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Systems Metabolic Engineering of Escherichia coli Improves Coconversion of Lignocellulose-Derived Sugars

BIOTECHNOLOGY JOURNAL 2019年第9期14卷 e1800441页

作者： Kim, Joonhoon Tremaine, Mary Grass, Jeffrey A. Purdy, Hugh M. Landick, Robert Kiley, Patricia J. Reed, Jennifer L. Univ Wisconsin US Dept Energy Energy Great Lakes Bioenergy Res Ctr Madison WI 53711 USA Univ Wisconsin Dept Chem & Biol Engn 1415 Engn Dr Madison WI 53711 USA Univ Wisconsin Dept Biochem Madison WI 53711 USA Univ Wisconsin Dept Bacteriol Madison WI 53711 USA Univ Wisconsin Dept Biomol Chem Madison WI 53711 USA Pacific Northwest Natl Lab Richland WA USA

Currently, microbial conversion of lignocellulose-derived glucose and xylose to biofuels is hindered by the fact that most microbes (including Escherichia coli [E. coli], Saccharomyces cerevisiae, and Zymomonas mobilis) preferentially consume glucose first and consume xylose slowly after glucose is depleted in lignocellulosic hydrolysates. In this study, E. coli strains are developed that simultaneously utilize glucose and xylose in lignocellulosic biomass hydrolysate using genome-scale models and adaptive laboratory evolution. E. coli strains are designed and constructed that coutilize glucose and xylose and adaptively evolve them to improve glucose and xylose utilization. Whole-genome resequencing of the evolved strains find relevant mutations in metabolic and regulatory genes and the mutations' involvement in sugar coutilization is investigated. The developed strains show significantly improved coconversion of sugars in lignocellulosic biomass hydrolysates and provide a promising platform for producing next-generation biofuels.

关键词： adaptive laboratory evolution constraint-based modeling metabolic engineering sugar coutilization

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Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

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MICROBIOME 2019年第1期7卷 75-75页

作者： Heinken, Almut Raycheev, Dmitry A. Baldini, Federico Heirendt, Laurent Fleming, Ronan M. T. Thiele, Ines Natl Univ Ireland Sch Med Univ Rd Galway Ireland Univ Luxembourg Luxembourg Ctr Syst Biomed Belvaux Luxembourg Leiden Univ Fac Sci Leiden Acad Ctr Drug Res Div Analyt Biosci Leiden Netherlands Natl Univ Ireland Sch Nat Sci Discipline Microbiol Univ Rd Galway Ireland

BackgroundThe human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial ***, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://***). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each st

关键词： Gut microbiome Bile acids Host-microbe interactions Metabolism Genome-scale reconstruction constraint-based modeling Personalized modeling Systems biology

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Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches

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BMC BIOLOGY 2019年第1期17卷 51-51页

作者： Lagziel, Shoval Lee, Won Dong Shlomi, Tomer Technion Fac Comp Sci Haifa Israel Technion Fac Biol Haifa Israel Technion Lokey Ctr Life Sci & Engn Haifa Israel

The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and constraint-based Reconstruction and Analysis (COBRA). We describe the applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying novel enzymatic targets for anti-cancer drugs. We further highlight the advantages and limitations of COBRA and 13C-MFA and the main challenges ahead.

关键词： Cancer metabolism Metabolic network modeling Metabolic flux analysis constraint-based modeling COBRA Isotope tracing 13C-MFA

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DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression

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BMC SYSTEMS BIOLOGY 2019年第1期13卷 2-2页

作者： Yang, Laurence Ebrahim, Ali Lloyd, Colton J. Saunders, Michael A. Palsson, Bernhard O. Univ Calif San Diego Dept Bioengn 9500 Gilman Dr La Jolla CA 92093 USA Stanford Univ Dept Management Sci & Engn 475 Via Ortega Stanford CA 94305 USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark

BackgroundGenome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic *** develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (inertia) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative ***, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

关键词： constraint-based modeling Metabolism Proteome Dynamic simulation Batch culture

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Information Elasticity in Ultra-Wideband Target Detection Amongst Distributed Clutter 22

Information Elasticity in Ultra-Wideband Target Detection Am...

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Conference on Radar Sensor Technology XXII

作者： Singerman, Paul G. Narayanan, Ram M. Rangaswamy, Muralidhar Penn State Univ University Pk PA 16802 USA Air Force Res Lab AFRL RYAP Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510617780

Information elasticity is a new concept proposed by us to assess the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. One such application is the role of bandwidth when applied to target detection in ground clutter. Continuous, uniformly distributed, land clutter, such as snow, sand, or dirt, has a normalized radar cross section (NRCS) that increases with frequency in the microwave region of the electromagnetic (EM) spectrum. If a target's radar cross section (RCS) increases slower than the clutter that surrounds it as the signal bandwidth increases, this can limit the amount of useful fractional bandwidth that can be used for achieving high range resolution. Using equations for the RCS of a typical sphere target and the overall NRCS of land clutter, it is possible to determine the signal-to-clutter ratio (SCR) at differing fractional bandwidth percentages. If the size of the frequency bins measured is kept constant, a higher fractional bandwidth will have more frequency bins causing an increase in processing time and therefore a decrease in the elasticity. Several different target shapes and types of distributed clutter were simulated, and it is shown that there can be a decrease in elasticity when the fractional bandwidth exceeds a certain value. Furthermore, factoring in the sensor processing time shows that there exists a tipping point beyond which information overload occurs resulting in negative elasticity.

关键词： information overload information elasticity signal-to-clutter ratio radar cross section constraint-based modeling

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Information elasticity in pseudorandom code pulse compression 22

Information elasticity in pseudorandom code pulse compressio...

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Conference on Radar Sensor Technology XXII

作者： Liu, Andrew Z. Narayanan, Ram M. Rangaswamy, Muralidhar Penn State Univ University Pk PA 16802 USA AFRL RYAP Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781510617780

Information elasticity is a new concept which characterizes the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. Increasing the quantity of information used in radar processing has the ability to decrease the performance of the radar in certain contexts, depending on what constraints and objectives exist. Because of this phenomenon (known as information overload), it is advantageous to find the optimal amount of information tailored to the specific context the radar is used in. This paper analyzes the process of finding the point of information overload using the information elasticity model. This model is used in observation of different contexts in pseudorandom code pulse compression. In this model, the length of the pseudorandom code represents the amount of information. Increasing this quantity affects both the quality of pulse compression and constraints of the system. We observe this relationship between the constraints and information quantity by developing constraint functions. In this paper, two decision metrics are created for pseudorandom pulse compression, the first based on the peak to side-lobe ratio and the second based on the detection region of the radar.

关键词： Information elasticity information overload pseudorandom-binary sequences phase code modulation constraint-based modeling

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Dynamic Network modeling of Stem Cell Metabolism

Dynamic Network Modeling of Stem Cell Metabolism

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作者： Fangzhou Shen Camden Cheek Sriram Chandrasekaran

Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states. less

关键词： Metabolomics Transcriptomics Proteomics MATLAB Python Homo sapiens Mus musculus Metabolism Stem cells constraint-based modeling Flux balance analysis (FBA) Dynamic network modeling Genome-scale metabolic models (GEMs)

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Probing the Genome-Scale Metabolic Landscape of Bordetella pertussis, the Causative Agent of Whooping Cough

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APPLIED AND ENVIRONMENTAL MICROBIOLOGY 2017年第21期83卷 0152817-0152817页

作者： dos Santos, Filipe Branco Olivier, Brett G. Boele, Joost Smessaert, Vincent De Rop, Philippe Krumpochova, Petra Klau, Gunnar W. Giera, Martin Dehottay, Philippe Teusink, Bas Goffin, Philippe Vrije Univ Amsterdam Syst Bioinformat AIMMS Amsterdam Netherlands Univ Amsterdam Swammerdam Inst Life Sci Mol Microbial Physiol Amsterdam Netherlands CWI Amsterdam Netherlands GSK Vaccines Rixensart Belgium Heinrich Heine Univ Algorithm Bioinformat Dusseldorf Germany Leiden Univ Ctr Prote & Metabol Med Ctr Leiden Netherlands ULB Fac Sci IBMM Lab Genet & Physiol Bacterienne Gosselies Belgium

Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis. Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with > 2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content. IMPORTANCE The metabolic capabilities of Bordetella pertussis, the causative agent of whooping cough, were investigated from a systems-level perspective. We constructed a comprehensive genome-scale metabolic mo

关键词： Bordetella pertussis whooping cough vaccine production genome-scale metabolic model rational medium design constraint-based modeling

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Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

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METABOLIC ENGINEERING 2017年 39卷 220-227页

作者： King, Zachary A. O'Brien, Edward J. Feist, Adam M. Palsson, Bernhard O. Univ Calif San Diego Dept Bioengn 9500 Gilman Dr 0412 La Jolla CA 92093 USA Univ Calif San Diego Dept Pediat 9500 Gilman Dr 0412 La Jolla CA 92093 USA Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil Bldg 220 DIC-2800 Lyngby Denmark

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell;they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

关键词： constraint-based modeling Genome-scale model ME-model Literature mining Escherichia coli

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A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks

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BMC BIOINFORMATICS 2017年第1期18卷 1-10页

作者： Roehl, Annika Bockmayr, Alexander Free Univ Berlin Dept Math & Comp Sci Arnimallee 6 Berlin Germany

Background: constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Results: Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Conclusions: Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

关键词： constraint-based modeling Model reduction Stoichiometric models Mixed-integer linear programming Metabolic networks

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