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

Computational Design of Auxotrophy-Dependent Microbial Biosensors for Combinatorial Metabolic Engineering Experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools)

Key challenges in designing CHO chassis platforms

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.This work has been supported the Federal Ministry for Digital and Economic Affairs (bmwd), the Federal Ministry for Transport, Innovation and Technology (bmvit), the Styrian Business Promotion Agency SFG, the Standortagentur Tirol, Government of Lower Austria and ZIT - Technology Agency of the City of Vienna through the COMET-Funding Program managed by the Austrian Research Promotion Agency FFG. A.H. has been supported by the Portuguese NORTE-08-5369-FSE-000053 operation. Additional funding came from the PhD program BioToP (Biomolecular Technology of Proteins) of the Austrian Science Fund (FWF Project W1224) and MIT-Portugal PhD program (Bioengineering Systems). The funding agencies had no influence on the conduct of this research. Open Access Funding by the University of Vienna.info:eu-repo/semantics/publishedVersio

Flux balance analysis of metabolic models: a review of recent advances and applications

2016 Spring.Includes bibliographical references.Genome-level reconstructions of metabolic networks have provided new insight into the cellular functions of many organisms. These metabolic models are massive constructs, often including thousands of metabolic and transport reactions and metabolite species for even the most basic organisms. Construction of these models has typically involved an initial genomic analysis to identify known genes or genes with homologous structures for which the function may be inferred, followed by an intensive process of literature searching and experimental validation to refine the model. A number of automated algorithms have been developed to assist with this process. Once the model has been constructed, optimization techniques are applied to predict the distribution of fluxes through the reaction network. The systems then studied by FBA are generally static systems, assumed to be operating at a steady state, and thus constrained by the stoichiometries of the reactions rather than the kinetics. While these assumptions have shown to be valid under select laboratory conditions, evidence indicates that most organisms are not always at this steady state. A number of model improvements have been considered to bring predicted results more in line with experimental data, including the addition of regulatory controls, more detailed incorporation of thermodynamics, and the consideration of metabolite pool and flux data from metabolomics and labeled carbon studies, respectively. The improved predictive capabilities of these models readily find application in metabolic engineering in the custom strain design of organisms. Often this purpose is the production of some valuable bioproduct. This review seeks to give overview the advances made on both the model construction and application ends, with particular emphasis on model improvements via more complex constraints and the incorporation of experimental data

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

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems

Development of an integrated platform for the in silico phenotype simulation of microbial strains

Dissertação de mestrado em BioinformáticaRecentes técnicas de sequenciação genómica e abordagens estão a aumentar constantemente, a uma taxa exponencial, os dados biológicos disponíveis. Esta informação está se tornar ainda mais acessível com o desenvolvimento de novas tecnologias de high-throughout, fazendo com que a simulação in silico de sistemas vivos ou sintéticos se torne mais atrativa. A crescente investigação dos últimos dez anos permitiu o desenvolvimento de modelos matemáticos sofisticados que, por meio de engenharia metabólica, são utilizados numa tentativa de otimizar as funções do organismo, modificando-os geneticamente para produzir compostos de interesse industrial. Em relação ao uso dos modelos de simulação de fenótipo, métodos como o Flux Balance Analysis são utilizados para identificar conjuntos de manipulações genéticas que resultam em estirpes mutantes capazes de produzir compostos desejados. Variações desses métodos têm rapidamente surgido e, em paralelo, um grande número de ferramentas computacionais surgiram com a capacidade de realizar esses métodos. Todas estas ferramentas estão disponíveis para a comunidade e construídas em diferentes sistemas e linguagens. No entanto, nenhuma inclui todos os métodos relevantes, o que resulta numa necessidade de recurso a mais do que uma ferramenta para realizar certas tarefas. Neste trabalho, uma plataforma que pode integrar todos estes métodos é apresentada com o objetivo de centralizar e integrar os métodos de simulação de fenótipo mais relevantes e também permitir a sua extensão fácil com outros métodos. Para este trabalho, foi realizado um estudo dos métodos e ferramentas mais relevantes de modo a que esta plataforma possa integrar os métodos e ferramentas mais significativas. Esta plataforma é dividida em duas camadas: uma camada de ligação que é responsável pela troca de informação através das ferramentas computacionais e línguas, e uma camada de formulação responsável por executar os métodos dessas ferramentas e também apresentar os seus resultados. Através destas camadas, a plataforma pode executar métodos de qualquer ferramenta disponível construída em qualquer linguagem computacional e fornecer aos investigadores o acesso a todos os métodos em uma única plataforma. Como aplicação prática desta plataforma, foi desenvolvido um plugin e integrado no OptFlux, um software de código aberto para apoiar as tarefas de engenharia metabólica, e encontra-se disponível em www.optflux.org. Este plugin oferece uma ligação com a ferramenta COBRA e executa os métodos de simulação metabólicas mais relevantes presentes no último.Modern sequencing techniques and omics approaches are constantly increasing available biological data at an exponential rate. This information is becoming even more accessible with the development of new high-throughout technologies, making in silico simulation of living and synthetic systems more attractive. The growing research in the past decade allowed the development of sophisticated mathematical models that, through Metabolic Engineering, are used in an attempt to optimize organism's functions, genetically modifying them to produce compounds of industrial interest. Regarding the use of models for phenotype simulation, methods such as Flux Balance Analysis are used to identify sets of genetic manipulations that result in mutant strains capable of producing desired compounds. Variations of these methods have rapidly appeared and in parallel, a great number of computational tools emerged with the capability of performing these methods. All these tools are available for the community and built in different systems and languages. However, none includes all the relevant methods, which results in a need of recurring to more than one tool to accomplish certain tasks. In this work, a platform that can integrate all these methods is presented with the goal of centralizing and integrating the most relevant existing phenotype simulation methods and also enabling their easy extension with other methods. For this work, a study of the most relevant methods and tools was made so that this platform could integrate the most significant methods and tools. This platform is divided in two layers: a connection layer that is responsible for exchanging information through the computational tools and languages, and a formulation layer responsible for performing the methods from these tools and also presenting the results. Through these layers, the platform can perform methods from any available tool built in any computational language and provide to the researchers the access to all methods in a single platform. As a practical implementation of this platform, a plugin was developed and integrated in OptFlux, an open-source software to support metabolic engineering tasks, available at www.optflux.org. This plugin provides a connection with the COBRA Toolbox and performs the most relevant metabolic simulation methods present in the latter