In silico design of improved cell factories - New methods and experimental validation

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Systems biology for the development of microbial cell factories

Industrial Biotechnology is increasingly replacing chemical processes in numerous industrial sectors since it allows the use of renewable raw-materials and provides a more sustainable manufacturing base. The field of Metabolic Engineering (ME) has thus gained a major importance since it allows the design of improved microorganisms for industrial applications. However, in Metabolic Engineering problems, it is often difficult to predict the effects of genetic modifications on the resulting microbial phenotype, owing to the complexity of metabolic networks. Consequently, the task of identifying the modifications that will lead to an improved microbial phenotype is a quite complex one, requiring robust mathematical and computational tools. In this presentation I will focus in some of our efforts in these fields, namely in the generation of better mathematical models of microbial metabolism and the development of reliable and effective computational and mathematical methods for the design of rational metabolic engineering strategies Furthermore, I will introduce the open-source software tool developed in house, called OptFlux (www.OptFlux.org), that allows researchers both from industry and academia to simulate, in a user-friendly way, the behavior of industrially important microorganisms under a variety of conditions and also indicates which genetic modifications may lead to enhanced strains for a particular application

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

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

New tools for the simulation and optimization of microbes in metabolic engineering problems

Industrial Biotechnology is increasingly replacing chemical processes in numerous industrial sectors since it allows the use of renewable raw-materials and provides a more sustainable manufacturing base. The field of Metabolic Engineering (ME) has thus gained a major importance since it allows the design of improved microorganisms for industrial applications, starting with wild-type strains that usually have low production capabilities in terms of the target compounds. The ultimate aim of ME is to identify genetic manipulations in silico leading to improved microbial strains, that can be implemented using novel molecular biology techniques. This task, however, is a complex one, requiring the existence of reliable (genome-scale-) metabolic models for strain simulation and robust optimization algorithms for target identification. Strain simulation is usually performed by using Linear or Quadratic Programing methods that assume a steady state over the intracellular metabolites. However, there is no guarantee that the engineered cells actually function according to the optimal pathway predicted by these methods. In this scope, we have been working towards the use of a modification of the concept of Control Effective Fluxes to be able to find Metabolic Engineering solutions that couple growth with product formation while considering optimal, as well as sub-optimal routes and their efficiency. Regarding strain optimization, the most common task is to solve a bi-level optimization problem, where the strain that maximizes the production of a given compound is sought, while trying to keep the organism viable. Several different algorithms have been proposed to address this problem, namely mixed integer linear programming. More recently, we have proposed the use of stochastic meta-heuristics, such as Evolutionary Algorithms (EAs) and Simulated Annealing (SA). These approaches allow to solve the Metabolic Engineering problem in a considerable shorter time, originating a family of (sub)optimal solutions. Moreover, they are quite flexible regarding the use of non-linear objective functions. However, so far optimization approaches have been limited to the tasks of selecting the best set of genes to knockout from an organism. To extend the manipulation possibilities, we have been using both dynamic and steady-state models in modified formulations to account for gene over and under expression. In this way, it is possible to indicate the set of genes that should be modified, the type of modification that should be performed and the degree of over and underexpression. These algorithms have been validated with different case-studies, namely the production of lactic and succinic acid with E. coli and S. cerevisiae and some are already available in the open source and user friendly software tool Optflux

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

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

Removing zero Lyapunov exponents in volume-preserving flows

Baraviera and Bonatti proved that it is possible to perturb, in the c^1 topology, a volume-preserving and partial hyperbolic diffeomorphism in order to obtain a non-zero sum of all the Lyapunov exponents in the central direction. In this article we obtain the analogous result for volume-preserving flows.Comment: 10 page

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