An artificial intelligence approach to Bacillus amyloliquefaciens CCMI 1051 cultures: application to the production of anti-fungal compounds.
The combined effect of incubation time (IT) and aspartic acid concentration (AA) on the predicted biomass concentration (BC), Bacillus sporulation (BS) and anti-fungal activity of compounds (AFA) produced by Bacillus amyloliquefaciens CCMI 1051, was studied using Artificial Neural Networks (ANNs). The values predicted by ANN were in good agreement with experimental results, and were better than those obtained when using Response Surface Methodology. The database used to train and validate ANNs contains experimental data of B. amyloliquefaciens cultures (AFA, BS and BC) with different incubation times (1-9 days) using aspartic acid (3-42 mM) as nitrogen source. After the training and validation stages, the 2-7-6-3 neural network results showed that maximum AFA can be achieved with 19.5 mM AA on day 9; however, maximum AFA can also be obtained with an incubation time as short as 6 days with 36.6 mM AA. Furthermore, the model results showed two distinct behaviors for AFA, depending on IT.
Caldeira
A Teresa
AT
Escola de Ciências e Tecnologia, Departamento de Química e Centro de Química de Évora, Universidade de Évora, Rua Romão Ramalho, 59, 7000-671 Évora, Portugal.
Arteiro
José M
JM
Roseiro
José C
JC
Neves
José
J
Vicente
H
H
D000935:Antifungal Agents / Q000138:chemical synthesis* / Q000494:pharmacology; D001185:Artificial Intelligence; D001224:Aspartic Acid / Q000032:analysis; D001407:Bacillus / Q000166:cytology* / Q000187:drug effects / Q000502:physiology; D018533:Biomass; D001709:Biotechnology / Q000379:methods*; D019992:Databases as Topic; D016571:Neural Networks, Computer; D013171:Spores, Bacterial / Q000187:drug effects / Q000378:metabolism; D013997:Time Factors
D000935:Antifungal Agents; D001224:Aspartic Acid