Molecular Biotechnology

, Volume 61, Issue 12, pp 873–891 | Cite as

Design of Experiments As a Tool for Optimization in Recombinant Protein Biotechnology: From Constructs to Crystals

  • Christos PapaneophytouEmail author


In this review, the basic concepts and applications of design of experiments (DoE) in recombinant protein biotechnology will be discussed. The production of recombinant proteins usually begins with the construction of an expression vector that is then introduced into a microbial host. The target protein is overexpressed in the host’s cells and subsequently, it is isolated using a suitable purification method, its activity is assessed using a biological assay, while its crystallization is often required. Because each protein is unique and due to the complex interactions among the reagents in experiments, it is impossible that one set of reaction conditions would be optimal for all cases. Optimization of experimental conditions is usually carried out by the inefficient one-factor-at-a-time approach that does not take into account the combined effects of factors on a process. On the other hand, DoE approaches with a carefully selected small set of experiments, and therefore with a reduced cost and in a limited amount of time predict the effect of each factor and the effects of their interactions on a process. Importantly, several software packages are available that facilitate the choice of the DoE approach, design of the experiments, and analysis of the results.


Design of experiments Recombinant proteins Optimization Response surface methodology 



  1. 1.
    Palomares, L. A., Estrada-Mondaca, S., & Ramirez, O. T. (2004). Production of recombinant proteins: Challenges and solutions. Methods in Molecular Biology, 267, 15–52.PubMedGoogle Scholar
  2. 2.
    Leader, B., Baca, Q. J., & Golan, D. E. (2008). Protein therapeutics: a summary and pharmacological classification. Nature Reviews Drug Discovery, 7, 21–39.CrossRefGoogle Scholar
  3. 3.
    Kesik-Brodacka, M. (2018). Progress in biopharmaceutical development. Biotechnology and Applied Biochemistry, 65, 306–322.CrossRefGoogle Scholar
  4. 4.
    Jozala, A. F., Geraldes, D. C., Tundisi, L. L., Feitosa, V. A., Breyer, C. A., Cardoso, S. L., et al. (2016). Biopharmaceuticals from microorganisms: From production to purification. Brazilian Journal of Microbiology, 47(Suppl 1), 51–63.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Basu, A., Li, X., & Leong, S. S. (2011). Refolding of proteins from inclusion bodies: Rational design and recipes. Applied Microbiology and Biotechnology, 92, 241–251.CrossRefGoogle Scholar
  6. 6.
    Sanchez-Garcia, L., Martín, L., Mangues, R., Ferrer-Miralles, N., Vázquez, E., & Villaverde, A. (2016). Recombinant pharmaceuticals from microbial cells: A 2015 update. Microbial Cell Factories, 15, 33.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Kim, Y., Bigelow, L., Borovilos, M., Dementieva, I., Duggan, E., Eschenfeldt, W., et al. (2008). High-throughput protein purification for x-ray crystallography and NMR. Advances in Protein Chemistry and Structural Biology, 75, 85–105.PubMedGoogle Scholar
  8. 8.
    Tralau-Stewart, C. J., Wyatt, C. A., Kleyn, D. E., & Ayad, A. (2009). Drug discovery: New models for industry–academic partnerships. Drug Discovery Today, 14, 95–101.CrossRefGoogle Scholar
  9. 9.
    Structural Genomics, C., Architecture et Fonction des Macromolécules, B., Berkeley Structural Genomics, C., China Structural Genomics, C., Integrated Center for, S., Function, I., Israel Structural Proteomics, C., Joint Center for Structural, G., Midwest Center for Structural, G., New York Structural Genomi, X. R. C. f. S. G., Northeast Structural Genomics, C., Oxford Protein Production, F., Protein Sample Production Facility, M. D. C. f. M. M., Initiative, R. S. G. P. and Complexes, S. (2008). Protein production and purification. Nature Methods, 5, 135–146.CrossRefGoogle Scholar
  10. 10.
    Khan, K. H. (2013). Gene expression in mammalian cells and its applications. Advanced Pharmaceutical Bulletin, 3, 257–263.PubMedPubMedCentralGoogle Scholar
  11. 11.
    Walsh, G. (2014). Biopharmaceutical benchmarks 2014. Nature Biotechnology, 32, 992–1000.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Marisch, K., Bayer, K., Cserjan-Puschmann, M., Luchner, M., & Striedner, G. (2013). Evaluation of three industrial Escherichia coli strains in fed-batch cultivations during high-level SOD protein production. Microbial Cell Factories, 12, 58.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Long, X., Gou, Y., Luo, M., Zhang, S., Zhang, H., Bai, L., et al. (2015). Soluble expression, purification, and characterization of active recombinant human tissue plasminogen activator by auto-induction in E. coli. BMC Biotechnology, 15, 13.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Rosano, G. L., & Ceccarelli, E. A. (2014). Recombinant protein expression in Escherichia coli: Advances and challenges. Frontiers in Microbiology, 5, 172.PubMedPubMedCentralGoogle Scholar
  15. 15.
    Hughes, R. A., Miklos, A. E., & Ellington, A. D. (2011). Gene synthesis: Methods and applications. Methods in Enzymology, 498, 277–309.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Jia, B., & Jeon, C. O. (2016). High-throughput recombinant protein expression in Escherichia coli: current status and future perspectives. Open Biology, 6, 160196.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Young, C. L., Britton, Z. T., & Robinson, A. S. (2012). Recombinant protein expression and purification: A comprehensive review of affinity tags and microbial applications. Biotechnology Journal, 7, 620–634.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Papaneophytou, C. P., & Kontopidis, G. (2014). Statistical approaches to maximize recombinant protein expression in Escherichia coli: A general review. Protein Expression and Purification, 94, 22–32.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Lee, K. M., & Gilmore, D. F. (2006). Statistical experimental design for bioprocess modeling and optimization analysis: Repeated-measures method for dynamic biotechnology process. Applied Biochemistry and Biotechnology, 135, 101–116.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Chambers, S. P., & Swalley, S. E. (2009). Designing experiments for high-throughput protein expression. In S. A. Doyle (Ed.), High throughput protein expression and purification: Methods and protocols (pp. 19–29). Totowa, NJ: Humana Press.CrossRefGoogle Scholar
  21. 21.
    Oxford English Dictionary. (2008). Oxford: Oxford University Press.Google Scholar
  22. 22.
    Jeff Wu, C.-F., & Hamada, M. (2000). Experiments: Planning, analysis, and parameter design optimization. Hoboken: Wiley.Google Scholar
  23. 23.
    Rodrigues, M., & Francisco Iemma, A. (2014). Experimental design and process optimization. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  24. 24.
    Bora, N., Bawa, Z., Bill, R. M., & Wilks, M. D. (2012). The implementation of a design of experiments strategy to increase recombinant protein yields in yeast (review). Methods in Molecular Biology, 866, 115–127.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Mandenius, C. F., & Brundin, A. (2008). Bioprocess optimization using design-of-experiments methodology. Biotechnology Progress, 24, 1191–1203.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Weissman, S. A., & Anderson, N. G. (2015). Design of experiments (DoE) and process optimization. A review of recent publications. Organic Process Research & Development, 19, 1605–1633.CrossRefGoogle Scholar
  27. 27.
    Swalley, S. E., Fulghum, J. R., & Chambers, S. P. (2006). Screening factors effecting a response in soluble protein expression: Formalized approach using design of experiments. Analytical Biochemistry, 351, 122–127.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Hicks, C. R., & Turner, K. V. (1999). Fundamental concepts in the design of experiments. New York: Oxford University Press.Google Scholar
  29. 29.
    Box, G. E., Hunter, J. S., & Hunter, W. G. (2005). Statistics for experimenters. Hoboken: Wiley.Google Scholar
  30. 30.
    Montgomery, D. C. (2008). Design and analysis of experiments. New York: Wiley.Google Scholar
  31. 31.
    Vijesh, K., Akriti, B., & Rathore, A. S. (2014). Design of experiments applications in bioprocessing: Concepts and approach. Biotechnology Progress, 30, 86–99.CrossRefGoogle Scholar
  32. 32.
    Marini, G., Luchese, M. D., Argondizzo, A. P. C., de Góes, A. C. M. A., Galler, R., Alves, T. L. M., et al. (2014). Experimental design approach in recombinant protein expression: determining medium composition and induction conditions for expression of pneumolysin from Streptococcus pneumoniae in Escherichia coli and preliminary purification process. BMC Biotechnology, 14, 1.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Box, G. E. P., & Hunter, J. S. (1961). The 2k−p fractional factorial designs. Technometrics, 3, 311–351.Google Scholar
  34. 34.
    Plackett, R. L., & Burman, J. P. (1946). The design of optimum multifactorial experiments. Biometrika, 33, 305–325.CrossRefGoogle Scholar
  35. 35.
    Phadke, M. S. (1989). Quality engineering using robust design. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  36. 36.
    Cavazzuti, M. (2013). Optimization methods: From theory to design. Berlin: Springer.CrossRefGoogle Scholar
  37. 37.
    Karna, S., & Sahai, R. (2012). An overview on Taguchi method. IJEMS, 1, 1–7.Google Scholar
  38. 38.
    Daniel, C. (1959). Use of half-normal plots in interpreting factorial two-level experiments. Technometrics, 1, 311–341.CrossRefGoogle Scholar
  39. 39.
    Shah, M., & Pathak, K. (2010). Development and statistical optimization of solid lipid nanoparticles of simvastatin by using 23 full-factorial design. An Official Journal of the American Association of Pharmaceutical Scientists, 11, 489–496.Google Scholar
  40. 40.
    Uhoraningoga, A., Kinsella, G. K., Henehan, G. T., & Ryan, B. J. (2018). The goldilocks approach: A Review of employing design of experiments in prokaryotic recombinant protein production. Bioengineering (Basel), 5, E89.CrossRefGoogle Scholar
  41. 41.
    Leardi, R. (2009). Experimental design in chemistry: A tutorial. Analytica Chimica Acta, 652, 161–172.CrossRefGoogle Scholar
  42. 42.
    Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76, 965–977.CrossRefGoogle Scholar
  43. 43.
    Luzier, W. D. (1992). Materials derived from biomass biodegradable materials. Proceedings of the National academy of Sciences of the United States of America, 89, 839–842.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Carter, C. W., Jr., & Carter, C. W. (1979). Protein crystallization using incomplete factorial experiments. Journal of Biological Chemistry, 254, 12219–12223.PubMedGoogle Scholar
  45. 45.
    Carter, C. W. (1990). Efficient factorial designs and the analysis of macromolecular crystal growth conditions. Methods, 1, 12–24.CrossRefGoogle Scholar
  46. 46.
    Collins, L. M., Dziak, J. J., & Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods, 14, 202–224.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Byar, D. P., Herzberg, A. M., & Tan, W. Y. (1993). Incomplete factorial designs for randomized clinical trials. Statistics in Medicine, 12(17), 1629–1641.CrossRefGoogle Scholar
  48. 48.
    Marchuk, D., Drumm, M., Saulino, A., & Collins, F. S. (1991). Construction of T-vectors, a rapid and general system for direct cloning of unmodified PCR products. Nucleic Acids Research, 19, 1154.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Weeks, S. D., Drinker, M., & Loll, P. J. (2007). Ligation independent cloning vectors for expression of SUMO fusions. Protein Expression and Purification, 53, 40–50.CrossRefGoogle Scholar
  50. 50.
    Rashtchian, A., Thornton, C. G., & Heidecker, G. (1992). A novel method for site-directed mutagenesis using PCR and uracil DNA glycosylase. PCR Methods and Applications, 2, 124–130.CrossRefGoogle Scholar
  51. 51.
    Walhout, A. J. M., Temple, G. F., Brasch, M. A., Hartley, J. L., Lorson, M. A., van den Heuvel, S., et al. (2000). GATEWAY recombinational cloning: Application to the cloning of large numbers of open reading frames or ORFeomes. Methods in Enzymology, 328, 575–592.CrossRefGoogle Scholar
  52. 52.
    Cheo, D. L., Titus, S. A., Byrd, D. R., Hartley, J. L., Temple, G. F., & Brasch, M. A. (2004). Concerted assembly and cloning of multiple DNA segments using in vitro site-specific recombination: Functional analysis of multi-segment expression clones. Genome Research, 14, 2111–2120.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Court, D. L., Sawitzke, J. A., & Thomason, L. C. (2002). Genetic engineering using homologous recombination. Annual Review of Genetics, 36, 361–388.CrossRefGoogle Scholar
  54. 54.
    Zuo, P., & Rabie, B. M. (2010). One-step DNA fragment assembly and circularization for gene cloning. Current Issues in Molecular Biology, 12, 11–16.PubMedGoogle Scholar
  55. 55.
    Shuldiner, A. R., Scott, L. A., & Roth, J. (1990). PCR-induced (ligase-free) subcloning: A rapid reliable method to subclone polymerase chain reaction (PCR) products. Nucleic Acids Research, 18, 1920–1920.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Shuldiner, A. R., Tanner, K., Scott, L. A., Moore, C. A., & Roth, J. (1991). Ligase-free subcloning: A versatile method to subclone polymerase chain reaction (PCR) products in a single day. Analytical Biochemistry, 194, 9–15.CrossRefGoogle Scholar
  57. 57.
    Mitchell, D. B., Ruggli, N., & Tratschin, J. D. (1992). An improved method for cloning PCR fragments. PCR Methods and Applications, 2, 81–82.CrossRefGoogle Scholar
  58. 58.
    Mead, D. A., Pey, N. K., Herrnstadt, C., Marcil, R. A., & Smith, L. M. (1991). A universal method for the direct cloning of PCR amplified nucleic acid. Biotechnology, 9, 657–663.PubMedGoogle Scholar
  59. 59.
    Guo, B., & Bi, Y. (2002). Cloning PCR products. In B.-Y. Chen & H. W. Janes (Eds.), PCR cloning protocols (pp. 111–119). Totowa, NJ: Humana Press.CrossRefGoogle Scholar
  60. 60.
    Tan, S. C., & Yiap, B. C. (2009). DNA, RNA, and protein extraction: The past and the present. Journal of Biomedicine and Biotechnology, 2009, 10.CrossRefGoogle Scholar
  61. 61.
    Roux, K. H. (2009). Optimization and troubleshooting in PCR. New York: Cold Spring Harbor Protocols.CrossRefGoogle Scholar
  62. 62.
    Boleda, M. D., Briones, P., Farrés, J., Tyfield, L., & Pi, R. (1996). Experimental design: A useful tool for PCR optimization. BioTechniques, 21, 134–140.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Benčina, M. (2002). Optimisation of multiple PCR using a combination of full factorial design and three-dimensional simplex optimisation method. Biotechnology Letters, 24, 489–495.CrossRefGoogle Scholar
  64. 64.
    Besseris, G. J. (2014). A fast-and-robust profiler for improving polymerase chain reaction diagnostics. PLoS ONE, 9, e108973.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Wadle, S., Lehnert, M., Rubenwolf, S., Zengerle, R., & von Stetten, F. (2016). Real-time PCR probe optimization using design of experiments approach. Biomolecular Detection and Quantification, 7, 1–8.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Hui, K., & Feng, Z. P. (2013). Efficient experimental design and analysis of real-time PCR assays. Channels, 7, 160–170.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Dorazio, R. M., & Hunter, M. E. (2015). Statistical models for the analysis and design of digital polymerase chain reaction (dPCR) experiments. Analytical Chemistry, 87, 10886–10893.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Smith, D. R. (1993). Restriction endonuclease digestion of DNA. Methods in Molecular Biology, 18, 427–431.PubMedPubMedCentralGoogle Scholar
  69. 69.
    Ng, D. T. W., & Sarkar, C. A. (2012). Model-guided ligation strategy for optimal assembly of DNA libraries. Protein Engineering, Design & Selection, 25, 669–678.CrossRefGoogle Scholar
  70. 70.
    Dugaiczyk, A., Boyer, H. W., & Goodman, H. M. (1975). Ligation of EcoRI endonuclease-generated DNA fragments into linear and circular structures. Journal of Molecular Biology, 96, 171–184.CrossRefGoogle Scholar
  71. 71.
    Legerski, R. J., & Robberson, D. L. (1985). Analysis and optimization of recombinant DNA joining reactions. Journal of Molecular Biology, 181, 297–312.CrossRefGoogle Scholar
  72. 72.
    Revie, D., Smith, D. W., & Yee, T. W. (1988). Kinetic analysis for optimization of DNA ligation reactions. Nucleic Acids Research, 16, 10301–10321.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Dardel, F. (1988). Computer simulation of DNA ligation: Determination of initial DNA concentrations favouring the formation of recombinant molecules. Nucleic Acids Research, 16, 1767–1778.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Chandra, P. K., & Wikel, S. K. (2005). Analyzing ligation mixtures using a PCR based method. Biological Procedures Online, 7, 93–100.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Thomason, L. C., Sawitzke, J. A., Li, X., Costantino, N., & Court, D. L. (2014). Recombineering: Genetic engineering in bacteria using homologous recombination. Current Protocols in Molecular Biology, 106, 39.Google Scholar
  76. 76.
    Chan, W.-T., Verma, Chandra S., Lane, David P., & Gan, Samuel K.-E. (2013). A comparison and optimization of methods and factors affecting the transformation of Escherichia coli. Bioscience Reports, 33, e00086.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Dower, W. J., Miller, J. F., & Ragsdale, C. W. (1988). High efficiency transformation of E. coli by high voltage electroporation. Nucleic Acids Research, 16, 6127–6145.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Aune, T. E. V., & Aachmann, F. L. (2010). Methodologies to increase the transformation efficiencies and the range of bacteria that can be transformed. Applied Microbiology and Biotechnology, 85, 1301–1313.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Tang, X., Nakata, Y., Li, H. O., Zhang, M., Gao, H., Fujita, A., et al. (1994). The optimization of preparations of competent cells for transformation of E. coli. Nucleic Acids Research, 22, 2857–2858.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Yildirim, S., Thompson, M. G., Jacobs, A. C., Zurawski, D. V., & Kirkup, B. C. (2016). Evaluation of parameters for high efficiency transformation of Acinetobacter baumannii. Scientific Reports, 6, 22110.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Hartley, D. L., & Kane, J. F. (1988). Properties of inclusion bodies from recombinant Escherichia coli. Biochemical Society Transactions, 16, 101–102.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Carrió, M. M., & Villaverde, A. (2002). Construction and deconstruction of bacterial inclusion bodies. Journal of Biotechnology, 96, 3–12.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Ferrer-Miralles, N., Domingo-Espín, J., Corchero, J. L., Vázquez, E., & Villaverde, A. (2009). Microbial factories for recombinant pharmaceuticals. Microbial Cell Factories, 8, 17.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Makino, T., Skretas, G., & Georgiou, G. (2011). Strain engineering for improved expression of recombinant proteins in bacteria. Microbial Cell Factories, 10, 32.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Sørensen, H. P., & Mortensen, K. K. (2005). Soluble expression of recombinant proteins in the cytoplasm of Escherichia coli. Microbial Cell Factories, 4, 1.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Schein, C. H., & Noteborn, M. H. M. (1988). Formation of soluble recombinant proteins in Escherichia coli is favored by lower growth temperature. Biotechnology, 6, 291–294.Google Scholar
  87. 87.
    Ramirez, O. T., Zamora, R., Espinosa, G., Merino, E., Bolivar, F., & Quintero, R. (1994). Kinetic-study of penicillin acylase production by recombinant Escherichia Coli in batch cultures. Process Biochemistry, 29, 197–206.CrossRefGoogle Scholar
  88. 88.
    Shaw, M. K., & Ingraham, J. L. (1967). Synthesis of macromolecules by Escherichia coli near the minimal temperature for growth. Journal of Bacteriology, 94, 157–164.PubMedPubMedCentralGoogle Scholar
  89. 89.
    Galloway, C. A., Sowden, M. P., & Smith, H. C. (2003). Increasing the yield of soluble recombinant protein expressed in E. coli by induction during late log phase. BioTechniques, 34, 524–530.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Ou, J. X., Wang, L., Ding, X. L., Du, J. Y., Zhang, Y., Chen, H. P., et al. (2004). Stationary phase protein overproduction is a fundamental capability of Escherichia coli. Biochemical and Biophysical Research, 314, 174–180.CrossRefGoogle Scholar
  91. 91.
    Waugh, D. S. (2005). Making the most of affinity tags. Trends in Biotechnology, 23, 316–320.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Czitrom, V. (1999). One-factor-at-a-time versus designed experiments. American Statistician, 53, 126–131.Google Scholar
  93. 93.
    Kasli, I. M., Thomas, O. R. T., & Overton, T. W. (2019). Use of a design of experiments approach to optimise production of a recombinant antibody fragment in the periplasm of Escherichia coli: selection of signal peptide and optimal growth conditions. AMB Express, 9, 5.CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Morowvat, M. H., Babaeipour, V., Rajabi Memari, H., & Vahidi, H. (2015). Optimization of fermentation conditions for recombinant human interferon beta production by Escherichia coli using the response surface methodology. Jundishapur Journal of Microbiology, 8, e16236.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Shafiee, F., Rabbani, M., & Jahanian-Najafabadi, A. (2017). Optimization of the expression of DT386-BR2 fusion protein in Escherichia coli using response surface methodology. Advanced Biomedical Research, 6, 22.CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Maharjan, S., Singh, B., Bok, J.-D., Kim, J.-I., Jiang, T., Cho, C.-S., et al. (2014). Exploring codon optimization and response surface methodology to express biologically active transmembrane RANKL in E. coli. PLoS ONE, 9, 96259.CrossRefGoogle Scholar
  97. 97.
    Wang, Y., Wang, Q., Wang, Y., Han, H., Hou, Y., & Shi, Y. (2017). Statistical optimization for the production of recombinant cold-adapted superoxide dismutase in E. coli using response surface methodology. Bioengineered, 8, 693–699.CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Larentis, A. L., Argondizzo, A. P. C., Esteves, Jessouron, Galler, R., & Medeiros, M. A. (2011). Cloning and optimization of induction conditions for mature PsaA (pneumococcal surface adhesin A) expression in Escherichia coli and recombinant protein stability during long-term storage. Protein Expression and Purification, 78, 38–47.CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Larentis, A. L., Nicolau, J. F. M. Q., Argondizzo, A. P. C., Galler, R., Rodrigues, M. I., & Medeiros, M. A. (2012). Optimization of medium formulation and seed conditions for expression of mature PsaA (pneumococcal surface adhesin A) in Escherichia coli using a sequential experimental design strategy and response surface methodology. Journal of Industrial Microbiology and Biotechnology, 39, 897–908.CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Nikerel, İ. E., Toksoy, E., Kırdar, B., & Yıldırım, R. (2005). Optimizing medium composition for TaqI endonuclease production by recombinant Escherichia coli cells using response surface methodology. Process Biochemistry, 40, 1633–1639.CrossRefGoogle Scholar
  101. 101.
    Zhao, J., Wang, Y., Chu, J., Zhang, S., Zhuang, Y., & Yuan, Z. (2008). Statistical optimization of medium for the production of pyruvate oxidase by the recombinant Escherichia coli. Journal of Industrial Microbiology and Biotechnology, 35, 257–262.CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Wang, Y.-H., Jing, C.-F., Yang, B., Mainda, G., Dong, M.-L., & Xu, A.-L. (2005). Production of a new sea anemone neurotoxin by recombinant Escherichia coli: Optimization of culture conditions using response surface methodology. Process Biochemistry, 40, 2721–2728.CrossRefGoogle Scholar
  103. 103.
    Chen, Y., Xing, X.-H., Ye, F., Kuang, Y., & Luo, M. (2007). Production of MBP–HepA fusion protein in recombinant Escherichia coli by optimization of culture medium. Biochemical Engineering Journal, 34, 114–121.CrossRefGoogle Scholar
  104. 104.
    Batumalaie, K., Khalili, E., Mahat, N. A., Huyop, F. Z., & Wahab, R. A. (2018). A statistical approach for optimizing the protocol for overexpressing lipase KV1 in Escherichia coli: Purification and characterization. Biotechnology and Biotechnological Equipment, 32, 69–87.CrossRefGoogle Scholar
  105. 105.
    Volontè, F., Marinelli, F., Gastaldo, L., Sacchi, S., Pilone, M. S., Pollegioni, L., et al. (2008). Optimization of glutaryl-7-aminocephalosporanic acid acylase expression in E. coli. Protein Expression and Purification, 61, 131–137.CrossRefPubMedPubMedCentralGoogle Scholar
  106. 106.
    Papaneophytou, C. P., & Kontopidis, G. A. (2012). Optimization of TNF-alpha overexpression in Escherichia coli using response surface methodology: Purification of the protein and oligomerization studies. Protein Expression and Purification, 86, 35–44.CrossRefPubMedPubMedCentralGoogle Scholar
  107. 107.
    Papaneophytou, C. P., Rinotas, V., Douni, E., & Kontopidis, G. (2013). A statistical approach for optimization of RANKL overexpression in Escherichia coli: Purification and characterization of the protein. Protein Expression and Purification, 90, 9–19.CrossRefPubMedPubMedCentralGoogle Scholar
  108. 108.
    Papaneophytou, C., & Kontopidis, G. (2016). A comparison of statistical approaches used for the optimization of soluble protein expression in Escherichia coli. Protein Expression and Purification, 120, 126–137.CrossRefPubMedPubMedCentralGoogle Scholar
  109. 109.
    Antoniou, G., Papakyriacou, I., & Papaneophytou, C. (2017). Optimization of soluble expression and purification of recombinant human rhinovirus type-14 3C protease using statistically designed experiments: Isolation and characterization of the enzyme. Molecular Biotechnology, 59, 407–424.CrossRefPubMedPubMedCentralGoogle Scholar
  110. 110.
    Abergel, C., Coutard, B., Byrne, D., Chenivesse, S., Claude, J. B., Deregnaucourt, C., et al. (2003). Structural genomics of highly conserved microbial genes of unknown function in search of new antibacterial targets. Journal of Structural and Functional Genomics, 4, 141–157.CrossRefGoogle Scholar
  111. 111.
    Noguere, C., Larsson, A. M., Guyot, J. C., & Bignon, C. (2012). Fractional factorial approach combining 4 Escherichia coli strains, 3 culture media, 3 expression temperatures and 5 N-terminal fusion tags for screening the soluble expression of recombinant proteins. Protein Expression and Purification, 84, 204–213.CrossRefPubMedPubMedCentralGoogle Scholar
  112. 112.
    Asenjo, J. A., & Andrews, B. A. (2009). Protein purification using chromatography: selection of type, modelling and optimization of operating conditions. Journal of Molecular Recognition, 22, 65–76.CrossRefPubMedPubMedCentralGoogle Scholar
  113. 113.
    Uhlen, M., Forsberg, G., Moks, T., Hartmanis, M., & Nilsson, B. (1992). Fusion proteins in biotechnology. Current Opinion in Biotechnology, 3, 363–369.CrossRefPubMedPubMedCentralGoogle Scholar
  114. 114.
    Jenny, R. J., Mann, K. G., & Lundblad, R. L. (2003). A critical review of the methods for cleavage of fusion proteins with thrombin and factor Xa. Protein Expression and Purification, 31, 1–11.CrossRefPubMedPubMedCentralGoogle Scholar
  115. 115.
    Zheng, N., Perez Jde, J., Zhang, Z., Dominguez, E., Garcia, J. A., & Xie, Q. (2008). Specific and efficient cleavage of fusion proteins by recombinant plum pox virus NIa protease. Protein Expression and Purification, 57, 153–162.CrossRefPubMedPubMedCentralGoogle Scholar
  116. 116.
    Amadeo, I., Mauro, L., Ortí, E., & Forno, G. (2014). Establishment of a design space for biopharmaceutical purification processes using DoE. In N. E. Labrou (Ed.), Protein downstream processing: Design, development and application of high and low-resolution methods (pp. 11–27). Totowa, NJ: Humana Press.CrossRefGoogle Scholar
  117. 117.
    Shin, H. S., & Cha, H. J. (2003). Statistical optimization for immobilized metal affinity purification of secreted human erythropoietin from Drosophila S2 cells. Protein Expression and Purification, 28, 331–339.CrossRefGoogle Scholar
  118. 118.
    Song, Y. H., Sun, X. W., Jiang, B., Liu, J. E., & Su, X. H. (2015). Purification optimization for a recombinant single-chain variable fragment against type 1 insulin-like growth factor receptor (IGF-1R) by using design of experiment (DoE). Protein Expression and Purification, 116, 98–104.CrossRefGoogle Scholar
  119. 119.
    Amadeo, I., Mauro, L. V., Orti, E., & Forno, G. (2011). Determination of robustness and optimal work conditions for a purification process of a therapeutic recombinant protein using response surface methodology. Biotechnology Progress, 27, 724–732.CrossRefGoogle Scholar
  120. 120.
    Shahbaz Mohammadi, H., Mostafavi, S. S., Soleimani, S., Bozorgian, S., Pooraskari, M., & Kianmehr, A. (2015). Response surface methodology to optimize partition and purification of two recombinant oxidoreductase enzymes, glucose dehydrogenase and d-galactose dehydrogenase in aqueous two-phase systems. Protein Expression and Purification, 108, 41–47.CrossRefPubMedPubMedCentralGoogle Scholar
  121. 121.
    Azar, S. R., Naiebi, R., Homami, A., Akbari, Z., Kianmehr, A., Mahdizadehdehosta, R., et al. (2015). Expression and response surface optimization of the recovery and purification of recombinant d-galactose dehydrogenase from Pseudomonas fluorescens. Indian Journal of Biochemistry & Biophysics, 52, 68–74.Google Scholar
  122. 122.
    Altekar, M., Homon, C. A., Kashem, M. A., Mason, S. W., Nelson, R. M., Patnaude, L. A., et al. (2007). Assay optimization: A statistical design of experiments approach. Clinics in Laboratory Medicine, 27, 139–154.CrossRefPubMedPubMedCentralGoogle Scholar
  123. 123.
    Andricopulo, A. D., Salum, L. B., & Abraham, D. J. (2009). Structure-based drug design strategies in medicinal chemistry. Current Topics in Medicinal Chemistry, 9, 771–790.CrossRefPubMedPubMedCentralGoogle Scholar
  124. 124.
    Guido, R. V., Oliva, G., & Andricopulo, A. D. (2008). Virtual screening and its integration with modern drug design technologies. Current Medicinal Chemistry, 15, 37–46.CrossRefPubMedPubMedCentralGoogle Scholar
  125. 125.
    DeSilva, B., Smith, W., Weiner, R., Kelley, M., Smolec, J., Lee, B., et al. (2003). Recommendations for the bioanalytical method validation of ligand-binding assays to support pharmacokinetic assessments of macromolecules. Pharmaceutical Research, 20, 1885–1900.CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Papaneophytou, C. P., Mettou, A. K., Rinotas, V., Douni, E., & Kontopidis, G. A. (2013). Solvent selection for insoluble ligands, a challenge for biological assay development: A TNF-alpha/SPD304 study. ACS Medicinal Chemistry Letters, 4, 137–141.CrossRefPubMedPubMedCentralGoogle Scholar
  127. 127.
    Papaneophytou, C. P., Grigoroudis, A. I., McInnes, C., & Kontopidis, G. (2014). Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity. ACS Medicinal Chemistry Letters, 5, 931–936.CrossRefPubMedPubMedCentralGoogle Scholar
  128. 128.
    Cowan, K. J., Erickson, R., Sue, B., Delarosa, R., Gunter, B., Coleman, D. A., et al. (2012). Utilizing design of experiments to characterize assay robustness. Bioanalysis, 4, 2127–2139.CrossRefPubMedPubMedCentralGoogle Scholar
  129. 129.
    Onyeogaziri, F. C., & Papaneophytou, C. (2019). A general guide for the optimization of enzyme assay conditions using the design of experiments approach. SLAS Discovery, 24, 587–596.PubMedPubMedCentralGoogle Scholar
  130. 130.
    Bisswanger, H. (2014). Enzyme assays. Perspectives on Science, 1, 41–55.CrossRefGoogle Scholar
  131. 131.
    DOE in Assay Development Trends 2009 Report, published by HTStec Limited, Cambridge, UK, 18 July 2009.Google Scholar
  132. 132.
    Zhang, J. H., Chung, T. D., & Oldenburg, K. R. (1999). A simple statistical parameter for use in evaluation and validation of high throughput screening assays. Journal of Biomolecular Screening, 4, 67–73.CrossRefGoogle Scholar
  133. 133.
    Boyacı, İ. H. (2005). A new approach for determination of enzyme kinetic constants using response surface methodology. Biochemical Engineering Journal, 25, 55–62.CrossRefGoogle Scholar
  134. 134.
    Fang, H., Dong, H., Cai, T., Zheng, P., Li, H., Zhang, D., et al. (2016). In vitro optimization of enzymes involved in precorrin-2 synthesis using response surface methodology. PLoS ONE, 11, e0151149.CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Buss, O., Jager, S., Dold, S. M., Zimmermann, S., Hamacher, K., Schmitz, K., et al. (2016). Statistical evaluation of HTS assays for enzymatic hydrolysis of beta-keto esters. PLoS ONE, 11, e0146104.CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Chen, X. C., Zhou, L., Gupta, S., & Civoli, F. (2012). Implementation of design of experiments (DOE) in the development and validation of a cell-based bioassay for the detection of anti-drug neutralizing antibodies in human serum. Journal of Immunological Methods, 376, 32–45.CrossRefGoogle Scholar
  137. 137.
    Sittampalam, G. S., Smith, W. C., Miyakawa, T. W., Smith, D. R., & McMorris, C. (1996). Application of experimental design techniques to optimize a competitive ELISA. Journal of Immunological Methods, 190, 151–161.CrossRefGoogle Scholar
  138. 138.
    Hammond, O., Reynolds, J., Rubinstein, L. J., Sikkema, D., & Marchese, R. D. (2008). Complexities of clinical assay development and optimization prior to first-in-man immunization trials—A description of immunogenicity assay development for the testing of samples from a phase 1 Alzheimer’s vaccine trial. Journal of Immunoassay & Immunochemistry, 29, 332–347.CrossRefGoogle Scholar
  139. 139.
    Ray, C. A., Patel, V., Shih, J., Macaraeg, C., Wu, Y., Thway, T., et al. (2009). Application of multi-factorial design of experiments to successfully optimize immunoassays for robust measurements of therapeutic proteins. Journal of Pharmaceutical and Biomedical Analysis, 49, 311–318.CrossRefGoogle Scholar
  140. 140.
    Mikulskis, A., Yeung, D., Subramanyam, M., & Amaravadi, L. (2011). Solution ELISA as a platform of choice for development of robust, drug tolerant immunogenicity assays in support of drug development. Journal of Immunological Methods, 365, 38–49.CrossRefGoogle Scholar
  141. 141.
    Joelsson, D., Moravec, P., Troutman, M., Pigeon, J., & DePhillips, P. (2008). Optimizing ELISAs for precision and robustness using laboratory automation and statistical design of experiments. Journal of Immunological Methods, 337, 35–41.CrossRefGoogle Scholar
  142. 142.
    Schmidt, T., Bergner, A., & Schwede, T. (2014). Modelling three-dimensional protein structures for applications in drug design. Drug Discovery Today, 19, 890–897.CrossRefPubMedPubMedCentralGoogle Scholar
  143. 143.
    Kwon, J. S., II, Nayhouse, M., Christofides, P. D., & Orkoulas, G. (2014). Protein crystal shape and size control in batch crystallization: Comparing model predictive control with conventional operating policies. Industrial and Engineering Chemistry Research, 53, 5002–5014.CrossRefGoogle Scholar
  144. 144.
    Jancarik, J., & Kim, S.-H. (1991). Sparse matrix sampling: A screening method for crystallization of proteins. Journal of Applied Crystallography, 24, 409–411.CrossRefGoogle Scholar
  145. 145.
    Stevens, R. C. (2000). High-throughput protein crystallization. Current Opinion in Structural Biology, 10, 558–563.CrossRefPubMedPubMedCentralGoogle Scholar
  146. 146.
    Giege, R. (2013). A historical perspective on protein crystallization from 1840 to the present day. FEBS Journal, 280, 6456–6497.CrossRefPubMedPubMedCentralGoogle Scholar
  147. 147.
    Abergel, C., Moulard, M., Moreau, H., Loret, E., Cambillau, C., & Fontecilla-Camps, J. C. (1991). Systematic use of the incomplete factorial approach in the design of protein crystallization experiments. Journal of Biological Chemistry, 266, 20131–20138.PubMedPubMedCentralGoogle Scholar
  148. 148.
    Doudna, J. A., Grosshans, C., Gooding, A., & Kundrot, C. E. (1993). Crystallization of ribozymes and small RNA motifs by a sparse matrix approach. Proceedings of the National Academy of Sciences of the United States of America, 90, 7829–7833.CrossRefPubMedPubMedCentralGoogle Scholar
  149. 149.
    Luft, J. R., Newman, J., & Snell, E. H. (2014). Crystallization screening: the influence of history on current practice. Archive of Acta Crystallographica Section F, Structural Biology Communications, 70, 835–853.CrossRefGoogle Scholar
  150. 150.
    Snell, E. H., Nagel, R. M., Wojtaszcyk, A., O’Neill, H., Wolfley, J. L., & Luft, J. R. (2008). The application and use of chemical space mapping to interpret crystallization screening results. Acta Crystallographica. Section D, Biological Crystallography, 64, 1240–1249.CrossRefPubMedPubMedCentralGoogle Scholar
  151. 151.
    Dinc, I., Pusey, M. L., & Aygun, R. S. (2016). Optimizing associative experimental design for protein crystallization screening. IEEE Transactions on NanoBioscience, 15, 101–112.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Life and Health Sciences, School of Sciences and EngineeringUniversity of NicosiaNicosiaCyprus

Personalised recommendations