Optimization of actinomycin V production by Streptomyces triostinicus using artificial neural network and genetic algorithm

Artificial neural network (ANN) and genetic algorithm (GA) were applied to optimize the medium components for the production of actinomycinV from a newly isolated strain of Streptomyces triostinicus which is not reported to produce this class of antibiotics. Experiments were conducted using the cent...

Full description

Saved in:
Bibliographic Details
Published in:Applied microbiology and biotechnology 2009-02, Vol.82 (2), p.379-385
Main Authors: Singh, Vineeta, Khan, Mahvish, Khan, Saif, Tripathi, C. K. M
Format: Article
Language:English
Subjects:
Accuracy
Actinomycin V
Algorithms
Anti-Bacterial Agents - metabolism
Antibiotics
Biological and medical sciences
Biotechnology
Central composite design
Culture Media - metabolism
Dactinomycin - metabolism
Design of experiments
Fermentation
Fundamental and applied biological sciences. Psychology
Genetic algorithm
Genetic algorithms
Glucose
Life Sciences
Mathematical models
Methods
Microbial Genetics and Genomics
Microbiology
Models, Biological
Models, Statistical
Neural network
Neural networks
Neural Networks (Computer)
Optimization
Sodium chloride
Soybeans
Streptomyces
Streptomyces - genetics
Streptomyces - metabolism
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Artificial neural network (ANN) and genetic algorithm (GA) were applied to optimize the medium components for the production of actinomycinV from a newly isolated strain of Streptomyces triostinicus which is not reported to produce this class of antibiotics. Experiments were conducted using the central composite design (CCD), and the data generated was used to build an artificial neural network model. The concentrations of five medium components (MgSO₄, NaCl, glucose, soybean meal and CaCO₃) served as inputs to the neural network model, and the antibiotic yield served as outputs of the model. Using the genetic algorithm, the input space of the neural network model was optimized to find out the optimum values for maximum antibiotic yield. Maximum antibiotic yield of 452.0 mg l⁻¹ was obtained at the GA-optimized concentrations of medium components (MgSO₄ 3.657; NaCl 1.9012; glucose 8.836; soybean meal 20.1976 and CaCO₃ 13.0842 gl⁻¹). The antibiotic yield obtained by the ANN/GA was 36.7% higher than the yield obtained with the response surface methodology (RSM).
ISSN:0175-7598
1432-0614
DOI:10.1007/s00253-008-1828-0