3 Biotech

, 7:301 | Cite as

Different methodologies for sustainability of optimization techniques used in submerged and solid state fermentation

Review Article

Abstract

Optimization techniques are considered as a part of nature’s way of adjusting to the changes happening around it. There are different factors that establish the optimum working condition or the production of any value-added product. A model is accepted for a particular process after its sustainability has been verified on a statistical and analytical level. Optimization techniques can be divided into categories as statistical, nature inspired and artificial neural network each with its own benefits and usage in particular cases. A brief introduction about subcategories of different techniques that are available and their computational effectivity will be discussed. The main focus of the study revolves around the applicability of these techniques to any particular operation such as submerged fermentation (SmF) and solid state fermentation (SSF), their ability to produce secondary metabolites and the usefulness in the laboratory and industrial level. Primary studies to determine the enzyme activity of different microorganisms such as bacteria, fungi and yeast will also be discussed. l-Asparaginase, the most commonly used drugs in the treatment of acute lymphoblastic leukemia (ALL) shall be considered as an example, a short discussion on models used in the production by the processes of SmF and SSF will be discussed to understand the optimization techniques that are being dealt. It is expected that this discussion would help in determining the proper technique that can be used in running any optimization process for different purposes, and would help in making these processes less time-consuming with better output.

Keywords

Optimization Submerged fermentation Solid state fermentation Scale-up l-Asparaginase 

References

  1. Abbas Ahmed MM (2015) Production, purification and characterization of l-asparaginase from marine endophytic Aspergillus sp. ALAA-2000 under submerged and solid state fermentation. J Microb Biochem Technol 7:165–172CrossRefGoogle Scholar
  2. Afshinmanesh F, Marandi A, Rahimi-Kian A (2005) A novel binary particle swarm optimization method using artificial immune system. In: Computer as a Tool, 2005. EUROCON 2005. pp 217–220Google Scholar
  3. Agrawal H, Sharma M (2015) Optimization of NTRU cryptosystem using genetic algorithm. Int J Adv Res Comput Sci Softw Eng 5:944–947Google Scholar
  4. Agrawal H, Sharma M (2016) Optimization of NTRU cryptosystem using ACO and PSO algorithm. Int J Sci Eng Technol Res 5:617–621Google Scholar
  5. Ali U, Naveed M, Ullah A et al (2016) l-asparaginase as a critical component to combat acute lymphoblastic leukaemia (ALL): a novel approach to target ALL. Eur J Pharmacol 771:199–210CrossRefGoogle Scholar
  6. Arora S, Singh S (2013) The firefly optimization algorithm: convergence analysis and parameter selection. Int J Comput Appl 69:48–52Google Scholar
  7. Ashok A, Doriya K, Rao DRM, Kumar DS (2017) Design of solid state bioreactor for industrial applications: an overview to conventional bioreactors. Biocatal Agric Biotechnol 9:11–18Google Scholar
  8. Athreya S, Venkatesh YD (2012) Application of Taguchi method for optimization of process parameters in improving the surface roughness of lathe facing operation. Int Ref J Eng Sci 1:13–19Google Scholar
  9. Ba D, Boyaci IH (2007) Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. J Food Eng 78:846–854CrossRefGoogle Scholar
  10. Baskar G, Renganathan S (2012) Optimization of l-asparaginase production by Aspergillus terreus MTCC 1782 using response surface methodology and artificial neural network-linked genetic algorithm. Asia Pac J Chem Eng 7(2):212–220CrossRefGoogle Scholar
  11. Biswas A, Dasgupta S, Das S, Abraham A (2007) Synergy of PSO and bacterial foraging optimization—a comparative study on numerical benchmarks. Adv Soft Comput 44:255–263CrossRefGoogle Scholar
  12. Biswas T, Kumari K, Ramsingh H, Jha SK (2014) Optimization of three-phase partitioning system for enhanced recovery of l-asparaginase from Escherichia coli K12 using design of experiment (DOE). Int J Adv Res 2:1142–1147Google Scholar
  13. Bruns RE, Luis S, Ferreira C et al (2014) Statistical designs and response surface techniques for the optimization of chromatographic systems. J Chromatogr 1158:2–14Google Scholar
  14. Cammack KA, Marlborough DI, Miller DS (1972) Physical properties and subunit structure of l-asparaginase isolated from Erwinia carotovora. Biochem J 126:361–379CrossRefGoogle Scholar
  15. Carreiro MM, Sinsabaugh RL, Repert DA, Parkhurst DF (2017) Microbial enzyme shifts explain litter decay responses to simulated nitrogen deposition. Wiley Ecol Soc Am 81:2359–2365Google Scholar
  16. Chang BV, Chang YM (2016) Biodegradation of toxic chemicals by Pleurotus eryngii in submerged fermentation and solid-state fermentation. J Microbiol Immunol Infect 49:175–181CrossRefGoogle Scholar
  17. Chang JS, Lee JT, Chang AC (2006) Neural-network rate-function modeling of submerged cultivation of Monascus anka. Biochem Eng J 32:119–126CrossRefGoogle Scholar
  18. Chen L, Yang X, Raza W et al (2011) Solid-state fermentation of agro-industrial wastes to produce bioorganic fertilizer for the biocontrol of fusarium wilt of cucumber in continuously cropped soil. Bioresour Technol 102:3900–3910CrossRefGoogle Scholar
  19. Coello CAC (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inf Syst 1:129–156CrossRefGoogle Scholar
  20. Couto SR, Sanromán MA (2006) Application of solid-state fermentation to food industry—a review. J Food Eng 76:291–302CrossRefGoogle Scholar
  21. Dange VU, Peshwe SA (2015) Statistical assessment of media components by factorial design for l-asparaginase production by Aspergillus niger in surface fermentation. Eur J Exp Biol 5:57–61Google Scholar
  22. de Melo AG, Pedroso RCF, Guimarães LHS, Alves JGLF, Dias ES, de Resende MLV, Cardoso PG (2014) The optimization of Aspergillus sp. GM4 tannase production under submerged fermentation. Adv Microbiol 4:143–150CrossRefGoogle Scholar
  23. Desai KM, Survase SA, Saudagar PS et al (2008) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: a case study of fermentative production of scleroglucan. Biochem Eng J 41:266–273CrossRefGoogle Scholar
  24. Dhanarajan G, Mandal M, Sen R (2014) A combined artificial neural network modeling-particle swarm optimization strategy for improved production of marine bacterial lipopeptide from food waste. Biochem Eng J 84:59–65CrossRefGoogle Scholar
  25. Dorigo M, Stützle T (2009) Ant colony optimization. Encycl Mach Learn 1:28–39Google Scholar
  26. Doriya K, Jose N, Gowda M, Kumar DS (2016) Solid-state fermentation vs submerged fermentation for the production of l-asparaginase. Adv Food Nutr Res 78:115–135CrossRefGoogle Scholar
  27. El-Bakry M, Abraham J, Cerda A et al (2015) From wastes to high value added products: novel aspects of SSF in the production of enzymes. Crit Rev Environ Sci Technol 45:1999–2042CrossRefGoogle Scholar
  28. Elias F, Soares F, Braga FR et al (2010) Optimization of medium composition for protease production by Paecilomyces marquandii in solid-state-fermentation using response surface methodology. Afr J Microbiol Res 4:2699–2703Google Scholar
  29. El-Naggar NEA, Moawad H, El-Shweihy NM, El-Ewasy SM (2015) Optimization of culture conditions for production of the anti-leukemic glutaminase free l-asparaginase by newly isolated Streptomyces olivaceus NEAE-119 using response surface methodology. Biomed Res Int 2015:1–17CrossRefGoogle Scholar
  30. El-Sersy NA, Ebrahim HA, Abou-Elela GM (2010) Response surface methodology as a tool for optimizing the production of antimicrobial agents from Bacillus licheniformis SN2. Curr Res Bacteriol 3:1–14CrossRefGoogle Scholar
  31. Faisal PA, Hareesh ES, Priji P et al (2014) Optimization of parameters for the production of lipase from Pseudomonas sp. BUP6 by solid state fermentation. Adv Enzyme Res 2:125–133CrossRefGoogle Scholar
  32. Falahian R, Mehdizadeh Dastjerdi M, Molaie M et al (2015) Artificial neural network-based modeling of brain response to flicker light. Nonlinear Dyn 81:1951–1967CrossRefGoogle Scholar
  33. Farag AM, Hassan SW, Beltagy EA, El-Shenawy MA (2015) Optimization of production of anti-tumor l-asparaginase by free and immobilized marine Aspergillus terreus. Egypt J Aquat Res 41:295–302CrossRefGoogle Scholar
  34. Fasham AMJR, Evans GT, Kiefer DA et al (1995) The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station at 47°N20°W [and discussion]. Philos Trans R Soc Lond B Biol Sci 348:203–209CrossRefGoogle Scholar
  35. Fenech A, Fearn T, Strlic M (2012) Use of Design-of-Experiment principles to develop a dose-response function for colour photographs. Polym Degrad Stab 97:621–625CrossRefGoogle Scholar
  36. Fister I Jr, Yang XS, Fister I et al (2013) A brief review of nature-inspired algorithms for optimization. Elektroteh Vestn/Electrotech Rev 80:116–122Google Scholar
  37. Funes E, Allouche Y, Beltrán G, Jiménez A (2015) A review: artificial neural networks as tool for control food industry process. J Sens Technol 5:28–43CrossRefGoogle Scholar
  38. Garsoux G, Lamotte J, Gerday C, Feller G (2004) Kinetic and structural optimization to catalysis at low temperatures in a psychrophilic cellulase from the Antarctic bacterium Pseudoalteromonas haloplanktis. Biochem J 384:247–253CrossRefGoogle Scholar
  39. Ghosh S, Murthy S, Govindasamy S, Chandrasekaran M (2013) Optimization of l-asparaginase production by Serratia marcescens (NCIM 2919) under solid state fermentation using coconut oil cake. Sustain Chem Process 1:9CrossRefGoogle Scholar
  40. Hoa BT, Hung PV (2013) Optimization of nutritional composition and fermentation conditions for cellulase and pectinase production by Aspergillus oryzae using response surface methodology. Int Food Res J 20:3269–3274Google Scholar
  41. Hongwen C, Baishan F, Zongding H (2005) Optimization of process parameters for key enzymes accumulation of 1,3-propanediol production from Klebsiella pneumoniae. Biochem Eng J 25:47–53CrossRefGoogle Scholar
  42. Irfan M, Asghar U, Nadeem M et al (2016) Optimization of process parameters for xylanase production by Bacillus sp. in submerged fermentation. J Radiat Res Appl Sci 9:139–147CrossRefGoogle Scholar
  43. Kalantar E, Deopurkar R (2007) Application of factorial design for the optimized production of antistaphylococcal metabolite by Aureobasidium pullulans. Jundishapur J Nat Pharm Prod 2:69–77Google Scholar
  44. Karmakar M, Ray RR (2011) Statistical optimization of FPase production from water hyacinth using Rhizopus oryzae PR 7. J Biochem 3:225–229Google Scholar
  45. Kenari SLD, Alemzadeh I, Maghsodi V (2011) Production of l-asparaginase from Escherichia coli ATCC 11303: optimization by response surface methodology. Food Bioprod Process 89:315–321CrossRefGoogle Scholar
  46. Khaleel H, Ali Q, Zulkali M et al (2012) Economic benefit from the optimization of citric acid production from rice straw through Plackett–Burman design and central. Turk J Eng Environ Sci 36:81–93Google Scholar
  47. Kim S-K, Min W-K, Park Y-C, Seo J-H (2015) Application of repeated aspartate tags to improving extracellular production of Escherichia coli l-asparaginase isozyme II. Enzyme Microb Technol 79–80:49–54CrossRefGoogle Scholar
  48. Kumar GM, Knowles NR (1993) Changes in lipid peroxidation and lipolytic and free-radical scavenging enzyme activities during aging and sprouting of potato (Solanum tuberosum) seed-tubers. Plant Physiol 102:115–124CrossRefGoogle Scholar
  49. Kumar R, Balaji S, Uma TS et al (2010) Optimization of influential parameters for extracellular keratinase production by Bacillus subtilis (MTCC9102) in solid state fermentation using horn meal—a biowaste management. Appl Biochem Biotechnol 160:30–39CrossRefGoogle Scholar
  50. Kunamneni A, Singh S (2016) Response surface optimization of enzymatic hydrolysis of maize starch for higher glucose production maize starch for higher glucose production. Biochem Eng 27:179–190CrossRefGoogle Scholar
  51. Kunamneni A, Raju KVVSNB, Zargar MI et al (2015) Optimization of process parameters for production of lipase in solid-state fermentation by newly isolated Aspergillus species. Indian J Biotechnol 3:65–69Google Scholar
  52. Li S, Lin S, Chien YW et al (2001) Statistical optimization of gastric floating system for oral controlled delivery of calcium. AAPS Pharm Sci Tech 2:11–22CrossRefGoogle Scholar
  53. Mandal A, Kar S, Dutta T et al (2015) Parametric optimization of submerged fermentation conditions for xylanase production by Bacillus cereus BSA1 through Taguchi Methodology. Acta Biol Szeged 59:189–195Google Scholar
  54. Meng F, Xing G, Li Y et al (2015) The optimization of Marasmius androsaceus submerged fermentation conditions in 5-l fermentor. Saud J Biol Sci 23:S99–S105CrossRefGoogle Scholar
  55. Mitchell DA, Krieger N, Stuart DM, Pandey A (2000) New developments in solid-state fermentation II. Rational approaches to the design, operation, and scale-up of bioreactors. Process Biochem 35:1211–1225CrossRefGoogle Scholar
  56. Mohana S, Shrivastava S, Divecha J, Madamwar D (2008) Response surface methodology for optimization of medium for decolorization of textile dye direct black 22 by a novel bacterial consortium. Bioresour Technol 99:562–569CrossRefGoogle Scholar
  57. Montgomery DC (2013) Design and analysis of experiments, 8th edn. Wiley, New JerseyGoogle Scholar
  58. Müller SD, Marchetto J, Airaghi S, Koumoutsakos P (2002) Optimization based on bacterial chemotaxis. IEEE Trans Evol Comput 6:16–29CrossRefGoogle Scholar
  59. Negi S, Banerjee R (2006) Optimization of amylase and protease production from Aspergillus awamori in single bioreactor through EVOP factorial design technique. Food Technol Biotechnol 44:257–261Google Scholar
  60. Pallem C, Manipati S, Somalanka SR, Pradesh A (2010) Process optimization of l-glutaminase production by Trichoderma koningii under solid state fermentation (SSF) Centre for Biotechnology, Department of Chemical Engineering, AU College of Engineering Department of Surgery, NRI Institute of Medical Science. 1168–1174Google Scholar
  61. Pappu JSM, Gummadi SN (2016) Multi response optimization for enhanced xylitol production by Debaryomyces nepalensis in bioreactor. 3. Biotech 6:1–10Google Scholar
  62. Rizzari C, Zucchetti M, Conter V et al (2000) l-asparagine depletion and l-asparaginase activity in children with acute lymphoblastic leukemia receiving i.m. or i.v. Erwinia C. or E. coli l-asparaginase as first exposure. Ann Oncol 11:189–193CrossRefGoogle Scholar
  63. Rodríguez-fernández DE, Rodríguez-león JA, De Carvalho JC et al (2011) The behavior of kinetic parameters in production of pectinase and xylanase by solid-state fermentation. Bioresour Technol 102:10657–10662CrossRefGoogle Scholar
  64. Roy RV, Das M, Banerjee R, Bhowmick AK (2006) Comparative studies on rubber biodegradation through solid-state and submerged fermentation. Process Biochem 41:181–186CrossRefGoogle Scholar
  65. Schutyser MAI, Briels WJ, Boom RM, Rinzema A (2004) Combined discrete particle and continuum model predicting solid-state fermentation in a drum fermentor. Biotechnol Bioeng 86:405–413CrossRefGoogle Scholar
  66. Sindhu R, Suprabha GN, Shashidhar S (2009) Optimization of process parameters for the production of α-amylase from Penicillium janthinellum (NCIM 4960) under solid state fermentation. Afr J Microbiol Res 3:498–503Google Scholar
  67. Singh Y, Srivastava K (2013) Statistical and evolutionary optimization for enhanced production of an antileukemic enzyme, l-asparaginase, in a protease-deficient Bacillus aryabhattai ITBHU02 isolated from the soil contaminated with hospital waste. Indian J Exp Biol 51:322–335Google Scholar
  68. Sinha RK, Singh HR (2016) Statistical optimization of the recombinant l-asparaginase from Pseudomonas fluorescens by Taguchi DOE. Int J Pharm Tech Res 9:254–260Google Scholar
  69. Subramaniyam R, Vimala R (2012) Solid state and submerged fermentation for the production of bioactive substances: a comparative study. Int J Sci Nat 3:480–486Google Scholar
  70. Varalakshmi V, Raju K (2013) Optimization of l-asparaginase production by Aspergillus terreus mtcc 1782 using bajra seed flour under solid state fermentation. Int J Res Eng Technol 2:121–129CrossRefGoogle Scholar
  71. Venil CK, Nanthakumar K, Karthikeyan K (2009) Production of l-asparaginase by Serratia marcescens SB08: optimization by response surface methodology. Iran J Biotechnol 7:10–18Google Scholar
  72. Weng XY, Sun JY (2006) Kinetics of biodegradation of free gossypol by Candida tropicalis in solid-state fermentation. Biochem Eng J 32:226–232CrossRefGoogle Scholar
  73. Williams JC, ReVelle CS, Levin SA (2004) Using mathematical optimization models to design nature reserves. Front Ecol Environ 2:98–105CrossRefGoogle Scholar
  74. Xu CP, Kim SW, Hwang HJ et al (2003) Optimization of submerged culture conditions for mycelial growth and exo-biopolymer production by Paecilomyces tenuipes C240. Process Biochem 38:1025–1030CrossRefGoogle Scholar
  75. Xu H, Caramanis C, Mannor S (2012) Statistical optimization in high dimensions. In: Proceedings of the 15th international conference on artificial intelligence and statistics (AISTATS) 2012, La Palma, Canary Islands, pp 1332–1340Google Scholar
  76. Yam CH, Izzo D, Biscani F (2010) Towards a high fidelity direct transcription method for optimisation of low-thrust trajectories. In: In 4th International Conference on Astrodynamics Tools and Techniques, pp 1–9Google Scholar
  77. Yang X (2014) Nature-inspired optimization algorithms. Elsevier, LondonGoogle Scholar
  78. Ying Y, Shao P, Jiang S, Sun P (2009) Artificial neural network analysis of immobilized lipase catalyzed synthesis of biodiesel from rapeseed. Comput Comput Technol Agric 2:1239–1249Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Industrial Bioprocess and BioProspecting Laboratory (IBBL), Department of Chemical Engineering, Room No: 530, Hostel Block EIndian Institute of Technology, HyderabadSangareddyIndia

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