OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

doi:10.1007/s40484-014-0033-7

Quantitative Biology

2014, Vol. 2

Issue (3): 100-109 https://doi.org/10.1007/s40484-014-0033-7

本期目录

OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Honglei Liu, Yanda Li, Xiaowo Wang(

)

MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of Automation, Tsinghua University, Beijing100084, China

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Abstract：

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations (upregulation, downregulation and deletion of reactions) and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N-acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP-Synthetic is freely available at:http://bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.

Key words： metabolic network flux analysis optimization

收稿日期: 2014-09-12 出版日期: 2015-01-14

Corresponding Author(s): Xiaowo Wang

引用本文:

. [J]. Quantitative Biology, 2014, 2(3): 100-109.
Honglei Liu, Yanda Li, Xiaowo Wang. OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites. Quant. Biol., 2014, 2(3): 100-109.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-014-0033-7
https://academic.hep.com.cn/qb/CN/Y2014/V2/I3/100

Fig.1

Fig.2

	Approach	Manipulation	Succinate production ( $mmol ? gDW − 1 h − 1$ )	Computation time ( s)
CoreE. coli metabolic model	OptKnock GDLS GDBB OptForce OP-Synthetic^a	ACALD2x, CYTBD, FBP, GLUN, GND, LDH_D, SUCDi ACALD, GLUDy, PFL, PYK, ME2 NA AKGDH( $↑$ ), GAPD( $×$ ), FUM( $×$ ), PFL( $×$ ), ATPM( $×$ ) (step 1) FUM( $↑$ ) (step 2) MDH( $↑$ ) (step 3) PPC( $↑$ ) (step 4) PYK( $×$ ) (step 5) ALCD2x-ETOHt2r-EX_etoh(e)( $×$ ), ACALD ( $×$ )	10.29 9.92 NA 15* 10.30	21 3 NA 45831 30
E. coli iAF1260 metabolic model	OptKnock GDLS GDBB OptForce OP-Synthetic^a	ALCD2x, AP5AH, FE2t3pp, GLCDPP, PRATPP CO2tex, LDH_D, PFL, THD2pp, PPKr ALCD2x, GLCptspp, GLUDy, Pit2rpp, RPE MCOATA_f( $×$ ), PPA( $×$ ), GLYCLTDx( $↑$ ), NADH18pp( $↑$ ), TRSARr_f( $↑$ ) (step 1) FRD2( $↑$ ) (step 2) ETOHtex-ETOHt2rpp-EX_etoh(e) ( $×$ ) (step 3)ACALD( $×$ ) (step 4) PYK( $×$ ) (step 5) PPC( $↑$ )	10.79 9.77 9.42 15* 10.85	53553 49329 53 154583 514
E. coli iAF1260 metabolic model with addition of pyc	OptKnock GDLS GDBB OptForce OP-Synthetic^a	AP5AH, DDCAtexi, DTMPK, GLYK, MCITS, MLTG1, NOtpp, R1PK, URAtex ACALD, ACCOAL, EAR40x, EAR80x, Htex, ME2, PGI, PPKr, SUCOAS ACALD, LALDO2x, ALR4x OAADC, GART, THRA2i, GND, ME2, THD2pp 3HAD60( $×$ ), ACS( $×$ ), O2tex_f( $×$ ), PFL( $×$ ), PGAMT_f( $×$ ), GAPD6( $↑$ ), MALS( $↑$ ), MDH3( $↑$ ), NADH18pp( $↑$ ) (step 1) ACALD( $×$ ), ETOHtex-ETOHt2rpp-EX_etoh(e) ( $×$ ), ALCD2x( $×$ ) (step 2) FDR2( $↑$ ) (step 3) MDH( $↑$ ), PYC( $↑$ ) (step 4) EX_for(e)-FORtex ( $×$ ), FORtppi( $×$ ) (step 5) PFL ( $×$ ),	15.10 12.98 10.69 15* 15.15	62483 9395 136 154592 522

Tab.1

Fig.3

Fig.4

Step	Manipulations
1	G6PDA(nagB) ( $×$ ), AMANAPEr(nanE)( $×$ ), EDD( $×$ ), EX_neu(nanT)( $×$ ), AMANK(nanK)( $×$ ), GLUABUTt7pp( $×$ ), ETOHtex- ETOHt2rpp- EX_etoh(e)( $×$ ), AGDC( $×$ ), ABUTt2pp( $×$ )
2	EX_for(e)-FORtex( $×$ ), PFL( $×$ ), PYK( $×$ )
3	Act2rpp( $×$ ), ACtex-EX_ac(e)( $×$ )

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