Optimization approaches for the in silico discovery of optimal targets for gene over/underexpression

Metabolic engineering (ME) efforts have been recently boosted by the increase in the number of annotated genomes and by the development of several genome-scale metabolic models for microbes of interest in industrial biotechnology. Based on these efforts, strain optimization methods have been proposed to reach the best set of genetic changes to apply to selected host microbes, in order to create strains that are able to overproduce metabolites of industrial interest. Previous work in strain optimization has been mostly based in finding sets of gene (or reaction) deletions that lead to desired phenotypes in computational simulations. In this work, we focus on enlarging the set of possible genetic changes, considering gene over and underexpression. A gene is considered under (over) expressed if its expression value is constrained to be significantly lower (higher) than the one in the wild-type strain, used as a reference. A method is proposed to propagate relative gene expression values to flux constraints over related reactions, making use of the available transcriptional/ translational information. The algorithms chosen for the optimization tasks are metaheuristics such as eolutionary agorithm (EA) and smulated anealing (SA), based on previous successful work on gene knockout optimization. These methods were modified appropriately to accommodate the novel optimization tasks and were applied to study the optimization of succinic and lactic acid production using Escherichia coli as the host. The results are compared with previous ones obtained in gene knockout optimization, thus showing the usefulness of the approach. The methods proposed in this work were implemented in a novel plug-in for OptFlux, an open-source software framework for ME. Supplementary Material is available at www.liebertonline.com/cmb

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)

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

Análise dos fluxos metabólicos em Saccharomyces cerevisiae a partir de D-xilulose como fonte de carbono utilizando Optflux

A viabilidade econômica da produção de etanol 2G depende da eficiente fermentação da fração hemicelulósica por S. cerevisiae. A xilulose, isômero da xilose e principal componente da hemicelulose do bagaço, pode ser convertida pela levedura em biomassa, etanol, xilitol ou outros metabólitos. A otimização da produção de etanol requer a análise do metabolismo da xilulose. Modelos metabólicos permitem efetuar simulações de sistemas biológicos, viabilizando o estudo in silico das respostas celulares perante perturbações ambientais e genéticas. Uma das técnicas mais usadas para estudos deste tipo é a Análise do Balanço de Fluxos Metabólicos (do inglês, FBA). Neste trabalho, usando o software OptFlux, foi aplicado o método “parcimonious FBA” ao modelo iND750 para estimar os fluxos metabólicos em condições de aerobiose e anaerobiose, utilizando xilulose como fonte de carbono. Os resultados das simulações foram comparados a dados experimentais e o modelo foi ajustado adicionando restrições de expressão em fluxos metabólicos da via pentose fosfato e de formação de biomassa. A produção de etanol só foi observada na condição anaeróbia, com favorecimento da seletividade para baixos fluxos de xilulose

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy

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

The current status of pharmacotherapy for the treatment of Parkinson's disease: transition from single-target to multitarget therapy

Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons. Motor features such as tremor, rigidity, bradykinesia and postural instability are common traits of PD. Current treatment options provide symptomatic relief to the condition but are unable to reverse disease progression. The conventional single-target therapeutic approach might not always induce the desired effect owing to the multifactorial nature of PD. Hence, multitarget strategies have been proposed to simultaneously target multiple proteins involved in the development of PD. Herein, we provide an overview of the pathogenesis of PD and the current pharmacotherapies. Furthermore, rationales and examples of multitarget approaches that have been tested in preclinical trials for the treatment of PD are also discussed

Developing computational tools for studying cancer metabolism and genomics

The interplay between different genomic and epigenomic alterations lead to different prognoses in cancer patients. Advances in high-throughput technologies, like gene expression profiling, next-generation sequencing, proteomics, and fluxomics, have enabled detailed molecular characterization of various tumors, yet studying this interplay is a complex computational problem.Here we set to develop computational approaches to identify and study emerging challenges in cancer metabolism and genomics. We focus on three research questions, addressed by different computational approaches: (1) What is the set of metabolic interactions in cancer metabolism? To this end we generated a computational framework that quantitatively predicts synthetic dosage lethal (SDL) interactions in human metabolism, by developing a new algorithmic-modeling approach. SDLs offer a promising way to selectively kill cancer cells by targeting the SDL partners of activated oncogenes in tumors, which are often difficult to target directly. (2) What is the landscape of metabolic regulation in breast cancer? To this end we established a new framework that utilizes different data types to perform multi-omics data integration and flux prediction, by incorporating machine learn- ing techniques with Genome Scale Metabolic Modeling (GSMM). This enabled us to study the regulation of breast cancer cell line under different growth conditions, from multiple omics data. (3) What is the power of somatic mutations derived from RNA in estimating the tumor mutational burden? Here we develop a new tool to detect somatic mutations from RNA sequencing data without a matched- normal sample. To this end we developed a machine learning pipeline that takes as input a list of single nucleotide variants and classifies them as either somatic or germline, based on read-level features as well as position-specific variant statistics and common germline databases. We showed that detecting somatic mutations directly from RNA enables the identification of expressed mutations, and therefore represent a more relevant metric in estimating the tumor mutational burden, which is significantly associated with patient survival. In sum, my work has been focused around developing computational methods to tackle different research questions in cancer metabolism and genomics, utilizing various types of omics data and a variety of computational approaches. These methods provide new solutions to some important computational challenges, and their applications help to generate promising leads for cancer research, and can be utilized in many future applications, analyzing novel and existing datasets