A study on the robustness of strain optimization algorithms

5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011), ISBN 978-3-642-19913-4In recent years, there have been considerable advances in the use of genome-scale metabolic models to provide accurate phenotype simulation methods, which in turn enabled the development of efficient strain optimization algorithms for Metabolic Engineering. In this work, we address some of the limitations of previous studies regarding strain optimization algorithms, mainly its use of Flux Balance Analysis in the simulation layer.We perform a thorough analysis of previous results by relying on Flux Variability Analysis and on alternative methods for phenotype simulation, such as ROOM. This last method is also used in the simulation layer, as a basis for optimization, and the results obtained are also the target of thorough analysis and comparison with previous ones.Fundação para a Ciência e a Tecnologia (FCT) - project MIT-PT/BS-BB/0082/200

Visualizing omics data in the OptFlux workbench

OptFlux is an open-source and extensible workbench for Metabolic Engineering (ME) tasks. Since 2012, OptFlux adoption has been steadily increasing among users, making it one of the reference go-to platforms among the ME community. The workbench supports common ME tasks such as phenotype prediction for both wild-type and mutant genotypes, metabolic control analysis and pathway analysis as well as strain optimization procedures. Moreover, a visualization plug-in is included, allowing the navigation and edition of biochemical network layouts in a multitude of standard formats. This plug-in also allows the overlap of specific phenotypic conditions in the network layouts, providing an intuitive mechanism to explore and understand the associated flux distributions. Navigation between multiple layouts is also included. However, for more specialized applications, such as the inclusion of experimental data, this framework was still lagging behind. In this work, the current visualization platform included in OptFlux is extended to support loading generic experimental data sources (e.g. transcript, protein, metabolite and flux measurements) and mapping it to the model information for posterior overlap with the layouts. The visualization features that will represent this data are also fully customizable. The inclusion of multiple conditions or time-dependent measurements is also supported for metabolite-associated data with intuitive bar-plots being displayed for immediate visual comparison. Finally, compound structural information from KEGG is also automatically downloaded and presented

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

In silico optimization of the production of amino-acids in Escherichia coli

The increasing need to replace chemical synthesis of compounds of interest by more environmentally friendly biological processes is driving the research for microbial cell factories. The industrial production of amino and organic acids includes several examples of success stories using microorganisms to convert inexpensive substrates into added value products. Traditionally, the design of such microbes relied on cycles of random mutagenesis followed by phenotypic selection [1], but a deeper knowledge of the microbial physiology allowed a more rational approach to this optimization problem [2,3]. However, this task is not straightforward, since the cell metabolism has proved to be highly complex and hard to predict. One of the approaches to tackle this problem is to use Systems Biology simulation tools to predict the microorganism behavior when subjected to genetic modifications. Using genome scale stoichiometric models, such as the latest iAF1260 for Escherichia coli [4] one can simulate a great diversity of possible metabolic phenotypes under steady state conditions by imposing flux-balance constrains. The use of flux balanced analysis (FBA) allows the determination of flux values through all the reactions in the network under a set of environmental conditions and genetic manipulations, by using an objective function, such as the maximization of growth [5]. In this work, we used genetic algorithms, such as OptGene [6] to search for sets of gene knockouts that result in the overproduction in silico of amino-acids in Escherichia coli. From all the proteinogenic amino-acids, glycine yielded the best results in the optimizations. A careful analysis of the in silico flux distribution in some of the mutants revealed an interesting and non-intuitive mechanism behind glycine accumulation. Furthermore, in these mutants the growth is coupled to the production of glycine, which makes them excellent candidates for in vivo implementation. We are reaching a point where bioinformatics tools are advanced enough to aid in complex tasks, such as the optimization of microbial cell factories. Here we described an effort to optimize in silico the production of amino-acids in Escherichia coli, which resulted in the discovery of a potential set of knock-outs that leads to glycine overproduction. This serves to show the increasing importance of in silico optimizations to aid in the metabolic engineering projects, especially to search for non-intuitive beneficial genome modifications

An algorithm to assemble gene-protein-reaction associations for genome-scale metabolic model reconstruction

The considerable growth in the number of sequenced genomes and recent advances in Bioinformatics and Systems Biology fields have provided several genome-scale metabolic models (GSMs) that have been used to provide phenotype simulation methods. Given their importance in biomedical research and biotechnology applications (e.g. in Metabolic Engineering efforts), several workflows and computational platforms have been proposed for GSM reconstruction. One of the challenges of these methods is related to the assignment of gene-protein-reaction (GPR) associations that allow to add transcriptional/ translational information to GSMs, a task typically addressed through manual literature curation. This work proposes a novel algorithm to create a set of GPR rules, based on the integration of the information provided by the genome annotation with information on protein composition and function (protein complexes, sub-units, iso-enzymes, etc.) provided by the UniProt database. The methods are validated by using two state-of-the-art models for E. coli and S. cerevisiae, with competitive results.The 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 Tech- nology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEst-OE/EEI/UI0752/2011

Development and application of efficient pathway enumeration algorithms for metabolic engineering applications

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modelling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and limitations are evaluated when designing novel heterologous pathways, by applying these methods on three case studies of synthetic ME related to the production of non-native compounds in E. coli and S. cerevisiae: 1-butanol, curcumin and vanillin. Some targeted improvements are implemented, extending both methods to address limitations identified that impair their scalability, improving their ability to extract potential pathways over large-scale databases. In all case-studies, the algorithms were able to find already described pathways for the production of the target compounds, but also alternative pathways that can represent novel ME solutions after further evaluation.The 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 Strategic Project PEst-OE/EQB/LA0023/2013, and also by Project 23060, PEM - Technological Support Platform for Metabolic Engineering, co-funded by FEDER through Portuguese QREN under the scope of the Technological Research and Development Incentive system, North Operational

SafeRegions: performance evaluation of multi-party protocols on HBase

On-line applications and services are now a critical part of our everyday life. Using these services typically requires us to trust our personal or company's information to a large number of third-party entities. These entities enforce several security measures to avoid unauthorized accesses but data is still stored on common database systems that are designed without data privacy concerns in mind. As a result, data is vulnerable against anyone with direct access to the database, which may be external attackers, malicious insiders, spies or even subpoenas. Building strong data privacy mechanisms on top of common database systems is possible but has a significant impact on the system's resources, computational capabilities and performance. Notably, the amount of useful computation that may be done over strongly encrypted data is close to none, which defeats the purpose of offloading computation to third-party services. In this paper, we propose to shift the need to trust in the honesty and security of service providers to simply trust that they will not collude. This is reasonable as cloud providers, being competitors, do not share data among themselves. We focus on NoSQL databases and present SafeRegions, a novel prototype of a distributed and secure NoSQL database that is built on top of HBase and that guarantees strong data privacy while still providing most of HBase's query capabilities. SafeRegions relies on secret sharing and multiparty computation techniques to provide a NoSQL database built on top of multiple, non-colluding service providers that appear as a single one to the user. Strikingly, service providers, individually, cannot disclose any of the user's data but, together, are able to offer data storage and processing capabilities. Additionally, we evaluate SafeRegions exposing performance trade-offs imposed by security mechanisms and provide useful insights for future research on performance optimization

TDPS - Turnover dependent phenotypic simulation: a quantitative constraint-based simulation method that accommodates all main strain design strategies

Constraint-based modelling methodologies can expedite the strain engineering process by helping in the search for interesting genetic modification targets. Although the search for gene knock-outs is fairly established with in silico methodologies, most computational strain design methods still model gene up/down-regulations by forcing the corresponding flux values to pre-calculated levels without considering the availability of resources. We have developed a new simulation method, Turnover Dependent Phenotypic Simulation (TDPS), which was designed with the goal of simulating quantitatively the phenotype of strains with diverse genetic modifications in a resource conscious manner. Besides gene deletions and down-regulations, TDPS can also simulate the up-regulation of metabolic reactions as well as the introduction of heterologous genes or the activation of dormant reactions. In TDPS the flux values through modified metabolic reactions are modelled by taking into consideration the availability of precursor metabolites in the network, which is accomplished by assuming that the production turnover of a metabolite can be used as an indication of its abundance. The developed method is based on a MILP formulation that manipulates the fractions of metabolite turnovers consumed by the modified reactions. Furthermore, TDPS also integrates a new objective function that promotes network rigidity in order to predict the flux phenotype of modified strains. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite

A study on the effects of using gene-reaction rules on in silico strain optimization

To identify a set of genetic manipulations that will result in a microbial strain with improved production capabilities of a metabolite / product of industrial interest, is one of the greatest challenges in Metabolic Engineering. This problem represents a complex combination between the development of accurate metabolic and regulatory models / networks, plus the need for appropriate simulation and optimization tools. To achieve this end, Evolutionary Algorithms (EAs) and Simulation Annealing (SA) have been previously proposed as tools to perform in silico Metabolic Engineering [1]. These methods are used to identify sets of reaction deletions, towards the maximization of a desired physiological objective function. In order to simulate the cell phenotype for each mutant strain, including its growth and the by-products secretion, the Flux-Balance Analysis approach is used, assuming that microorganisms have maximized their growth along evolution. Currently, the available optimization algorithms work only with reaction deletions, i.e. their result is a set of reactions that have to be removed from the metabolic model. Biologically, it is possible to knockout genes, not reactions. In this work, the transcriptional information is added to the underlying models using gene-reaction rules based on a boolean logic representation. So, for each reaction we have a Boolean expression, where the variables are the encoding genes and including the logical AND and OR operators. The aim is to find the optimal / near-optimal set of gene knockouts necessary to reach a given productivity goal. The results obtained are compared with the ones using the deletion of reactions. A set of computational experiments were performed, using four case studies and the production of succinate and lactic acid as the metabolite to maximize and E. coli as the selected organism. Genome-scale models including both reactions and gene-reaction rules [2] are used to conduct the necessary FBA simulations. The results show that several of the results from reaction deletion optimizations are not feasible using the provided gene-reaction rules, i.e. the genes that would need to be removed in order to delete the reaction also lead to the removal of other reactions causing side effects that make the strain unviable. Nevertheless, basing the optimization algorithms on gene knockouts, we were able to reach solutions where the production of the desired compounds is similar to the ones using reaction deletions.MIT-P

Endocarditis, Meningitis and Pneumocystis Pneumonia

Infective endocarditis (IE) is associated with high morbidity and mortality despite advances in antibiotic and surgical treatment. Systemic embolism occurs in up to 49% of IE patients and may involve the major arteries, limb arteries, viscera and the central nervous system. In this report we describe a 60-year-old female patient with a history of acute lymphoblastic leukaemia who presented with endocarditis manifesting as stroke, acute limb ischaemia and meningitis. Early diagnosis is essential since treatment lowers the risk of embolism, with most events occurring within 2 weeks of treatment initiatio