Introduction, Review of Literature and Objectives
1.4. Fermentative production of swainsonine from Metarhizium anisopliae In despite of extraction from plants and chemical modes of synthesis, the
1.4.1. Parameters and optimization models for fermentative production
1.4.1.4. Hybrid genetic algorithms: Artificial neural network- genetic algorithm In recent years, genetic algorithm (GA) based on ANN model as an objective
or fitness function has been applied successfully in optimizing the input space of various bioprocess studies (Singh and Kaur, 2013; Zafar et al., 2012). GA is an artificial intelligence based stochastic non-linear optimization technique which solves optimization problems based on natural selection or Darwin’s theory of evolution (Desai et al., 2008). In hybrid ANN-GA optimization model, The ANN derived fuzzy population (collection of individual sets of experiment) is set to undergo the various genetic operations i.e. selection, crossover, mutation etc. to finally produce a most evolved or optimal sets of solution over successive generations. Hence, in a particular
environment, the individuals (solutions) who better fit the environment will be able to survive and hand down their chromosomes (optimal inputs variables) to their descendants, while the less evolved individuals will become extinct. In the original genetic algorithm an individual chromosome is represented by a binary string. The bits of each string are called genes and their varying values as alleles. A group of individual chromosomes are called a population. Basic genetic operators include reproduction, crossover and mutation. Genetic algorithms are especially capable of handling problems in which the objective function is discontinuous or non differentiable, non-convex, multimodal or noisy. Since the algorithms operate on a population instead of a single point in the search space, they climb many peaks in parallel and therefore reduce the probability of finding local minima (Whitley, 1988).
Nagata and Chu (2003) analysed RSM data (presented by Achary et al., 1997) using neural network and GA to predict optimum conditions and reported that feed-forward neural network and GA (FFNN-GA) is a more efficient optimization model. A more advanced form of Real Coded GA is also practiced instead of conventional Binary Coded GA. To compensate for the excessive computation time required by the BGA, the real encoded genetic algorithm (REGA) emphasizing on the coding of the chromosomes with floating point representation was introduced and proven to have significant improvements on the computation speed and precision (Gen and Cheng 2000; Goldberg 1991). The appropriate implementation of GA includes the definition of the objective function, definition and implementation of the genetic representation, and definition and implementation of the genetic operators. The basic operation scheme for a typical GA is shown in Fig 1.3. At the same time, much effort was imposed to improve computation performance of GAs and avoid premature convergence of solutions. GA is capable of exploring large input variables’ space
through the search operators viz., selection, crossing over, mutation and elitism (Weuster-Botz, 2000). These GA operators are as discussed below:
(i) Selection (of the mating pool)
The ANN generated population/chromosomes (experimental sets) undergoes the selection procedure to seek out the best fitted individuals for mating to generate the consecutive populations.
(ii) Crossover
The algorithm exchanges the parent chromosomes (if binary coded) between pairs of solutions in mating pool with some probability (pcross) to produce the candidates for the next generation population. For the real encoded population individuals, the random combination of two best candidates from the mating pool is set to undergo random cross over with some defined probability for the consecutive iterations
(iii) Mutation
The binary coded population individuals are mutated by flipping bit values by some defined or random probability value (pmut). The real encoded population individuals are mutated with some random integers replacing the integer values in another individual, preferably in a very low frequency. The final result of the mutation is only selected if it is a feasible candidate solution.
(iv) Elitism
This step of the algorithm is only employed if the population individuals generated by crossover and mutation steps are worse or less evolved than the parent
individuals to prevent the less evolved candidates to be passed to the next generation.
In this situation, the whole steps are repeated to seek the better evolved candidates.
The biochemical engineering applications of artificial intelligence based ANN- GA (real or binary coded) optimization models would present an economic incentive towards the fermentative production of swainsonine. Singh and Kaur (2013) have described an ANN combined with real encoded genetic algorithm (ANN-REGA) to optimize the production media components for swainsonine production at shake flask levels, and compared the optimization efficiency of the model with that of statistical RSM- optimization model.
The optimization of bioreactor operation conditions i.e., agitation, aeration and incubation time is also studied using the real value encoded evolutionary programming (EP) of the similar nature and correlated successfully with the scale up parameters (oxygen mass transfer coefficient KLa s-1 and gassed power per unit of volume Pg/VL W/m3) for swainsonine production. The nonlinear behaviour and time varying properties make bioreactors difficult to control with traditional techniques. In this context, there is a need to consider quantitative mathematical models, capable of describing the process dynamics and the interrelation among relevant variables.
Additionally, robust optimization techniques must deal with the complexity of models, the environment constraints and the inherent noise of the experimental process. GA has proven to be extremely suitable for the optimization of highly non- linear problems with many variables associated to a variety of microbial fermentation models. There are numerous reports regarding the successful applications of these heuristic optimization models for the optimization of various fermentation models, such as the three folds increase in mevinolin biosynthesis by Aspergillus terreusi (Zuzek et al., 1996), nearly two fold increase in the scleroglucan production (Desai et
al., 2008), 6 % increase in glucansucrase activities (Singh et al., 2008) and about 70
% enhancement in marine biosurfactant production (Sivapathasekaran et al., 2010) etc. Process modelling is a combination of both science and art, because a good dose of creativity is required to make assumptions for a computationally simple yet predictive model. Modelling inherently involves a compromise between accuracy (complexity), cost and effort involved in developing a model. There are many optimum design methods which combine the optimization algorithms with the computer simulations that have been reported for fermentation modelling (Abakarov et al., 2009; Guo et al., 2010).
1.5. Downstream recovery of swainsonine