On the effects of phenotype prediction methods over strain design algorithms. A multi-objective approach

The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, several methods for the prediction of phenotypes were also put forward. With the ever-growing information provided by such methods, new questions arose. Metabolic Engineering in particular posed some interesting questions. Recently, Schuetz and co-workers proposed that the metabolism of bacteria operates close to the Pareto-optimal surface of a three-dimensional space definedned by competing objectives and demonstrated the validity of their claims for various environmental perturbations. However, phenotype prediction methods have all been developed to operate based on the assumption of a given single-objective, as an example Flux Balance Analysis (FBA) often assumes that the organisms are evolutionarily optimized towards optimal growth. On the other hand, Minimization of Metabolic Adjustment (MOMA) proposes that after a perturbation, the goal of the organisms shifts from optimal growth to the minimization of the global metabolic adjustment relative to the wild-type. Albeit multi-objective approaches focused on the bio-engineering objectives have been proposed, none tackles the multi-objective nature of the cellular objectives. In this work we analyze the inuence of several phenotype prediction methods on the strainsdesigned by metaheuristic algorithms and suggest a multi-objective approach capable of finding designs compliant with the cellular objectives assumed by the various phenotype prediction methods. Using a recent model of Escherichia coli K12, we observed the effect of different phenotype prediction methods in the convergence of metaheuristic algorithms performing strain optimization, evolving growth-coupled production mutants in aerobic and anaerobic conditions. A critical analysis of the different mutant ux distributions was performed, and we concluded that, for a selected phenotype prediction method, the strain designs proposed by the optimization algorithms were generally not robust when another method was used to predict their phenotypes. There is variation in the Biomass-product coupled yield (BPCY) of aerobically succinate producing mutants with glucose as carbon source, when solutions generated with either pFBA (a variation of FBA that minimizes the overall use of enzyme-associated flux) or LMOMA (a linear implementation of MOMA) (box colors) are simulated with the other (x-axis). Besides the great variation in fitness for the different phenotype simulation methods, we veri_ed that in some cases less than 10% of the solutions generated by pFBA are valid in LMOMA (BPCY _ 0:0001). Assumptions regarding the cellular objectives of an organism when subjected to distinct conditions (environmental, genetic, etc.) are still the object of active discussion. This fact motivated us to develop a method capable of suggesting designs compliant with more than one phenotype prediction method. Solutions generated by our method are simulated using pFBA and LMOMA and plotted by BPCY for both phenotype simulation methods. The ad-hoc clusters reveal a group of interesting solutions (cluster 2). An analysis on the flux distribution of the solutions presented in these clusters is also provided and a rational for robust solution design is derived

Modelling fed-batch fermentation processes: An approach based on artificial neural networks

Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work, a set of computational tools are proposed, allowing researchers in Biotechnology to use ANNs for the modelling of fed-batch fermentation processes. The main task is to predict the values of kinetics parameters from the values of a set of state variables. The tools were validated with two case studies, showing the main functionalities of the application.This work was supported by the Portuguese FCT under project POSC/EIA/59899/2004

OptFerm - a computational platform for the optimization of fermentation processes

We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for Bioengineering process optimization that can be used to increase productivity. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Also, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The software was built using a component-based modular development methodology, using Java as the programming language. AlBench. a Model-View-Control based application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Also, this allows the tool to be easily extended with new modules, currently being developed

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

Computational tools for strain optimization by tuning the optimal level of gene expression

In this work, a plug-in for the OptFlux Metabolic Engineering platform is presented, implementing methods that allow the identification of sets of genes to over/under express, relatively to their wild type levels. The optimization methods used are Simulated Annealing and Evolutionary Algorithms, working with a novel representation and operators. This overcomes the limitations of previous approaches based solely on gene knockouts, bringing new avenues for Biotechnology, fostering the discovery of genetic manipulations able to increase the production of certain compounds using a host microbe. The plug-in is made freely available together with appropriate documentation.Support of FCT and Programa COMPETE (ref. PTDC/EIA-EIA/ 115176/2009)

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