L-Lysine is one of the important amino acid required for humans and animals. It has a high commercial market. Large scale production of this amino acid is essential to meet the commercial demands. Typically, L-lysine is produced by batch fermentation. In the present study, the important process, as well as nutrient parameters such as glucose concentration (g/L), rpm, incubation temperature (°C), pH and incubation time for L-lysine production by Corynebacterium glutamicum ATCC13032, were optimized by a combined approach of response surface methodology (RSM) with artificial neural network (ANN) method. Initially, 32 runs face central composite design was employed. In the first step, the data was analyzed by the RSM and the optimum conditions for L-lysine production were determined. In the second step, the same data was used to train the neural network. A feed-forward neural network with error backpropagation was used. The best network was obtained by optimizing the no of neurons in the hidden layer. From the best network, the optimized weights and predicted responses were used to optimize the conditions of the selected parameters by genetic algorithm (GA). Overall with the combination of RSM-ANN-GA onefold of L-lysine production from Corynebacterium glutamicum ATCC 13032 was improved.
The Author is thankful to the Dept of Biotech and Math works Computer Dept for providing the necessary fund and infrastructure for conducting the studies to enable my Doctoral thesis.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
 Pfefferle W, Möckel B, Bathe B, Marx A. Biotechnol Manufacture Lysine. Adv Biochem Eng Biotechnol. 2003;79:60–112.Search in Google Scholar
 Kawaguchi H, Vertes AA, Okino S, Inui M, Yukawa H. Engineering of a xylose metabolic pathway in Corynebacterium glutamicum. Appl Environ Microbiol. 2006;72:3418–28.10.1128/AEM.72.5.3418-3428.2006Search in Google Scholar
 Meiswinkel TM, Gopinath V, Lindner SN, Nampoothiri KM, Wendisch VF. Accelerated pentose utilization by Corynebacterium glutamicum for accelerated production of lysine, glutamate, ornithine and putrescine. Microb Biotechnol. 2013;6:131–40.10.1111/1751-7915.12001Search in Google Scholar
 Mimitsuka T, Sawai H, Hatsu M, Yamada K. Metabolic engineering of Corynebacterium glutamicum for cadaverine fermentation. Biosci Biotechnol Biochem. 2007;71:2130–5.10.1271/bbb.60699Search in Google Scholar
 Laxmi GS, Sathish T, SubbaRao C, Brahmaiah P, Hymavathi M, Prakasham RS. Palm fiber as novel substrate for enhanced xylanase production by isolated Aspergillus sp. RSP-6. Curr Trend Biotechnol Pharma. 2008;2:447–55.Search in Google Scholar
 Hymavathi M, Sathish T, Subba Rao C, Prakasham RS. Enhancement of L-asparaginase production by isolated Bacillus circulans (MTCC 8574) using response surface methodology. Appl Biochem Biotechnol. 2009;159:191–8.10.1007/s12010-008-8438-2Search in Google Scholar
 Sathish T, Laxmi GS, Rao CS, Brahmaiah P, Prakasham RS. Mixture design as first step for improved glutaminase production in solid-state fermentation by isolated Bacillus. Lett Appl Microbiol. 2008;47:256–62.10.1111/j.1472-765X.2008.02413.xSearch in Google Scholar
 Mahalaxmi Y, Sathish T, Rao CS, Prakasham RS. Corn husk as a novel substrate for the production of rifamycin B by isolated Amycolatopsis sp RSP 3 under SSF. Process Biochem. 2010;45:47–53.10.1016/j.procbio.2009.08.001Search in Google Scholar
 Mahalaxmi Y, Sathish T, Prakasham RS. Development of balanced medium composition for improved rifamycin B production by isolated Amycolatopsis sp. RSP-3. Lett Appl Microbiol. 2009;49:533–8.10.1111/j.1472-765X.2009.02701.xSearch in Google Scholar
 Sathish T, Prakasham RS. Enrichment of glutaminase production by Bacillus subtilis RSP-GLU in submerged cultivation based on neural network - genetic algorithm approach. J Chem Technol Biotechnol. 2010;85:50–8.10.1002/jctb.2267Search in Google Scholar
 Prakash MJ, Sivakumar V, Thirugnanasambandham K, Sridhar R. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. Alexandria Eng J. 2013;52:507–16.10.1016/j.aej.2013.06.007Search in Google Scholar
 Willis MJ, Massimo CD, Montague GA, Tham MT, Morris AJ. Artificial neural networks in process engineering. IEE Proc D-Control Theory App. 1991;138:256–66.10.1049/ip-d.1991.0036Search in Google Scholar
 Gadekar MR, Ahammed MM. Modelling dye removal by adsorption onto water treatment residuals using combined response surface methodology-artificial neural network approach. J Environ Manage. 2019;231:241–8.10.1016/j.jenvman.2018.10.017Search in Google Scholar
 Subba Rao C, Sathish T, Mahalaxmi M, Laxmi GS, Rao RS, Prakasham RS. Modelling and optimization of fermentation factors for enhancement of alkaline protease production by isolated Bacillus circulans using feed-forward neural network and genetic algorithm. J Appl Microbiol. 2008;104:889–98.10.1111/j.1365-2672.2007.03605.xSearch in Google Scholar
 Chiranjeevi PV, Sathish T, Pandian MR. Integration of artificial neural network modeling and genetic algorithm approach for enrichment of Laccase production in solid state fermentation by Pleurotusostreatus. BioResources. 2014;9:2459–70.10.15376/biores.9.2.2459-2470Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston