Evaluating evolutionary multiobjective algorithms for the in silico optimization of mutant strains

In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. Evolutionary Algorithms (EAs) have been a successful approach for tackling the underlying in silico optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes the production of some compound is sought, while trying to keep the organism viable (maximizing biomass). In this work, this task is viewed as a multiobjective optimization problem and an approach based on multiobjective EAs is proposed. The algorithms are validated with a real world case study that uses E. coli to produce succinic acid. The results obtained are quite promising when compared to the available single objective algorithms.This work was supported by the Portuguese FCT project POSC/EIA/59899/200

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

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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Utilization of thermodynamically-weighted elementary modes for flux reconstruction of <it>E. coli </it>central metabolism revealed two clusters of EMs with respect to their Δ<it>G</it>_<it>p</it>°. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in <it>E. coli</it>. 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.</p> <p>Conclusion</p> <p>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 <it>E. coli</it>. 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.</p

Development of a framework for metabolic pathway analysis-driven strain optimization methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.The authors thank the project “DeYeastLibrary—Designer yeast strain library optimized for metabolic engineering applications”, Ref. ERA-IB-2/0003/2013, funded by national funds through “Fundação para a Ciência e Tecnologia / Ministério da Ciência, Tecnologia e Ensino Superior”.info:eu-repo/semantics/publishedVersio

Evolutionary computation for predicting optimal reaction knockouts and enzyme modulation strategies

One of the main purposes of Metabolic Engineering is the quantitative prediction of cell behaviour under selected genetic modifications. These methods can then be used to support adequate strain optimization algorithms in a outer layer. The purpose of the present study is to explore methods in which dynamical models provide for phenotype simulation methods, that will be used as a basis for strain optimization algorithms to indicate enzyme under/over expression or deletion of a few reactions as to maximize the production of compounds with industrial interest. This work details the developed optimization algorithms, based on Evolutionary Computation approaches, to enhance the production of a target metabolite by finding an adequate set of reaction deletions or by changing the levels of expression of a set of enzymes. To properly evaluate the strains, the ratio of the flux value associated with the target metabolite divided by the wild-type counterpart was employed as a fitness function. The devised algorithms were applied to the maximization of Serine production by Escherichia coli, using a dynamic kinetic model of the central carbon metabolism. In this case study, the proposed algorithms reached a set of solutions with higher quality, as compared to the ones described in the literature using distinct optimization techniques.This work is funded by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011. The work is also partially funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT within project ref. COMPETE FCOMP-01-0124- FEDER-015079. PEs work is supported by a PhD grant FCT SFRH/BD/51016/2010 from the Portuguese FCT

MOMO - multi-objective metabolic mixed integer optimization : application to yeast strain engineering

BACKGROUND: In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS: Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS: The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr

A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling

Multiscale computational models integrating sub-cellular, cellular, and multicellular levels can be powerful tools that help researchers replicate, understand, and predict multicellular biological phenomena. To leverage their potential, these models need correct parameter values, which specify cellular physiology and affect multicellular outcomes. This work presents a robust parameter optimization method, utilizing a parallel and distributed genetic-algorithm software package. A genetic algorithm was chosen because of its superiority in fitting complex functions for which mathematical techniques are less suited. Searching for optimal parameters proceeds by comparing the multicellular behavior of a simulated system to that of a real biological system on the basis of features extracted from each which capture high-level, emergent multicellular outcomes. The goal is to find the set of parameters which minimizes discrepancy between the two sets of features. The method is first validated by demonstrating its effectiveness on synthetic data, then it is applied to calibrating a simple mechanical model of biofilm wrinkling, a common type of morphology observed in biofilms. Spatiotemporal convergence of cellular movement derived from experimental observations of different strains of Bacillus subtilis colonies is used as the basis of comparison

MOMO - multi-objective metabolic mixed integer optimization : application to yeast strain engineering

In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP‡ as underlying multi-objective solver. Momo is available at http://momo-sysbio.gforge.inria.f

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.This work was partially funded by a Teesside University doctoral scholarship, EPSRC, and the EU grant MIMOMICS