Systems Metabolic Engineering Approach for Recombinant Protein Production in Microbial Cell Factories

  • Ashish A. Prabhu
  • Kanchan Hariramani
  • P. Lakshmi
  • V. Venkata DasuEmail author


The production of recombinant proteins using genetically engineered microbes are well known. However, integrating systems biology approach such as network-based modeling have enabled to identify all possible pathways that can be rationally engineered to improve protein production and also to reduce the by-product accumulation. Furthermore, by utilizing the insilico systems biology tools, the pathway editing can be easily carried out. Recently the exploration of genome engineering using CRISPR Cas9 technology has enhanced the foreign gene integration as well gene deletion in the genome of several cell factories. Hence, with the systems biology tool and synthetic biology approach superior organism can be created, which has ability to produce the recombinant protein in the range of grams per liter. In the present book chapter, we have discussed the constraint-based methods, which can be used for strain improvement. Further, we have briefly described the in vivo gene manipulation techniques used for bacteria as well as yeast system.


Recombinant proteins Metabolic engineering Network modeling Genome editing 


  1. Ahmad, M., Hirz, M., Pichler, H., & Schwab, H. (2014). Protein expression in Pichia pastoris. Applied Microbiology and Biotechnology, 98, 5301–5317. Scholar
  2. Allen, D. K., Libourel, I. G. L., & Shachar-Hill, Y. (2009). Metabolic flux analysis in plants: Coping with complexity. Plant, Cell and Environment, 32, 1241–1257. Scholar
  3. Almquist, J., Cvijovic, M., Hatzimanikatis, V., Nielsen, J., & Jirstrand, M. (2014). Kinetic models in industrial biotechnology - Improving cell factory performance. Metabolic Engineering, 24, 38–60. Scholar
  4. Andersen, D. C., & Krummen, L. (2002). Recombinant protein expression for therapeutic applications. Current Opinion in Biotechnology, 13, 117–123. Scholar
  5. Antoniewicz, M. R. (2015). Methods and advances in metabolic flux analysis: a mini-review. Journal of Industrial Microbiology and Biotechnology, 42, 317–325. Scholar
  6. Ata, Ö., Boy, E., Güneş, H., & Çalık, P. (2015). Codon optimization of xylA gene for recombinant glucose isomerase production in Pichia pastoris and fed-batch feeding strategies to fine-tune bioreactor performance. Bioprocess and Biosystems Engineering, 38, 889–903. Scholar
  7. Ata, Ö., Prielhofer, R., Gasser, B., Mattanovich, D., & Çalık, P. (2017). Transcriptional engineering of the glyceraldehyde-3-phosphate dehydrogenase promoter for improved heterologous protein production in Pichia pastoris. Biotechnology and Bioengineering, 114, 2319–2327. Scholar
  8. Bellgardt, K. -H. (2000). Bioprocess models. In: K. Schügerl, & K. -H. Bellgardt (Eds.), Bioreaction engineering: Modeling and control (pp. 44–105). Springer Berlin, Heidelberg.
  9. Berg, L., Strand, T. A., Valla, S., & Brautaset, T. (2013). Combinatorial mutagenesis and selection to understand and improve yeast promoters [WWW Document]. BioMed Research International.
  10. Boghigian, B. A., Seth, G., Kiss, R., & Pfeifer, B. A. (2010a). Metabolic flux analysis and pharmaceutical production. Metabolic Engineering 12, 81–95.
  11. Boghigian, B. A., Shi, H., Lee, K., Pfeifer, B. A. (2010b). Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design. BMC Systems Biology 4, 49.
  12. Bordbar, A., Monk, J. M., King, Z. A., & Palsson, B. O. (2014). Constraint-based models predict metabolic and associated cellular functions. Nature Reviews Genetics, 15, 107–120. Scholar
  13. Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003). Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and Bioengineering, 84, 647–657. Scholar
  14. Çalık, P., & Özdamar, T. H. (2011). Bioreaction network flux analysis for industrial microorganisms: A review. Reviews in Chemical Engineering, 18, 553–604. Scholar
  15. Caspi, R., Foerster, H., Fulcher, C. A., Hopkinson, R., Ingraham, J., Kaipa, P., et al. (2006). MetaCyc: A multiorganism database of metabolic pathways and enzymes. Nucleic Acids Research, 34, D511–D516. Scholar
  16. Çelik, E., & Çalık, P. (2012). Production of recombinant proteins by yeast cells. Biotechnology Advances, 30, 1108–1118. Scholar
  17. Çelik, E., Çalık, P., & Oliver, S. G. (2010). Metabolic flux analysis for recombinant protein production by Pichia pastoris using dual carbon sources: Effects of methanol feeding rate. Biotechnology and Bioengineering, 105, 317–329. Scholar
  18. Court, D. L., Sawitzke, J. A., & Thomason, L. C. (2002). Genetic engineering using homologous recombination. Annual Review of Genetics, 36, 361–388. Scholar
  19. Dai, Z., & Locasale, J. W., 2016. Understanding metabolism with flux analysis: From theory to application. Metabolic Engineering
  20. Darvishi, F., Ariana, M., Marella, E. R., & Borodina, I. (2018). Advances in synthetic biology of oleaginous yeast Yarrowia lipolytica for producing non-native chemicals. Applied Microbiology and Biotechnology, 102, 5925–5938. Scholar
  21. Datsenko, K. A., & Wanner, B. L. (2000). One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proceedings of the National Academy of Sciences, 97, 6640–6645. Scholar
  22. Dräger, A., Kronfeld, M., Ziller, M. J., Supper, J., Planatscher, H., Magnus, J. B., Oldiges, M., Kohlbacher, O., & Zell, A. (2009). Modeling metabolic networks in C. glutamicum: A comparison of rate laws in combination with various parameter optimization strategies. BMC Systems Biology 3, 5.
  23. Dumont, J., Euwart, D., Mei, B., Estes, S., & Kshirsagar, R. (2016). Human cell lines for biopharmaceutical manufacturing: history, status, and future perspectives. Critical Reviews in Biotechnology, 36, 1110–1122. Scholar
  24. Edwards, J. S., Covert, M., & Palsson, B. (2002). Metabolic modelling of microbes: The flux-balance approach. Environmental Microbiology, 4, 133–140.CrossRefGoogle Scholar
  25. Faust, K., Croes, D., van Helden, J., 2011. Prediction of metabolic pathways from genome-scale metabolic networks. Biosystems 105, 109–121. In Proceedings of the workshop “Integration of OMICS Datasets into Metabolic Pathway Analysis”. Edinburgh, U. K., 15 October 2010 IOMPA 2010.
  26. Feinberg, M., & Horn, F. J. M. (1974). Dynamics of open chemical systems and the algebraic structure of the underlying reaction network. Chemical Engineering Science, 29, 775–787. Scholar
  27. Feist, A. M., & Palsson, B. O. (2010). The biomass objective function. Current Opinion in Microbiology, 13, 344–349. Scholar
  28. Fiaux, J., Çakar, Z. P., Sonderegger, M., Wüthrich, K., Szyperski, T., & Sauer, U. (2003). Metabolic-flux profiling of the yeasts saccharomyces cerevisiae and Pichia stipitis. Eukaryotic Cell, 2, 170–180. Scholar
  29. Gaj, T., Gersbach, C. A., & Barbas, C. F. (2013). ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends in Biotechnology, 31, 397–405. Scholar
  30. Geng, J., Nielsen, J. (2017). In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models. Current Opinion in Systems Biology, 2, 29–38. In Regulatory and metabolic networks • cancer and systemic diseases.
  31. Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K.-Y., & Kitano, H. (2011). Software for systems biology: From tools to integrated platforms. Nature Reviews Genetics, 12, 821–832. Scholar
  32. Gombert, A. K., & Nielsen, J. (2000). Mathematical modelling of metabolism. Current Opinion in Biotechnology, 11, 180–186. Scholar
  33. Hamilton, C. M., Aldea, M., Washburn, B. K., Babitzke, P., & Kushner, S. R. (1989). New method for generating deletions and gene replacements in Escherichia coli. Journal of Bacteriology, 171, 4617–4622.CrossRefGoogle Scholar
  34. Hartner, F. S., Ruth, C., Langenegger, D., Johnson, S. N., Hyka, P., Lin-Cereghino, G. P., et al. (2008). Promoter library designed for fine-tuned gene expression in Pichia pastoris. Nucleic Acids Research, 36, e76. Scholar
  35. Jacobs, P. P., Geysens, S., Vervecken, W., Contreras, R., & Callewaert, N. (2008). Engineering complex-type N-glycosylation in Pichia pastoris using GlycoSwitch technology. Nature Protocols, 4, 58–70. Scholar
  36. Jakočiūnas, T., Bonde, I., Herrgård, M., Harrison, S. J., Kristensen, M., Pedersen, L. E., et al. (2015). Multiplex metabolic pathway engineering using CRISPR/Cas9 in Saccharomyces cerevisiae. Metabolic Engineering, 28, 213–222. Scholar
  37. Jakočiūnas, T., Jensen, M. K., & Keasling, J. D. (2016). CRISPR/Cas9 advances engineering of microbial cell factories. Metabolic Engineering, 34, 44–59. Scholar
  38. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., et al. (2008). KEGG for linking genomes to life and the environment. Nucleic Acids Research, 36, D480–D484. Scholar
  39. Kauffman, K. J., Prakash, P., & Edwards, J. S. (2003). Advances in flux balance analysis. Current Opinion in Biotechnology, 14, 491–496. Scholar
  40. Kim, J., & Reed, J. L. (2010). OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology, 4, 53. Scholar
  41. Kim, J., Reed, J. L., & Maravelias, C. T. (2011). Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques. PLoS ONE, 6, e24162. Scholar
  42. Kim, T. Y., Sohn, S. B., Kim, Y. B., Kim, W. J., & Lee, S. Y. (2012). Recent advances in reconstruction and applications of genome-scale metabolic models. Current Opinion in Biotechnology, 23, 617–623. In Nanobiotechnology • systems biology.
  43. Klamt, S., Schuster, S., & Gilles, E. D. (2002). Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotechnology and Bioengineering, 77, 734–751.CrossRefGoogle Scholar
  44. Klamt, S., Saez-Rodriguez, J., & Gilles, E. D. (2007). Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Systems Biology, 1, 2. Scholar
  45. Koganesawa, N., Aizawa, T., Masaki, K., Matsuura, A., Nimori, T., Bando, H., et al. (2001). Construction of an expression system of insect lysozyme lacking thermal stability: The effect of selection of signal sequence on level of expression in the Pichia pastoris expression system. Protein Engineering, 14, 705–710.CrossRefGoogle Scholar
  46. Kruger, N. J., & Ratcliffe, R. G. (2015). Fluxes through plant metabolic networks: Measurements, predictions, insights and challenges. Biochemical Journal, 465, 27–38. Scholar
  47. Kumar, N., Pandey, R., Prabhu, A. A., & Venkata Dasu, V. (2018). Genetic and substrate-level modulation of Bacillus subtilis physiology for enhanced extracellular human interferon gamma production. Preparative Biochemistry & Biotechnology, 48, 391–401. Scholar
  48. Lakshmanan, M., Koh, G., Chung, B. K. S., & Lee, D.-Y. (2014). Software applications for flux balance analysis. Briefings in Bioinformatics, 15, 108–122. Scholar
  49. Laukens, B., De Wachter, C., & Callewaert, N. (2015). Engineering the Pichia pastoris N-glycosylation pathway using the glycoswitch technology. In: N. J. Clifton (Ed.), Methods in molecular biology (vol. 1321, pp. 103–122).
  50. Lee, S. Y., Lee, D. -Y., Hong, S. H., Kim, T. Y., Yun, H., Oh, Y. -G., & Park, S. (2003). MetaFluxNet, a program package for metabolic pathway construction and analysis, and its use in large-scale metabolic flux analysis of Escherichia coli. Genome Informatics 14, 23–33. In International Conference on Genome Informatics.Google Scholar
  51. Lee, J. W., Kim, T. Y., Jang, Y.-S., Choi, S., & Lee, S. Y. (2011). Systems metabolic engineering for chemicals and materials. Trends in Biotechnology, 29, 370–378. Scholar
  52. Lee, S., Mattanovich, D., & Villaverde, A. (2012). Systems metabolic engineering, industrial biotechnology and microbial cell factories. Microbial Cell Factories, 11, 156. Scholar
  53. Link, A. J., Phillips, D., & Church, G. M. (1997). Methods for generating precise deletions and insertions in the genome of wild-type Escherichia coli: Application to open reading frame characterization. Journal of Bacteriology, 179, 6228–6237.CrossRefGoogle Scholar
  54. Löbs, A.-K., Schwartz, C., & Wheeldon, I. (2017). Genome and metabolic engineering in non-conventional yeasts: Current advances and applications. Synthetic and Systems Biotechnology, 2, 198–207. Scholar
  55. Looser, V., Bruhlmann, B., Bumbak, F., Stenger, C., Costa, M., Camattari, A., Fotiadis, D., & Kovar, K. (2015). Cultivation strategies to enhance productivity of Pichia pastoris: A review. Biotechnology Advances 33, 1177–1193. In BioTech 2014 and 6th Czech-Swiss Biotechnology Symposium.
  56. Meehl, M. A., & Stadheim, T. A. (2014). Biopharmaceutical discovery and production in yeast. Current Opinion in Biotechnology 30, 120–127. Chemical biotechnology • Pharmaceutical biotechnology.
  57. Nakashima, N., & Miyazaki, K. (2014). Bacterial cellular engineering by genome editing and gene silencing. International Journal of Molecular Sciences, 15, 2773–2793. Scholar
  58. Nielsen, J., n.d. Metabolic engineering. Applied Microbiology and Biotechnology 55, 263–283.
  59. Nocon, J., Steiger, M. G., Pfeffer, M., Sohn, S. B., Kim, T. Y., Maurer, M., et al. (2014). Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production. Metabolic Engineering, 24, 129–138. Scholar
  60. Nocon, J., Steiger, M., Mairinger, T., Hohlweg, J., Rußmayer, H., Hann, S., et al. (2016). Increasing pentose phosphate pathway flux enhances recombinant protein production in Pichia pastoris. Applied Microbiology and Biotechnology, 100, 5955–5963. Scholar
  61. Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis? Nature Biotechnology, 28, 245–248. Scholar
  62. Patil, K. R., Rocha, I., Förster, J., & Nielsen, J. (2005). Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, 308. Scholar
  63. Pharkya, P., & Maranas, C. D. (2006). An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metabolic Engineering, 8, 1–13. Scholar
  64. Pharkya, P., Burgard, A. P., & Maranas, C. D. (2004). OptStrain: A computational framework for redesign of microbial production systems. Genome Research, 14, 2367–2376. Scholar
  65. Porro, D., Sauer, M., Branduardi, P., & Mattanovich, D. (2005). Recombinant protein production in yeasts. Molecular Biotechnology, 31, 245–259. Scholar
  66. Pósfai, G., Koob, M., Hradecná, Z., Hasan, N., Filutowicz, M., & Szybalski, W. (1994). In vivo excision and amplification of large segments of the Escherichia coli genome. Nucleic Acids Research, 22, 2392–2398.CrossRefGoogle Scholar
  67. Potvin, G., Ahmad, A., & Zhang, Z. (2012). Bioprocess engineering aspects of heterologous protein production in Pichia pastoris: A review. Biochemical Engineering Journal, 64, 91–105. Scholar
  68. Prabhu, A. A., & Dasu, V. V. (2017). Dual-substrate inhibition kinetic studies for recombinant human interferon gamma producing Pichia pastoris. Preparative Biochemistry & Biotechnology, 47, 953–962. Scholar
  69. Prabhu, A. A., & Veeranki, V. D. (2018). Metabolic engineering of Pichia pastoris GS115 for enhanced pentose phosphate pathway (PPP) flux toward recombinant human interferon gamma (hIFN-γ) production. Molecular Biology Reports, 45, 961–972. Scholar
  70. Prabhu, A. A., Veeranki, V. D., & Dsilva, S. J. (2016). Improving the production of human interferon gamma (hIFN-γ) in Pichia pastoris cell factory: An approach of cell level. Process Biochemistry, 51, 709–718. Scholar
  71. Prabhu, A. A., Mandal, B., & Dasu, V. V. (2017a). Medium optimization for high yield production of extracellular human interferon-γ from Pichia pastoris: A statistical optimization and neural network-based approach. Korean Journal of Chemical Engineering 34, 1109–1121.
  72. Prabhu, A. A., Purkayastha, A., Mandal, B., Kumar, J. P., Mandal, B. B., & Dasu, V. V. (2017b). A novel reverse micellar purification strategy for histidine tagged human interferon gamma (hIFN-γ) protein from Pichia pastoris. International Journal of Biological Macromolecules.
  73. Prabhu, A. A., Bharali, B., Singh, A. K., Allaka, M., Sukumar, P., & Veeranki, V. D. (2018a). Engineering folding mechanism through Hsp70 and Hsp40 chaperones for enhancing the production of recombinant human interferon gamma (rhIFN-γ) in Pichia pastoris cell factory. Chemical Engineering Science 181, 58–67.
  74. Prabhu, A. A., Boro, B., Bharali, B., Chakraborty, S., & Dasu, V. V. (2018b). Gene and process level modulation to overcome the bottlenecks of recombinant proteins expression in Pichia pastoris. Current Pharmaceutical Biotechnology.
  75. Price, N. D., Papin, J. A., Schilling, C. H., & Palsson, B. O. (2003). Genome-scale microbial in silico models: The constraints-based approach. Trends in Biotechnology, 21, 162–169. Scholar
  76. Qin, X., Qian, J., Yao, G., Zhuang, Y., Zhang, S., & Chu, J. (2011). GAP promoter library for fine-tuning of gene expression in Pichia pastoris. Applied and Environment Microbiology, 77, 3600–3608. Scholar
  77. Raman, K., & Chandra, N. (2009). Flux balance analysis of biological systems: applications and challenges. Briefings in Bioinformatics, 10, 435–449. Scholar
  78. Raman, K., Rajagopalan, P., & Chandra, N. (2006). Principles and practices of pathway modelling [WWW Document]. Current Bioinformatics. Retrieved Jan 24, 2019, from
  79. Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013). Genome engineering using the CRISPR-Cas9 system. Nature Protocols, 8, 2281–2308. Scholar
  80. Ranganathan, S., Suthers, P. F., & Maranas, C. D. (2010). OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Computational Biology, 6, e1000744. Scholar
  81. Raschmanová, H., Weninger, A., Glieder, A., Kovar, K., Vogl, T. (n.d.). Implementing CRISPR-Cas technologies in conventional and non-conventional yeasts: Current state and future prospects. Biotechnology Advances.
  82. Rohwer, J. M. (2012). Kinetic modelling of plant metabolic pathways. Journal of Experimental Botany, 63, 2275–2292. Scholar
  83. Rosano, G. L., & Ceccarelli, E. A. (2014). Recombinant protein expression in Escherichia coli: Advances and challenges. Frontiers in Microbiology 5.
  84. Rothstein, R. (1991). [19] Targeting, disruption, replacement, and allele rescue: Integrative DNA transformation in yeast. In Methods in enzymology, guide to yeast genetics and molecular biology (pp. 281–301). Academic Press.
  85. Russell, C. B., Thaler, D. S., & Dahlquist, F. W. (1989). Chromosomal transformation of Escherichia coli recD strains with linearized plasmids. Journal of Bacteriology, 171, 2609–2613.CrossRefGoogle Scholar
  86. Samuel, P., Prasanna Vadhana, A. K., Kamatchi, R., Antony, A., & Meenakshisundaram, S. (2013). Effect of molecular chaperones on the expression of Candida antarctica lipase B in Pichia pastoris. Microbiological Research, 168, 615–620. Scholar
  87. Schilling, C. H., & Palsson, B. O. (2000). Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. Journal of Theoretical Biology, 203, 249–283. Scholar
  88. Schuster, S., & Hilgetag, C. (1994). On elementary flux modes in biochemical reaction systems at steady state. Journal of Biological Systems, 02, 165–182. Scholar
  89. Schuster, S., Dandekar, T., & Fell, D. A. (1999). Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology, 17, 53–60.CrossRefGoogle Scholar
  90. Schuster, S., Fell, D. A., & Dandekar, T. (2000). A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnology, 18, 326–332. Scholar
  91. Segrè, D., Vitkup, D., & Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences, 99, 15112–15117. Scholar
  92. Segrè, D., Zucker, J., Katz, J., Lin, X., D’Haeseleer, P., Rindone, W. P., et al. (2003). From annotated genomes to metabolic flux models and kinetic parameter fitting. Proceedings of the National Academy of Sciences, 7, 301–316. Scholar
  93. Sharan, S. K., Thomason, L. C., Kuznetsov, S. G., & Court, D. L. (2009). Recombineering: A homologous recombination-based method of genetic engineering. Nature Protocols, 4, 206–223. Scholar
  94. Singh, A., Chakraborty, D., & Maiti, S. (2016). CRISPR/Cas9: A historical and chemical biology perspective of targeted genome engineering. Chemical Society Reviews, 45, 6666–6684. Scholar
  95. Solà, A., Maaheimo, H., Ylönen, K., Ferrer, P., & Szyperski, T. (2004). Amino acid biosynthesis and metabolic flux profiling of Pichia pastoris. European Journal of Biochemistry, 271, 2462–2470. Scholar
  96. Solà, A., Jouhten, P., Maaheimo, H., Sánchez-Ferrando, F., Szyperski, T., & Ferrer, P. (2007). Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates. Microbiology Reading England, 153, 281–290. Scholar
  97. Stephanopoulos, G. (1999). Metabolic fluxes and metabolic engineering. Metabolic Engineering, 1, 1–11. Scholar
  98. Tang, P. W., Chua, P. S., Chong, S. K., Mohamad, M. S., Choon, Y. W., Deris, S., Omatu, S., Corchado, J. M., Rahim, W. H. C. & Abdul Rahim, A., 2015. A review of gene knockout strategies for microbial cells [WWW Document]. Recent Patents on Biotechnology. Retrieved July 2, 2019, from
  99. Terzer, M., & Stelling, J. (2008). Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24, 2229–2235. Scholar
  100. Trinh, C. T., Wlaschin, A., & Srienc, F. (2009). Elementary mode analysis: A useful metabolic pathway analysis tool for characterizing cellular metabolism. Applied Microbiology and Biotechnology, 81, 813–826. Scholar
  101. Tycko, J., Myer, V. E., & Hsu, P. D. (2016). Methods for optimizing CRISPR-Cas9 genome editing specificity. Molecular Cell, 63, 355–370. Scholar
  102. Unni, S., Prabhu, A. A., Pandey, R., Hande, R., & Veeranki, V. D. (2019). Artificial neural network-genetic algorithm (ANN-GA) based medium optimization for the production of human interferon gamma(hIFN-γ) in Kluyveromyces lactis cell factory. The Canadian Journal of Chemical Engineering.
  103. Wang, J. -R., Li, Y. -Y., Liu, D. -N., Liu, J. -S., Li, P., Chen, L. -Z., & Xu, S. -D. (2015). Codon optimization significantly improves the expression level of α-amylase gene from bacillus licheniformis in pichia pastoris [WWW Document]. BioMed Research International.
  104. Wurm, F. M. (2004). Production of recombinant protein therapeutics in cultivated mammalian cells. Nature Biotechnology, 22, 1393–1398. Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ashish A. Prabhu
    • 1
  • Kanchan Hariramani
    • 1
  • P. Lakshmi
    • 2
  • V. Venkata Dasu
    • 1
    Email author
  1. 1.Biochemical Engineering Laboratory, Department of Biosciences and BioengineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of BiotechnologyGoa UniversityTaleigaoIndia

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