah Borujeni A, Channarasappa AS, Salis HM (2014) Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Res 42(4):2646–2659
Espah Borujeni A, Salis HM (2016) Translation initiation is controlled by RNA folding kinetics via a ribosome drafting mechanism. J Am Chem Soc 138(22):7016–7023
Grosjean H, Fiers W (1982) Preferential codon usage in prokaryotic genes: the optimal codon-anticodon interaction energy and the selective codon usage in efficiently expressed genes. Gene 18(3):199–209
Tian T, Salis HM (2015) A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons. Nucleic Acids Res 43(14):7137–7151
Casini A et al (2014) R2oDNA designer: computational design of biologically neutral synthetic DNA sequences. ACS Synth Biol 3(8):525–528
Davis JH, Rubin AJ, Sauer RT (2011) Design, construction and characterization of a set of insulated bacterial promoters. Nucleic Acids Res 39(3):1131–1141
Kosuri S et al (2013) Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc Natl Acad Sci U S A 110(34):14024–14029
Brewster RC, Jones DL, Phillips R (2012) Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. PLoS Comput Biol 8(12):e1002811
Farasat I et al (2014) Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Mol Syst Biol 10:731–731
Khodayari A et al (2014) A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab Eng 25:50–62
Theisen MK, Lafontaine Rivera JG, Liao JC (2016) Stability of ensemble models predicts productivity of enzymatic systems. PLoS Comput Biol 12(3):e1004800
Tran LM, Rizk ML, Liao JC (2008) Ensemble modeling of metabolic networks. Biophys J 95(12):5606–5617
Gibson DG et al (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat Methods 6(5):343–345
Engler C, Kandzia R, Marillonnet S (2008) A one pot, one step, precision cloning method with high throughput capability. PLoS One 3(11):1–7
Weber E et al (2011) A modular cloning system for standardized assembly of multigene constructs. PLoS One 6(2):e16765
Murphy KC (1998) Use of bacteriophage – recombination functions to promote gene replacement in Escherichia coli. J Bacteriol 180(8):2063–2071
Jiang Y et al (2015) Multigene editing in the Escherichia coli genome using the CRISPR-Cas9 system. Appl Environ Microbiol 81(7):2506–2514
Wang HH et al (2009) Programming cells by multiplex genome engineering and accelerated evolution. Nature 460(7257):894–898
Ng CY et al (2015) Rational design of a synthetic Entner-Doudoroff pathway for improved and controllable NADPH regeneration. Metab Eng 29:86–96
Forde NR et al (2002) Using mechanical force to probe the mechanism of pausing and arrest during continuous elongation by Escherichia coli RNA polymerase. Proc Natl Acad Sci U S A 99(18):11682–11687
Fell DA (1998) Increasing the flux in metabolic pathways: a metabolic control analysis perspective. Biotechnol Bioeng 58(2–3):121–124
Smanski MJ et al (2014) Functional optimization of gene clusters by combinatorial design and assembly. Nat Biotechnol 32(12):1241–1249
Lin Z et al (2014) Metabolic engineering of Escherichia coli for the production of riboflavin. Microb Cell Factories 13:104
Nowroozi FF et al (2014) Metabolic pathway optimization using ribosome binding site variants and combinatorial gene assembly. Appl Microbiol Biotechnol 98(4):1567–1581
Su B et al (2015) Efficient production of xylitol from hemicellulosic hydrolysate using engineered Escherichia coli. Metab Eng 31:112–122
Ahmadi MK et al (2016) E. coli metabolic engineering for gram scale production of a plant-based anti-inflammatory agent. Metab Eng 38:382–388
Schmidl SR et al (2014) Refactoring and optimization of light-switchable Escherichia coli two-component systems. ACS Synth Biol 3(11):820–831
Yang L et al (2014) Permanent genetic memory with >1-byte capacity. Nat Methods 11(12):1261–1266
Zhou J et al (2014) Engineering Escherichia coli for selective geraniol production with minimized endogenous dehydrogenation. J Biotechnol 169:42–50
Moon TS et al (2009) Production of glucaric acid from a synthetic pathway in recombinant Escherichia coli. Appl Environ Microbiol 75(3):589–595
Ajikumar PK et al (2010) Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science (New York, NY) 330(6000):70–74
Thodey K, Galanie S, Smolke CD (2014) A microbial biomanufacturing platform for natural and semisynthetic opioids. Nat Chem Biol 10(10):837–844
Brockman IM, Prather KLJ (2015) Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. Metab Eng 28:104–113
Soma Y, Hanai T (2015) Self-induced metabolic state switching by a tunable cell density sensor for microbial isopropanol production. Metab Eng 30:7–15
Xu P et al (2014) Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc Natl Acad Sci U S A 111(31):11299–11304
Fang M et al (2016) Intermediate-sensor assisted push–pull strategy and its application in heterologous deoxyviolacein production in Escherichia coli. Metab Eng 33:41–51
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Halper, S.M., Cetnar, D.P., Salis, H.M. (2018). An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization. In: Jensen, M.K., Keasling, J.D. (eds) Synthetic Metabolic Pathways. Methods in Molecular Biology, vol 1671. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7295-1_4
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7295-1_4
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7294-4
Online ISBN: 978-1-4939-7295-1
eBook Packages: Springer Protocols