Quantitative Biology

, Volume 2, Issue 3, pp 100–109 | Cite as

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

  • Honglei Liu
  • Yanda Li
  • Xiaowo WangEmail author
Research Article


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:


metabolic network flux analysis optimization 

Supplementary material

40484_2014_33_MOESM1_ESM.pdf (669 kb)
Supplementary material, approximately 654 KB.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH 2014

Authors and Affiliations

  1. 1.MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, TNLIST/Department of AutomationTsinghua UniversityBeijingChina

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