Artificial Neural Network-Genetic Algorithm Approach to Optimize Media Constituents for Enhancing Lipase Production by a Soil Microorganism

Abstract

Results of lipase production by a soil microorganism, expressed in terms of lipolytic activities of the culture were modeled and optimized using artificial neural network (ANN) and genetic algorithm (GA) techniques, respectively. ANN model, developed based on back propagation algorithm, were highly accurate in predicting the system with coefficient of determination (R2) value being close to 0.99. Optimization using GA, based on the ANN model developed, resulted in the following values of the media constituents: 9.991 ml/l oil, 0.100 g/l MgSO4 and 0.009 g/l FeSO4. And a maximum value of 7.69 U/ml of lipolytic activity at 72 h of culture was obtained using the ANN-GA method, which was found to be 8.8% higher than the maximum values predicted by a statistical regression-based optimization technique-response surface methodology.

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

“…This is a new approach not reported earlier. However, optimization studies based on the ANN-GA for improved performance of biological systems have been reported earlier by Haider et al (2008) and Sivapathasekaran et al (2010).…”

Optimisation of pesticide crystal protein production from Bacillus thuringiensis employing artificial intelligence techniques

“…ANNs such as the three-layer back propagation network have been proved to serve as an approximation for multi-dimensional functions (Hornik et al, 1989;Poggio and Girosi, 1990). ANNs have already been applied to solve, predict and optimise a variety of environmental and biotechnological problems, such as biodegradation of organic compounds (Schuurmann and Muller, 1994), identifying unknown air pollution sources (Reich et al, 1999), predicting fed batch fermentation kinetics (Valdez-Castro et al, 2003), control and management of a biological reactor for treating hydrogen sulphide (Elías et al, 2006) and enhanced lipase production (Haider et al, 2008).…”

“…On the other hand, artificial intelligence techniques, such as the ANN and GA are generally found to outperform RSM in modelling and optimisation. But, in recent years, only a limited number of researchers have investigated the possibility of using these non-conventional techniques in biological processes (Haider et al, 2008). And, optimisation using such artificial intelligence techniques for enhancing PCP production from a microbial source has not been studied, which proves helpful even when a statistical based optimisation technique fail.…”

A Repository of Intelligence of Industrial Fermentation (RIIF) using Web Enabled Technology

“…the economic viability of each industrial process lies on the volume of intelligence vested in at least one of the three stages. For both novel and established processes, a constant research is required to improve the process beyond its actual optimum state (6,9) and to reach a maximum productivity with the lowest possible cost, while maintaining quality (5). thus, fermentation research constantly uses or/and generates novel intelligent ideas, approaches and uncommon wisdom (hereafter as Fermentation intelligence) within at least one of the process stages.…”