PSAMM: A Portable System for the Analysis of Metabolic Models

Metabolic engineering of Caldicellulosiruptor bescii for 2,3-butanediol production from unpretreated lignocellulosic biomass and metabolic strategies for improving yields and titers

The platform chemical 2,3-butanediol (2,3-BDO) is used to derive products, such as 1,3-butadiene and methyl ethyl ketone, for the chemical and fuel production industries. Efficient microbial 2,3-BDO production at industrial scales has not been achieved yet for various reasons, including product inhibition to host organisms, mixed stereospecificity in product formation, and dependence on expensive substrates (i.e., glucose). In this study, we explore engineering of a 2,3-BDO pathway in Caldicellulosiruptor bescii, an extremely thermophilic (optimal growth temperature = 78°C) and anaerobic bacterium that can break down crystalline cellulose and hemicellulose into fermentable C5 and C6 sugars. In addition, C. bescii grows on unpretreated plant biomass, such as switchgrass. Biosynthesis of 2,3-BDO involves three steps: two molecules of pyruvate are condensed into acetolactate; acetolactate is decarboxylated to acetoin, and finally, acetoin is reduced to 2,3-BDO. C. bescii natively produces acetoin; therefore, in order to complete the 2,3-BDO biosynthetic pathway, C. bescii was engineered to produce a secondary alcohol dehydrogenase (sADH) to catalyze the final step. Two previously characterized, thermostable sADH enzymes with high affinity for acetoin, one from a bacterium and one from an archaeon, were tested independently. When either sADH was present in C. bescii, the recombinant strains were able to produce up to 2.5-mM 2,3-BDO from crystalline cellulose and xylan and 0.2-mM 2,3-BDO directly from unpretreated switchgrass. This serves as the basis for higher yields and productivities, and to this end, limiting factors and potential genetic targets for further optimization were assessed using the genome-scale metabolic model of C. bescii.IMPORTANCELignocellulosic plant biomass as the substrate for microbial synthesis of 2,3-butanediol is one of the major keys toward cost-effective bio-based production of this chemical at an industrial scale. However, deconstruction of biomass to release the sugars for microbial growth currently requires expensive thermochemical and enzymatic pretreatments. In this study, the thermo-cellulolytic bacterium Caldicellulosiruptor bescii was successfully engineered to produce 2,3-butanediol from cellulose, xylan, and directly from unpretreated switchgrass. Genome-scale metabolic modeling of C. bescii was applied to adjust carbon and redox fluxes to maximize productivity of 2,3-butanediol, thereby revealing bottlenecks that require genetic modifications.

A predicted CRISPR-mediated symbiosis between uncultivated archaea

CRISPR-Cas systems defend prokaryotic cells from invasive DNA of viruses, plasmids and other mobile genetic elements. Here, we show using metagenomics, metatranscriptomics and single-cell genomics that CRISPR systems of widespread, uncultivated archaea can also target chromosomal DNA of archaeal episymbionts of the DPANN superphylum. Using meta-omics datasets from Crystal Geyser and Horonobe Underground Research Laboratory, we find that CRISPR spacers of the hosts Candidatus Altiarchaeum crystalense and Ca. A. horonobense, respectively, match putative essential genes in their episymbionts' genomes of the genus Ca. Huberiarchaeum and that some of these spacers are expressed in situ. Metabolic interaction modelling also reveals complementation between host-episymbiont systems, on the basis of which we propose that episymbionts are either parasitic or mutualistic depending on the genotype of the host. By expanding our analysis to 7,012 archaeal genomes, we suggest that CRISPR-Cas targeting of genomes associated with symbiotic archaea evolved independently in various archaeal lineages.

Identification of a deep-branching thermophilic clade sheds light on early bacterial evolution

It has been proposed that early bacteria, or even the last universal common ancestor of all cells, were thermophilic. However, research on the origin and evolution of thermophily is hampered by the difficulties associated with the isolation of deep-branching thermophilic microorganisms in pure culture. Here, we isolate a deep-branching thermophilic bacterium from a deep-sea hydrothermal vent, using a two-step cultivation strategy ("Subtraction-Suboptimal", StS) designed to isolate rare organisms. The bacterium, which we name Zhurongbacter thermophilus 3DAC, is a sulfur-reducing heterotroph that is phylogenetically related to Coprothermobacterota and other thermophilic bacterial groups, forming a clade that seems to represent a major, early-diverging bacterial lineage. The ancestor of this clade might be a thermophilic, strictly anaerobic, motile, hydrogen-dependent, and mixotrophic bacterium. Thus, our study provides insights into the early evolution of thermophilic bacteria.

Optimizing Strategies for Bio-Based Ethanol Production Using Genome-Scale Metabolic Modeling of the Hyperthermophilic Archaeon, Pyrococcus furiosus

A genome-scale metabolic model, encompassing a total of 623 genes, 727 reactions, and 865 metabolites, was developed for Pyrococcus furiosus, an archaeon that grows optimally at 100°C by carbohydrate and peptide fermentation. The model uses subsystem-based genome annotation, along with extensive manual curation of 237 gene-reaction associations including those involved in central carbon metabolism, amino acid metabolism, and energy metabolism. The redox and energy balance of P. furiosus was investigated through random sampling of flux distributions in the model during growth on disaccharides. The core energy balance of the model was shown to depend on high acetate production and the coupling of a sodium-dependent ATP synthase and membrane-bound hydrogenase, which generates a sodium gradient in a ferredoxin-dependent manner, aligning with existing understanding of P. furiosus metabolism. The model was utilized to inform genetic engineering designs that favor the production of ethanol over acetate by implementing an NADPH and CO-dependent energy economy. The P. furiosus model is a powerful tool for understanding the relationship between generation of end products and redox/energy balance at a systems-level that will aid in the design of optimal engineering strategies for production of bio-based chemicals and fuels. IMPORTANCE The bio-based production of organic chemicals provides a sustainable alternative to fossil-based production in the face of today's climate challenges. In this work, we present a genome-scale metabolic reconstruction of Pyrococcus furiosus, a well-established platform organism that has been engineered to produce a variety of chemicals and fuels. The metabolic model was used to design optimal engineering strategies to produce ethanol. The redox and energy balance of P. furiosus was examined in detail, which provided useful insights that will guide future engineering designs.

Manipulating Fermentation Pathways in the Hyperthermophilic Archaeon Pyrococcus furiosus for Ethanol Production up to 95°C Driven by Carbon Monoxide Oxidation

Genetic engineering of hyperthermophilic organisms for the production of fuels and other useful chemicals is an emerging biotechnological opportunity. In particular, for volatile organic compounds such as ethanol, fermentation at high temperatures could allow for straightforward separation by direct distillation. Currently, the upper growth temperature limit for native ethanol producers is 72°C in the bacterium Thermoanaerobacter ethanolicus JW200, and the highest temperature for heterologously-engineered bioethanol production was recently demonstrated at 85°C in the archaeon Pyrococcus furiosus. Here, we describe an engineered strain of P. furiosus that synthesizes ethanol at 95°C, utilizing a homologously-expressed native alcohol dehydrogenase, termed AdhF. Ethanol biosynthesis was compared at 75°C and 95°C with various engineered strains. At lower temperatures, the acetaldehyde substrate for AdhF is most likely produced from acetate by aldehyde ferredoxin oxidoreductase (AOR). At higher temperatures, the effect of AOR on ethanol production is negligible, suggesting that acetaldehyde is produced by pyruvate ferredoxin oxidoreductase (POR) via oxidative decarboxylation of pyruvate, a reaction known to occur only at higher temperatures. Heterologous expression of a carbon monoxide dehydrogenase complex in the AdhF overexpression strain enabled it to use CO as a source of energy, leading to increased ethanol production. A genome reconstruction model for P. furiosus was developed to guide metabolic engineering strategies and understand outcomes. This work opens the door to the potential for 'bioreactive distillation' since fermentation can be performed well above the normal boiling point of ethanol. IMPORTANCE Previously, the highest temperature for biological ethanol production was 85°C. Here, we have engineered ethanol production at 95°C by the hyperthermophilic archaeon Pyrococcus furiosus. Using mutant strains, we showed that ethanol production occurs by different pathways at 75°C and 95°C. In addition, by heterologous expression of a carbon monoxide dehydrogenase complex, ethanol production by this organism was driven by the oxidation of carbon monoxide. A genome reconstruction model for P. furiosus was developed to guide metabolic engineering strategies and understand outcomes.

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

BACKGROUND

Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools.

RESULTS

Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.

CONCLUSIONS

Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

Reconstruction and Analysis of Thermodynamically Constrained Models Reveal Metabolic Responses of a Deep-Sea Bacterium to Temperature Perturbations

Microbial acclimation to different temperature conditions can involve broad changes in cell composition and metabolic efficiency. A systems-level view of these metabolic responses in nonmesophilic organisms, however, is currently missing. In this study, thermodynamically constrained genome-scale models were applied to simulate the metabolic responses of a deep-sea psychrophilic bacterium, Shewanella psychrophila WP2, under suboptimal (4°C), optimal (15°C), and supraoptimal (20°C) growth temperatures. The models were calibrated with experimentally determined growth rates of WP2. Gibbs free energy change of reactions (Δ_rG'), metabolic fluxes, and metabolite concentrations were predicted using random simulations to characterize temperature-dependent changes in the metabolism. The modeling revealed the highest metabolic efficiency at the optimal temperature, and it suggested distinct patterns of ATP production and consumption that could lead to lower metabolic efficiency under suboptimal or supraoptimal temperatures. The modeling also predicted rearrangement of fluxes through multiple metabolic pathways, including the glycolysis pathway, Entner-Doudoroff pathway, tricarboxylic acid (TCA) cycle, and electron transport system, and these predictions were corroborated through comparisons to WP2 transcriptomes. Furthermore, predictions of metabolite concentrations revealed the potential conservation of reducing equivalents and ATP in the suboptimal temperature, consistent with experimental observations from other psychrophiles. Taken together, the WP2 models provided mechanistic insights into the metabolism of a psychrophile in response to different temperatures. IMPORTANCE Metabolic flexibility is a central component of any organism's ability to survive and adapt to changes in environmental conditions. This study represents the first application of thermodynamically constrained genome-scale models in simulating the metabolic responses of a deep-sea psychrophilic bacterium to various temperatures. The models predicted differences in metabolic efficiency that were attributed to changes in metabolic pathway utilization and metabolite concentration during growth under optimal and nonoptimal temperatures. Experimental growth measurements were used for model calibration, and temperature-dependent transcriptomic changes corroborated the model-predicted rearrangement of metabolic fluxes. Overall, this study highlights the utility of modeling approaches in studying the temperature-driven metabolic responses of an extremophilic organism.

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

Genome-Scale Metabolic Model of Caldicellulosiruptor bescii Reveals Optimal Metabolic Engineering Strategies for Bio-based Chemical Production

Metabolic modeling was used to examine potential bottlenecks that could be encountered for metabolic engineering of the cellulolytic extreme thermophile Caldicellulosiruptor bescii to produce bio-based chemicals from plant biomass. The model utilizes subsystems-based genome annotation, targeted reconstruction of carbohydrate utilization pathways, and biochemical and physiological experimental validations. Specifically, carbohydrate transport and utilization pathways involving 160 genes and their corresponding functions were incorporated, representing the utilization of C5/C6 monosaccharides, disaccharides, and polysaccharides such as cellulose and xylan. To illustrate its utility, the model predicted that optimal production from biomass-based sugars of the model product, ethanol, was driven by ATP production, redox balancing, and proton translocation, mediated through the interplay of an ATP synthase, a membrane-bound hydrogenase, a bifurcating hydrogenase, and a bifurcating NAD- and NADP-dependent oxidoreductase. These mechanistic insights guided the design and optimization of new engineering strategies for product optimization, which were subsequently tested in the C. bescii model, showing a nearly 2-fold increase in ethanol yields. The C. bescii model provides a useful platform for investigating the potential redox controls that mediate the carbon and energy flows in metabolism and sets the stage for future design of engineering strategies aiming at optimizing the production of ethanol and other bio-based chemicals. IMPORTANCE The extremely thermophilic cellulolytic bacterium, Caldicellulosiruptor bescii, degrades plant biomass at high temperatures without any pretreatments and can serve as a strategic platform for industrial applications. The metabolic engineering of C. bescii, however, faces potential bottlenecks in bio-based chemical productions. By simulating the optimal ethanol production, a complex interplay between redox balancing and the carbon and energy flow was revealed using a C. bescii genome-scale metabolic model. New engineering strategies were designed based on an improved mechanistic understanding of the C. bescii metabolism, and the new designs were modeled under different genetic backgrounds to identify optimal strategies. The C. bescii model provided useful insights into the metabolic controls of this organism thereby opening up prospects for optimizing production of a wide range of bio-based chemicals.

Microbiome Analysis Reveals Diversity and Function of Mollicutes Associated with the Eastern Oyster, Crassostrea virginica

Despite their biological and ecological significance, a mechanistic characterization of microbiome function is frequently missing from many nonmodel marine invertebrates. As an initial step toward filling this gap for the eastern oyster, Crassostrea virginica, this study provides an integrated taxonomic and functional analysis of the oyster microbiome using samples from a coastal salt pond in August 2017.

Marine invertebrate microbiomes play important roles in diverse host and ecological processes. However, a mechanistic understanding of host-microbe interactions is currently available for a small number of model organisms. Here, an integrated taxonomic and functional analysis of the microbiome of the eastern oyster, Crassostrea virginica, was performed using 16S rRNA gene-based amplicon profiling, shotgun metagenomics, and genome-scale metabolic reconstruction. Relatively high variability of the microbiome was observed across individual oysters and among different tissue types. Specifically, a significantly higher alpha diversity was observed in the inner shell than in the gut, gill, mantle, and pallial fluid samples, and a distinct microbiome composition was revealed in the gut compared to other tissues examined in this study. Targeted metagenomic sequencing of the gut microbiota led to further characterization of a dominant bacterial taxon, the class Mollicutes, which was captured by the reconstruction of a metagenome-assembled genome (MAG). Genome-scale metabolic reconstruction of the oyster Mollicutes MAG revealed a reduced set of metabolic functions and a high reliance on the uptake of host-derived nutrients. A chitin degradation and an arginine deiminase pathway were unique to the MAG compared to closely related genomes of Mollicutes isolates, indicating distinct mechanisms of carbon and energy acquisition by the oyster-associated Mollicutes. A systematic reanalysis of public eastern oyster-derived microbiome data revealed a high prevalence of the Mollicutes among adult oyster guts and a significantly lower relative abundance of the Mollicutes in oyster larvae and adult oyster biodeposits.

IMPORTANCE Despite their biological and ecological significance, a mechanistic characterization of microbiome function is frequently missing from many nonmodel marine invertebrates. As an initial step toward filling this gap for the eastern oyster, Crassostrea virginica, this study provides an integrated taxonomic and functional analysis of the oyster microbiome using samples from a coastal salt pond in August 2017. The study identified high variability of the microbiome across tissue types and among individual oysters, with some dominant taxa showing higher relative abundance in specific tissues. A high prevalence of Mollicutes in the adult oyster gut was revealed by comparative analysis of the gut, biodeposit, and larva microbiomes. Phylogenomic analysis and metabolic reconstruction suggested the oyster-associated Mollicutes is closely related but functionally distinct from Mollicutes isolated from other marine invertebrates. To the best of our knowledge, this study represents the first metagenomics-derived functional inference of Mollicutes in the eastern oyster microbiome.

Isolating structural errors in reaction networks in systems biology

MOTIVATION

The growing complexity of reaction-based models necessitates early detection and resolution of model errors. Considerable work has been done on the detection of mass balance errors, especially atomic mass analysis (AMA) (which compares the counts of atoms in the reactants and products) and Linear Programming analysis (which detects stoichiometric inconsistencies). This article extends model error checking to include: (i) certain structural errors in reaction networks and (ii) error isolation. First, we consider the balance of chemical structures (moieties) between reactants and products. This balance is expected in many biochemical reactions, but the imbalance of chemical structures cannot be detected if the analysis is done in units of atomic masses. Second, we improve on error isolation for stoichiometric inconsistencies by identifying a small number of reactions and/or species that cause the error. Doing so simplifies error remediation.

RESULTS

We propose two algorithms that address isolating structural errors in reaction networks. Moiety analysis finds imbalances of moieties using the same algorithm as AMA, but moiety analysis works in units of moieties instead of atomic masses. We argue for the value of checking moiety balance, and discuss two approaches to decomposing chemical species into moieties. Graphical Analysis of Mass Equivalence Sets (GAMES) provides isolation for stoichiometric inconsistencies by constructing explanations that relate errors in the structure of the reaction network to elements of the reaction network. We study the effectiveness of moiety analysis and GAMES on curated models in the BioModels repository. We have created open source codes for moiety analysis and GAMES.

AVAILABILITY AND IMPLEMENTATION

Our project is hosted at https://github.com/ModelEngineering/SBMLLint, which contains examples, documentation, source code files and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

BACKGROUND

Although native to North America, the invasion of the aphid-like grape phylloxera Daktulosphaira vitifoliae across the globe altered the course of grape cultivation. For the past 150 years, viticulture relied on grafting-resistant North American Vitis species as rootstocks, thereby limiting genetic stocks tolerant to other stressors such as pathogens and climate change. Limited understanding of the insect genetics resulted in successive outbreaks across the globe when rootstocks failed. Here we report the 294-Mb genome of D. vitifoliae as a basic tool to understand host plant manipulation, nutritional endosymbiosis, and enhance global viticulture.

RESULTS

Using a combination of genome, RNA, and population resequencing, we found grape phylloxera showed high duplication rates since its common ancestor with aphids, but similarity in most metabolic genes, despite lacking obligate nutritional symbioses and feeding from parenchyma. Similarly, no enrichment occurred in development genes in relation to viviparity. However, phylloxera evolved > 2700 unique genes that resemble putative effectors and are active during feeding. Population sequencing revealed the global invasion began from the upper Mississippi River in North America, spread to Europe and from there to the rest of the world.

CONCLUSIONS

The grape phylloxera genome reveals genetic architecture relative to the evolution of nutritional endosymbiosis, viviparity, and herbivory. The extraordinary expansion in effector genes also suggests novel adaptations to plant feeding and how insects induce complex plant phenotypes, for instance galls. Finally, our understanding of the origin of this invasive species and its genome provide genetics resources to alleviate rootstock bottlenecks restricting the advancement of viticulture.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

MOTIVATION

Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.

RESULTS

As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.

AVAILABILITY AND IMPLEMENTATION

The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From the raw bar to the bench: Bivalves as models for human health

Bivalves, from raw oysters to steamed clams, are popular choices among seafood lovers and once limited to the coastal areas. The rapid growth of the aquaculture industry and improvement in the preservation and transport of seafood have enabled them to be readily available anywhere in the world. Over the years, oysters, mussels, scallops, and clams have been the focus of research for improving the production, managing resources, and investigating basic biological and ecological questions. During this decade, an impressive amount of information using high-throughput genomic, transcriptomic and proteomic technologies has been produced in various classes of the Mollusca group, and it is anticipated that basic and applied research will significantly benefit from this resource. One aspect that is also taking momentum is the use of bivalves as a model system for human health. In this review, we highlight some of the aspects of the biology of bivalves that have direct implications in human health including the shell formation, stem cells and cell differentiation, the ability to fight opportunistic and specific pathogens in the absence of adaptive immunity, as source of alternative drugs, mucosal immunity and, microbiome turnover, toxicology, and cancer research. There is still a long way to go; however, the next time you order a dozen oysters at your favorite raw bar, think about a tasty model organism that will not only please your palate but also help unlock multiple aspects of molluscan biology and improve human health.

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models

The FASTCORE family is a family of algorithms that are mainly used to build context-specific models but can also be applied to other tasks such as gapfilling and consistency testing. The FASTCORE family has very low computational demands with running times that are several orders of magnitude lower than its main competitors. Furthermore, the models built by the FASTCORE family have a better resolution power (defined as the ability to capture metabolic variations between different tissues, cell types, or contexts) than models from other algorithms.

Using PSAMM for the Curation and Analysis of Genome-Scale Metabolic Models

PSAMM is an open source software package that supports the iterative curation and analysis of genome-scale models (GEMs). It aims to integrate the annotation and consistency checking of metabolic models with the simulation of metabolic fluxes. The model representation in PSAMM is compatible with version tracking systems like Git, which allows for full documentation of model file changes and enables collaborative curations of large, complex models. This chapter provides a protocol for using PSAMM functions and a detailed description of the various aspects in setting up and using PSAMM for the simulation and analysis of metabolic models. The overall PSAMM workflow outlined in this chapter includes the import and export of model files, the documentation of model modifications using the Git version control system, the application of consistency checking functions for model curations, and the numerical simulation of metabolic models.

The MONGOOSE Rational Arithmetic Toolbox

The modeling of metabolic networks has seen a rapid expansion following the complete sequencing of thousands of genomes. The constraint-based modeling framework has emerged as one of the most popular approaches to reconstructing and analyzing genome-scale metabolic models. Its main assumption is that of a quasi-steady-state, requiring that the production of each internal metabolite be balanced by its consumption. However, due to the multiscale nature of the models, the large number of reactions and metabolites, and the use of floating-point arithmetic for the stoichiometric coefficients, ensuring that this assumption holds can be challenging.The MONGOOSE toolbox addresses this problem by using rational arithmetic, thus ensuring that models are analyzed in a reproducible manner and consistently with modeling assumptions. In this chapter we present a protocol for the complete analysis of a metabolic network model using the MONGOOSE toolbox, via its newly developed GUI, and describe how it can be used as a model-checking platform both during and after the model construction process.

A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea.

IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Harnessing Big Data for Systems Pharmacology

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.