Natural computation meta-heuristics for the in silico optimization of microbial strains

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for <it>in silico </it>metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.</p> <p>Results</p> <p>This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of <it>in silico </it>metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using <it>S. cerevisiae </it>and <it>E. coli </it>as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs.</p> <p>Conclusion</p> <p>The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.</p

Large-scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.Hertz Foundatio

Large-scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing

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

Metaheuristics for strain optimization using transcriptional information enriched metabolic models

Publicado em "Evolutionary computation, machine learning and data mining in bioinformatics : 8th European Conference, EvoBIO 2010...", ISBN 978-3-642-12210-1The identification of a set of genetic manipulations that result in a microbial strain with improved production capabilities of a metabolite with industrial interest is a big challenge in Metabolic Engineering. Evolutionary Algorithms and Simulated Annealing have been used in this task to identify sets of reaction deletions, towards the maximization of a desired objective function. To simulate the cell phenotype for each mutant strain, the Flux Balance Analysis approach is used, assuming organisms have maximized their growth along evolution. In this work, transcriptional information is added to the models using gene-reaction rules. The aim is to find the (near-)optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones reached using the deletion of reactions, showing that we obtain solutions with similar quality levels and number of knockouts, but biologically more feasible. Indeed, we show that several of the previous solutions are not viable using the provided rules.This work was partially funded by Portuguese FCT through the AspectGrid project and also through project MIT-PT/BS-BB/0082/2008

An integrated framework for strain optimization

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/).This work is partially funded by ERDF- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP- 01-0124-FEDER-015079 and PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009

A generic multi-criterion approach for mutant strain optimization

Motivation: The identification of genetic modifications that can lead to mutant strains that overproduce compounds of industrial interest is a challenging task in Metabolic Engineering. Evolutionary Algorithms and other metaheuristics have provided successful methods for solving the underlying in silico bi-level optimization problems (e.g. to find the best set of gene knockouts) [1]. Although these algorithms perform well in some criteria, they lose sense of the inner multi-objective nature of these problems. Results: In this work, these tasks are viewed as multi-objective optimization problems and algorithms based on multi-objective EAs are proposed. The objectives include maximizing the production of the compound of interest, maximizing biomass and minimizing the number of knockouts. Furthermore, a generalization to integrate multiple-criterion capabilities into single-objective algorithms is proposed and implemented as an ensemble method. This new approach allows taking advantage of the solution space sampling capabilities of some algorithms (e.g. Simulated Annealing), while generating the set of solutions (Pareto-front) according to the multiobjective premises. The algorithms are validated with two case studies, where E. coli is used to produce succinate and lactate. Results show that this option provides an efficient alternative to the previous approaches, returning not a single solution, but rather sets of solutions that are trade-offs among the distinct objective functions. Availability: Algorithms are implemented as a plug-in for the open-source OptFlux [2] platform available in the site http://www.optflux.org

Multi-criterion approaches for the in silico optimization of mutant microorganisms

create mutants for the production of valuable compounds is, in Metabolic Engineering, a promising, while complex task. Approaches to tackle this problem have been proposed and included MILP-based techniques (OptKnock [1]), that lacked the possibility of including non-linear objective functions. More recently, meta-heuristic approaches like Evolutionary Algorithms (EAs) (OptGene [2]) have been put forward. Although these are more flexible and have provided good results in some cases, they relied on objective functions that aggregate several potentially conflicting optimization goals. In this work, the problem is interpreted as a Multi-Objective task and an approach based on Multi-Objective EAs (MOEAs) is proposed. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), two state-of-the-art MOEAs were adapted to conduct this task. The optimization goals are to simultaneously maximize the biomass and also the concentration of a desired compound (bi-objective optimization problems). Next, we took the problem further and added two new objectives: minimizing either the number of knockouts in the solution or the sum of all the fluxes present in the model (tri-objective optimization problems). These algorithms are validated using three real world case studies. The selected organisms are S.cerevisiae for the production of succinate and E.coli for the production of both succinate and lactate. The results are quite promising when compared with the available single-objective approaches