Development of a framework for metabolic pathway analysis-driven strain optimization methods

Genome-scale metabolic models (GSMMs) have become important assets for rational design of compound overproduction using microbial cell factories. Most computational strain optimization methods (CSOM) using GSMMs, while useful in metabolic engineering, rely on the definition of questionable cell objectives, leading to some bias. Metabolic pathway analysis approaches do not require an objective function. Though their use brings immediate advantages, it has mostly been restricted to small scale models due to computational demands. Additionally, their complex parameterization and lack of intuitive tools pose an important challenge towards making these widely available to the community. Recently, MCSEnumerator has extended the scale of these methods, namely regarding enumeration of minimal cut sets, now able to handle GSMMs. This work proposes a tool implementing this method as a Java library and a plugin within the OptFlux metabolic engineering platform providing a friendly user interface. A standard enumeration problem and pipeline applicable to GSMMs is proposed, making use by the community simpler. To highlight the potential of these approaches, we devised a case study for overproduction of succinate, providing a phenotype analysis of a selected strategy and comparing robustness with a selected solution from a bi-level CSOM.The authors thank the project “DeYeastLibrary—Designer yeast strain library optimized for metabolic engineering applications”, Ref. ERA-IB-2/0003/2013, funded by national funds through “Fundação para a Ciência e Tecnologia / Ministério da Ciência, Tecnologia e Ensino Superior”.info:eu-repo/semantics/publishedVersio

Comparison of pathway analysis and constraint-based methods for cell factory design

Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs.“DeYeastLibrary – Designer yeast strain library optimized for metabolic engineering applications”, Ref.ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES, DD-DeCaf and SHIKIFACTORY100, both funded by the European Union through the Horizon 2020 research and innovation programme (grant agreements no. 686070 and 814408). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors acknowledge the use of computing facilities within the scope of the Search-ON2: Revitalization of HPC infrastructure of UMinho” project (NORTE-07-0162-FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF). VV also thanks funding from FCT/MCTES for the PhD studentship with reference SFRH/BD/118657/2016.info:eu-repo/semantics/publishedVersio

Integrated network flow model for a reliability assessment of the national electric energy system

Electric energy availability and price depend not only on the electric generation and transmission facilities, but also on the infrastructure associated to the production, transportation, and storage of coal and natural gas. As the U.S. energy system has grown more complex and interdependent, failure or degradation on the performance of one or more of its components may possibly result in more severe consequences in the overall system performance. The effects of a contingency in one or more facilities may propagate and affect the operation, in terms of availability and energy price, of other facilities in the energy grid. In this dissertation, a novel approach for analyzing the different energy subsystems in an integrated analytical framework is presented, by using a simplified representation of the energy infrastructure structured as an integrated, generalized, multi-period network flow model. The model is capable of simulating the energy system operation in terms of bulk energy movements between the different facilities and prices at different locations under different scenarios. Assessment of reliability and congestion in the grid is performed through the introduction and development of nodal price-based metrics, which prove to be especially valuable for the assessment of conditions related to changes in the capacity of one or more of the facilities. Nodal price-based metrics are developed with the specific objectives of evaluating the impact of disruptions and of assessing capacity expansion projects. These metrics are supported by studying the relationship between nodal prices and congestion using duality theory. Techniques aimed at identifying system vulnerabilities and conditions that may significantly impact availability and price of electrical energy are also developed. The techniques introduced and developed through this work are tested using 2005 data, and special effort is devoted to the modeling and study of the effects of hurricanes Katrina and Rita in the energy system. In summary, this research is a step forward in the direction of an integrated analysis of the electric subsystem and the fossil fuel production and transportation networks, by presenting a set of tools for a more comprehensive assessment of congestion, reliability, and the effects of disruptions in the U.S. energy grid

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Unbiased analysis of metabolite exchanges in metabolic models

A metabolic network is the system of biochemical reactions that sustain the life of an organism. The genetics of a given organism is highly involved in the reaction fluxes in the network. In recent years, large amounts of genomic data have been obtained from a variety of organisms. This data can be integrated in mathematical models of a network, enabling both prediction and understanding of diverse metabolic networks under a wide variety of circumstances. The metabolic models can be mathematically analyzed more easily by imposing constraints on the reaction rates of the network, based on empirical data. Two types of constraint-based analysis have emerged: Biased analyses, based on maximizing or minimizing some objective of the metabolic model, for example maximizing cellular growth, and unbiased analyses, attempting to describe all possible flux combinations though a modeled metabolic network that are valid under the constraints of the model. While biased methods have evolved to analyze comprehensive genome-scale and multi-cellular models, the unbiased methods lag behind because the number of computations grow combinatorically with model size. Multiple approaches aim to improve scalability of unbiased analysis. In this thesis, I apply two unbiased methods: Enumeration of elementary conversion modes (ECMs) and enumeration of minimal pathways (MPs) on models of different sizes. I aim to compare the methods, reproduce results from earlier work, and gain insight on how to make unbiased analysis more scalable. The results highlight the importance of reproducing previous findings and the scalability of some unbiased methods. Initially the results were not successfully reproduced, but after the cause was discovered, the results were reproduced correctly. MP analysis scaled to larger models than ECMs, by allowing more constraints on the model and by implementing random sampling of MPs. However, this approach can be challenging, as the number of samples required is not known beforehand. Furthermore, the additional constraints could be a source of bias. Although challenges of unbiased analysis still persist, this research clarifies which strategies that could be implemented for improving scalability of unbiased analysis.Et metabolsk nettverk er det biokjemiske systemet som opprettholder en organisme. Genetikken til en gitt organisme er i høyest grad involvert i hvilke reaksjoner som forekommer i nettverket. I de siste årene har store mengder genetisk data blitt hentet fra et mangfoldig utvalg av organismer. Dataen kan broderes inn i matematiske modeller av metabolske nettverk, noe som tillater prediksjon og forståelse av varierte metabolske nettverk, utsatt for varierte betingelser. Disse modellene kan analyseres mye lettere ved å innføre begrensinger av hastigheten til reaksjonene i modellen, basert på empirisk data. Begrensings-basert modellering kan inndeles i to typer analyse: Partisk og upartisk analyse. Partisk analyse er basert på optimering av et egen-definert formål, for eksempel maksimering av cellevekst. Ved upartisk analyse er målet å beskrive alle mulige kombinasjoner av reaksjonshastigheter, som er mulige ved gitte begrensninger, gjennom det modellerte metabolske nettverket. Mens partiske metoder brukes på store metabolske modeller og flercellede modeller, har ikke de upartiske metodene utviklet seg til dette nivået ennå. En viktig grunn til dette er at antall kalkulasjoner som trengs for å finne alle hastighetskombinasjonene øker kombinatorisk med modellstørrelse. Flere strategier har blitt benyttet for å gjøre upartisk analyse mer skalerbart. I denne oppgaven anvender jeg to upartiske analysemetoder på metabolske modeller av ulik størrelse: Opptelling av elementary conversion modes (ECMs) og opptelling av minimal pathways (MPs). Formålet er å sammenligne metodene, reprodusere tidligere funn og forstå hvordan man kan gjøre upartisk analyse mer skalerbart. Resultatene viser viktigheten i å reprodusere tidligere funn og skalerbarheten til noen upartiske metoder. I første omgang ble ikke tidligere funn reprodusert, men etter en feil i programmet ble oppdaget og fikset ble de reprodusert. MP analyse skalerte bedre til større modeller enn ECMs ved å tillate flere begrensinger på modellen og ved å implementere tilfeldig utvalg av MPs. Likevel kan det være utfordrende å bruke denne tilnærmingen fordi man ikke kan vite utvalgsstørrelsen på forhånd. Dette arbeidet oppklarer hvilke strategier som kan implementeres for å forbedre skalerbarheten til upartiske analyser.submittedVersionM-K

Optimization Models and Algorithms for Truckload Relay Network Design

Driver turnover is a significant problem for full truckload (TL) carriers that operate using point-to-point (PtP) dispatching. The low quality of life of drivers due to the long periods of time they spend away from home is usually identified as one of the main reasons for the high turnover. In contrast, driver turnover is not as significant for less-than-truckload (LTL) carriers that use hub-and-spoke transportation networks which allow drivers to return home more frequently. Based on the differences between TL and LTL, the use of a relay network (RN) has been proposed as an alternative dispatching method for TL transportation in order to improve driver retention. In a RN, a truckload visits one or more relay points (RPs) where drivers and trailers are exchanged while the truckload continues its movement to the final destination. In this research, we propose a new composite variable model (CVM) to address the strategic TL relay network design (TLRND) problem. With this approach, we capture operational considerations implicitly within the variable definition instead of adding them as constraints in our model. Our composites represent feasible routes for the truckloads through the RN that satisfy limitations on circuity, number of RPs visited, and distances between RPs and between a RP and origin-destination nodes. Given a strict limitation on the number of RPs allowed to be visited, we developed a methodology to generate feasible routes using predefined templates. This methodology was preferred over an exact feasible path enumeration algorithm that was also developed to generate valid routes. The proposed approach was successfully used to obtain high quality solutions to largely-sized problem instances of TLRND. Furthermore extending the original CVM formulation, we incorporate mixed fleet dispatching decisions into the design of the RN. This alternative system allows routing some truckloads through the RN while the remaining truckloads are dispatched PtP. We analyze the performance of our models and the solutions obtained for TLRND problems through extensive computational testing. Finally, we conclude with a description of directions for future research

Co-evolution of strain design methods based on flux balance and elementary mode analysis

More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last ten years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications.Novo Nordisk UK Research Foundation(NORTE-07-0124-FEDER-000028