Hybrid of ant colony optimization and flux variability analysis for improving metabolites production

Metabolic engineering has been successfully used for the production of a variety of useful compounds such as L-phenylalanine and biohydrogen that received high demand on food, pharmaceutical, fossil fuels, and energy industries. Reaction deletion is one of the strategies of in silico metabolic engineering that can alter the metabolism of microbial cells with the objective to get the desired phenotypes. However, due to the size and complexity of metabolic networks, it is difficult to determine the near-optimal set of reactions to be knocked out. The complexity of the metabolic network is also caused by the presence of competing pathway that may interrupt the high production of a desireable metabolite. Consequently, this factor leads to low Biomass-Product Coupled Yield (BPCY), production rate and growth rate. Other than that, inefficiency of existing algorithms in modelling high growth rate and production rate is another problem that should be handled and solved. Therefore, this research proposed a hybrid algorithm comprising Ant Colony Optimization and Flux Variability Analysis (ACOFVA) to identify the best reaction combination to be knocked out to improve the production of desired metabolites in microorganisms. Based on the experimental results, ACOFVA shows an increase in terms of BPCY and production rate of L-Phenylalanine in Yeast and biohydrogen in Cyanobacteria, while maintaining the optimal growth rate for the target organism. Besides, suggested reactions to be knocked out for improving the production yield of L-Phenylalanine and biohydrogen have been identified and validated through the biological database. The algorithm also shows a good performance with better production rate and BPCY of L-Phenylalanine and biohydrogen than existing results

Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment

Microbial strains can be optimized using metabolic engineering which implements gene knockout techniques. These techniques manipulate potential genes to increase the yield of metabolites through restructuring metabolic networks. Nowadays, several hybrid optimization algorithms have been proposed to optimize the microbial strains. However, the existing algorithms were unable to obtain optimal strains because the nonessential genes are hardly to be diagnosed and need to be removed due to high complexity of metabolic network. Therefore, the main goal of this study is to overcome the limitation of the existing algorithms by proposing a hybrid of Differential Evolution and Minimization of Metabolic Adjustments (DEMOMA). Differential Evolution (DE) is known as population-based stochastic search algorithm with few tuneable parameter control. Minimization of Metabolic Adjustment (MOMA) is one of the constraint based algorithms which act to simulate the cellular metabolism after perturbation (gene knockout) occurred to the metabolic model. The strength of MOMA is the ability to simulate the strains that have undergone mutation precisely compared to Flux Balance Analysis. The data set used for the production of fumaric acid is S. cerevisiae whereas data set for lycopene production is Y. lipolytica metabolic networks model. Experimental results show that the DEMOMA was able to improve the growth rate for the fumaric acid production rate while for the lycopene production, Biomass Product Coupled Yield (BPCY) and production rate were both able to be optimized

Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

A hybrid of differential search algorithm and flux balance analysis to: Identify knockout strategies for in silico optimization of metabolites production

An increasing demand of naturally producing metabolites has gained the attention of researchers to develop better algorithms for predicting the effects of reaction knockouts. With the success of genome sequencing, in silico metabolic engineering has aided the researchers in modifying the genome-scale metabolic network. However, the complexities of the metabolic networks, have led to difficulty in obtaining a set of knockout reactions, which eventually lead to increase in computational time. Hence, many computational algorithms have been developed. Nevertheless, most of these algorithms are hindered by the solution being trapped in the local optima. In this paper, we proposed a hybrid of Differential Search Algorithm (DSA) and Flux Balance Analysis (FBA), to identify knockout reactions for enhancing the production of desired metabolites. Two organisms namely Escherichia coli and Zymomonas mobilis were tested by targeting the production rate of succinic acid, acetic acid, and ethanol. From this experiment, we obtained the list of knockout reactions and production rate. The results show that our proposed hybrid algorithm is capable of identifying knockout reactions with above 70% of production rate from the wild-type

Overview of Multiobjective Optimization Methods in in Silico Metabolic Engineering

Multiobjective optimization requires of finding a trade-off between multiple objectives. However, most of the objectives are contradict towards each other, thus makes it difficult for the traditional approaches to find a solution that satisfies all objectives. Fortunately, the problems are able to solve by the aid of Pareto methods. Meanwhile, in in silico Metabolic Engineering, the identification of reaction knockout strategies that produce mutant strains with a permissible growth rate and product rate of desired metabolites is still hindered. Previously, Evolutionary Algorithms (EAs) has been successfully used in determining the reaction knockout strategies. Nevertheless, most methods work by optimizing one objective function, which is growth rate or production rate. Furthermore, in bioprocesses, it involves multiple and conflicting objectives. In this review, we aim to show the different multiobjective evolutionary optimization methods developed for tackling the multiple and conflicting objectives in in silico metabolic engineering, as well as the approaches in multiobjective optimization

Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model

The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. Thus, this research aimed to estimate large-scale kinetic parameters of the main metabolic pathway of the E. coli model. In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. Initially, PSO algorithm was adapted to find the globally optimal result based on unorganized particle movement in the search space toward the optimal solution. This development then introduces the Se-PSO algorithm in which the particles are segmented to find a local optimal solution at the beginning and later sought by the PSO algorithm. Additionally, the study proposed an Enhance Se-PSO algorithm to improve the linear value of inertia weigh

NIHBA : A network interdiction approach for metabolic engineering design

Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project ‘Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)’. N.K. was funded by a Royal Academy of Engineering Chair in Emerging Technology award.Peer reviewedPublisher PD

Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei

This work presents an advanced modelling procedure, which applies both structural modelling and kinetic modelling approaches to the trypanothione metabolic network in the bloodstream form of Trypanosoma brucei, the parasite responsible for African Sleeping sickness. Trypanothione has previously been identified as an essential compound for parasitic protozoa, however the underlying metabolic processes are poorly understood. Structural modelling allows the study of the network metabolism in the absence of sufficient quantitative information of target enzymes. Using this approach we examine the essential features associated with the control and regulation of intracellular trypanothione level. The first detailed kinetic model of the trypanothione metabolic network is developed, based on a critical review of the relevant scientific papers. Kinetic modelling of the network focuses on understanding the effect of anti-trypanosomal drug DFMO and examining other enzymes as potential targets for anti-trypanosomal chemotherapy. We also consider the inverse problem of parameter estimation when the system is defined with non-linear differential equations. The performance of a recently developed population-based PSwarm algorithm that has not yet been widely applied to biological problems is investigated and the problem of parameter estimation under conditions such as experimental noise and lack of information content is illustrated using the ERK signalling pathway. We propose a novel multi-objective optimization algorithm (MoPSwarm) for the validation of perturbation-based models of biological systems, and perform a comparative study to determine the factors crucial to the performance of the algorithm. By simultaneously taking several, possibly conflicting aspects into account, the problem of parameter estimation arising from non-informative experimental measurements can be successfully overcome. The reliability and efficiency of MoPSwarm is also tested using the ERK signalling pathway and demonstrated in model validation of the polyamine biosynthetic pathway of the trypanothione network. It is frequently a problem that models of biological systems are based on a relatively small amount of experimental information and that extensive in vivo observations are rarely available. To address this problem, we propose a new and generic methodological framework guided by the principles of Systems Biology. The proposed methodology integrates concepts from mathematical modelling and system identification to enable physical insights about the system to be accounted for in the modelling procedure. The framework takes advantage of module-based representation and employs PSwarm and our proposed multi-objective optimization algorithm as the core of this framework. The methodological framework is employed in the study of the trypanothione metabolic network, specifically, the validation of the model of the polyamine biosynthetic pathway. Good agreements with several existing data sets are obtained and new predictions about enzyme kinetics and regulatory mechanisms are generated, which could be tested by in vivo approaches

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided