Quantitative Biology

, Volume 3, Issue 3, pp 107–114 | Cite as

Cis-acting regulatory elements: from random screening to quantitative design

  • Hailin Meng
  • Yong Wang


The cis-acting regulatory elements, e.g., promoters and ribosome binding sites (RBSs) with various desired properties, are building blocks widely used in synthetic biology for fine tuning gene expression. In the last decade, acquisition of a controllable regulatory element from a random library has been established and applied to control the protein expression and metabolic flux in different chassis cells. However, more rational strategies are still urgently needed to improve the efficiency and reduce the laborious screening and multifaceted characterizations. Building precise computational models that can predict the activity of regulatory elements and quantitatively design elements with desired strength have been demonstrated tremendous potentiality. Here, recent progress on construction of cisacting regulatory element library and the quantitative predicting models for design of such elements are reviewed and discussed in detail.


cis-acting regulatory element quantitative design synthetic biology random mutation modeling 


  1. 1.
    De Mey, M., Maertens, J., Lequeux, G. J., Soetaert, W. K. and Vandamme, E. J. (2007) Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering. BMC Biotechnol., 7, 34PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Meng, H., Wang, J., Xiong, Z., Xu, F., Zhao, G. and Wang, Y. (2013) Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network. PLoS One, 8, e60288PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Wang, J., Meng, H., Xiong, Z. and Wang, Y. (2013) Design and construction of artificial biological systems for complex natural products biosynthesis. Chinese J. Biotech. (in Chinese), 29, 1146–1160Google Scholar
  4. 4.
    Rhodius, V. A. and Mutalik, V. K. (2010) Predicting strength and function for promoters of the Escherichia coli alternative sigma factor, σE. Proc. Natl. Acad. Sci. USA, 107, 2854–2859PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Salis, H. M., Mirsky, E. A. and Voigt, C. A. (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol., 27, 946–950PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Canton, B., Labno, A. and Endy, D. (2008) Refinement and standardization of synthetic biological parts and devices. Nat. Biotechnol., 26, 787–793CrossRefPubMedGoogle Scholar
  7. 7.
    Yuan, Y., Liu, B., Xie, P., Zhang, M. Q., Li, Y., Xie, Z. and Wang, X. (2015) Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc. Natl. Acad. Sci. USA, 112, 3158–3163PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Qi, L., Haurwitz, R. E., Shao, W., Doudna, J. A. and Arkin, A. P. (2012) RNA processing enables predictable programming of gene expression. Nat. Biotechnol., 30, 1002–1006CrossRefPubMedGoogle Scholar
  9. 9.
    Alper, H., Fischer, C., Nevoigt, E. and Stephanopoulos, G. (2005) Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA, 102, 12678–12683PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Wong, W.W., Tsai, T. Y. and Liao, J. C. (2007) Single-cell zeroth-order protein degradation enhances the robustness of synthetic oscillator. Mol. Syst. Biol., 3, 130PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Yamanishi, M., Ito, Y., Kintaka, R., Imamura, C., Katahira, S., Ikeuchi, A., Moriya, H. and Matsuyama, T. (2013) A genome-wide activity assessment of terminator regions in Saccharomyces cerevisiae provides a “terminatome” toolbox. ACS Synth. Biol., 2, 337–347CrossRefPubMedGoogle Scholar
  12. 12.
    Siegl, T., Tokovenko, B., Myronovskyi, M. and Luzhetskyy, A. (2013) Design, construction and characterisation of a synthetic promoter library for fine-tuned gene expression in actinomycetes. Metab. Eng., 19, 98–106CrossRefPubMedGoogle Scholar
  13. 13.
    Qin, X., Qian, J., Yao, G., Zhuang, Y., Zhang, S. and Chu, J. (2011) GAP promoter library for fine-tuning of gene expression in Pichia pastoris. Appl. Environ. Microbiol., 77, 3600–3608PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q. A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P., et al. (2013) Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods, 10, 354–360CrossRefPubMedGoogle Scholar
  15. 15.
    Brewster, R. C., Jones, D. L. and Phillips, R. (2012) Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. PLoS Comput. Biol., 8, e1002811PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Na, D., Lee, S. and Lee, D. (2010) Mathematical modeling of translation initiation for the estimation of its efficiency to computationally design mRNA sequences with desired expression levels in prokaryotes. BMC Syst. Biol., 4, 71PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Juven-Gershon, T., Cheng, S. and Kadonaga, J. T. (2006) Rational design of a super core promoter that enhances gene expression. Nat. Methods, 3, 917–922CrossRefPubMedGoogle Scholar
  18. 18.
    Vilar, J. M. (2010) Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation. Biophys. J., 99, 2408–2413PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Na, D. and Lee, D. (2010) RBSDesigner: software for designing synthetic ribosome binding sites that yields a desired level of protein expression. Bioinformatics, 26, 2633–2634CrossRefPubMedGoogle Scholar
  20. 20.
    Nishikata, K., Cox, R. S., Shimoyama, S., Yoshida, Y., Matsui, M., Makita, Y. and Toyoda, T. (2014) Database construction for PromoterCAD: synthetic promoter design for mammals and plants. ACS Synth. Biol., 3, 192–196CrossRefPubMedGoogle Scholar
  21. 21.
    Cox, R. S., Nishikata, K., Shimoyama, S., Yoshida, Y., Matsui, M., Makita, Y. and Toyoda, T. (2013) PromoterCAD: Data-driven design of plant regulatory DNA. Nucleic Acids Res., 41, W569–W574PubMedCentralCrossRefPubMedGoogle Scholar
  22. 22.
    Ham, T. S., Dmytriv, Z., Plahar, H., Chen, J., Hillson, N. J. and Keasling, J. D. (2012) Design, implementation and practice of JBEIICE: an open source biological part registry platform and tools. Nucleic Acids Res., 40, e141PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
    Jensen, P. R. and Hammer, K. (1998) Artificial promoters for metabolic optimization. Biotechnol. Bioeng., 58, 191–195CrossRefPubMedGoogle Scholar
  24. 24.
    Jensen, P. R. and Hammer, K. (1998) The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. Appl. Environ. Microbiol., 64, 82–87PubMedCentralPubMedGoogle Scholar
  25. 25.
    Andersen, H.W., Pedersen, M. B., Hammer, K. and Jensen, P. R. (2001) Lactate dehydrogenase has no control on lactate production but has a strong negative control on formate production in Lactococcus lactis. Eur. J. Biochem., 268, 6379–6389CrossRefPubMedGoogle Scholar
  26. 26.
    Bakke, I., Berg, L., Aune, T. E., Brautaset, T., Sletta, H., Tøndervik, A. and Valla, S. (2009) Random mutagenesis of the PM promoter as a powerful strategy for improvement of recombinant-gene expression. Appl. Environ. Microbiol., 75, 2002–2011PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Solem, C. and Jensen, P. R. (2002) Modulation of gene expression made easy. Appl. Environ. Microbiol., 68, 2397–2403PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Solem, C., Koebmann, B. J. and Jensen, P. R. (2003) Glyceraldehyde-3- phosphate dehydrogenase has no control over glycolytic flux in Lactococcus lactis MG1363. J. Bacteriol., 185, 1564–1571PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Curran, K. A., Morse, N. J., Markham, K. A., Wagman, A. M., Gupta, A. and Alper, H. S. (2015) Short synthetic terminators for improved heterologous gene expression in yeast. ACS Synth. Biol., 4, 824–832CrossRefPubMedGoogle Scholar
  30. 30.
    De Mey, M., Maertens, J., Boogmans, S., Soetaert, W. K., Vandamme, E. J., Cunin, R. and Foulquié-Moreno, M. R. (2010) Promoter knockin: a novel rational method for the fine tuning of genes. BMC Biotechnol., 10, 26PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Stormo, G. D. (2000) DNA binding sites: representation and discovery. Bioinformatics, 16, 16–23CrossRefPubMedGoogle Scholar
  32. 32.
    Kakumani, R., Devabhaktuni, V. and Ahmad, M. (2008) A two-stage neural network based technique for protein secondary structure prediction. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2008, 1355–1358PubMedGoogle Scholar
  33. 33.
    Qu, W., Sui, H., Yang, B. and Qian, W. (2011) Improving protein secondary structure prediction using a multi-modal BP method. Comput. Biol. Med., 41, 946–959CrossRefPubMedGoogle Scholar
  34. 34.
    Capriotti, E., Fariselli, P. and Casadio, R. (2004) A neural-networkbased method for predicting protein stability changes upon single point mutations. Bioinformatics, 20, i63–i68CrossRefPubMedGoogle Scholar
  35. 35.
    Koessler, D. R., Knisley, D. J., Knisley, J. and Haynes, T. (2010) A predictive model for secondary RNA structure using graph theory and a neural network. BMC Bioinformatics, 11, S21PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Wang, J., Ungar, L. H., Tseng, H. and Hannenhalli, S. (2007) MetaProm: a neural network based meta-predictor for alternative human promoter prediction. BMC Genomics, 8, 374PubMedCentralCrossRefPubMedGoogle Scholar
  37. 37.
    Askary, A., Masoudi-Nejad, A., Sharafi, R., Mizbani, A., Parizi, S. N. and Purmasjedi, M. (2009) N4: a precise and highly sensitive promoter predictor using neural network fed by nearest neighbors. Genes Genet. Syst., 84, 425–430CrossRefPubMedGoogle Scholar
  38. 38.
    de Avila E Silva, S., Gerhardt, G. J. and Echeverrigaray, S. (2011) Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters. Genet. Mol. Biol., 34, 353–360PubMedCentralCrossRefPubMedGoogle Scholar
  39. 39.
    Lou, C., Stanton, B., Chen, Y. J., Munsky, B. and Voigt, C. A. (2012) Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol., 30, 1137–1142PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2015

Authors and Affiliations

  1. 1.Bioengineering Research Center, Guangzhou Institute of Advanced TechnologyChinese Academy of SciencesGuangzhouChina
  2. 2.CAS Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina

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