Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic algorithm

Multi-Objective Optimisation of Biodiesel Synthesis in Simulated Moving Bed Reactor

In this work, multi-objective optimisation study was performed to determine the performance improvement in a simulated moving bed reactor (SMBR) for biodiesel synthesis. The selection of the operating parameters such as switching time, liquid flow rates in various sections, as well as the length and number of columns is not straightforward in an SMBR. In most cases, conflicting requirements and constraints influence the optimal selection of the decision (operating or design) variables. A mathematical model that predicts single-column experimental results well was modified and verified experimentally for multiple-column SMBR system. In this article, a few multi-objective optimisation problems were carried out for both existing set-up as well as at the design stage. A non-dominated sorting genetic algorithm (NSGA) was used as the optimisation tool for the optimisation study. Due to conflicting effect of process parameters, the multi-objective optimisation study resulted in non-dominated Pareto optimal solutions. It was shown that significant increase in yield and purity of biodiesel in SMBR was possible both for operating and at design stage.

The optimization of the fermentation process of wheat germ for flavonoids and two benzoquinones using EKF-ANN and NSGA-II

Bi-objective optimization of wheat germ fermentation using EKF-ANN combined with NSGA-II.

Modeling and multi-criteria optimization of an industrial process for continuous lactic acid production

The key feature of this paper is the optimization of an industrial process for continuous production of lactic acid. For this, a two-stage fermentor process integrated with cell recycling has been mathematically modeled and optimized for overall productivity, conversion, and yield simultaneously. Non-dominated sorting genetic algorithm (NSGA-II) was applied to solve the constrained multi-objective optimization problem as it is capable of finding multiple Pareto-optimal solutions in a single run, thereby avoiding the need to use a single-objective optimization several times. Compared with traditional methods, NSGA-II could find most of the solutions in the true Pareto-front and its simulation is also very direct and convenient. The effects of operating variables on the optimal solutions are discussed in detail. It was observed that we can make higher profit with an acceptable compromise in a two-stage system with greater efficiency.

Comparative multi-goal tradeoffs in systems engineering of microbial metabolism

Background

Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs.

Results

Here, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (Escherichia coli, Saccharomyces cerevisiae, and Shewanella oneidensis) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity.

Conclusions

The resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.

Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75 g l(-1), 3.97 g $(-1)) to (0.44 g l(-1), 5.19 g $(-1)) for batch and from (1.5 g l(-1), 5.46 g $(-1)) to (1.1 g l(-1), 6.34 g $(-1)) for fed batch operations. One pareto solution each for batch and for fed batch mode was experimentally validated.

Hybrid on-line optimal control strategy for producing α-amylase by Bacillus subtilis

The combined effect of macronutrients in the extraction medium on α-amylase produced by Bacillus subtilis were studied by using response surface methodology in shaken flask cultures. The production of amylase was significantly affected by the interaction between wheat bran and the cotton seed extract in the extraction medium and by the interaction between the cotton seed extract and starch. The optimal combination in the extraction medium for maximum α-amylase production was determined as 10.80 g·L⁻¹ of wheat bran, 9.90 g·L⁻¹ of cotton seed extract, 0.5 g·L⁻¹ of starch, 2.0 g·L⁻¹ of yeast extract, 5.00 g·L⁻¹ of NaCl and 2.00 g·L⁻¹ of CaCl₂. A 12.55-fold increase of enzyme activity was recorded in the optimized medium compared to the result acquired in a minimum essential medium. The optimized medium was used to compare different cultivation strategies in fermenters. The pH-stat strategy for reducing cellular stress response and the substrate concentration-stat strategy for reducing substrate inhibition were independently investigated. The temperature-limited strategy has been proposed to solve the proteolytic digestion problem, although the high-pressure strategy resulted in high productivity. A hybrid strategy simultaneously controlling pH, temperature, substrate concentration and pO₂ was finally investigated to enhance the efficiency of the process. This hybrid strategy resulted in high activity of α-amylase, increasing the productivity almost three-fold as compared to an ordinary fed-batch culture.

Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design

BACKGROUND

Microbial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest.

RESULTS

Utilization of thermodynamically-weighted elementary modes for flux reconstruction of E. coli central metabolism revealed two clusters of EMs with respect to their Delta Gp degrees. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in E. coli. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm.

CONCLUSION

A computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from E. coli. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product.