Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Characterization of the metabolic requirements in yeast meiosis

The diploid yeast Saccharomyces cerevisiae undergoes mitosis in glucose-rich medium but enters meiosis in acetate sporulation medium. The transition from mitosis to meiosis involves a remarkable adaptation of the metabolic machinery to the changing environment to meet new energy and biosynthesis requirements. Biochemical studies indicate that five metabolic pathways are active at different stages of sporulation: glutamate formation, tricarboxylic acid cycle, glyoxylate cycle, gluconeogenesis, and glycogenolysis. A dynamic synthesis of macromolecules, including nucleotides, amino acids, and lipids, is also observed. However, the metabolic requirements of sporulating cells are poorly understood. In this study, we apply flux balance analyses to uncover optimal principles driving the operation of metabolic networks over the entire period of sporulation. A meiosis-specific metabolic network is constructed, and flux distribution is simulated using ten objective functions combined with time-course expression-based reaction constraints. By systematically evaluating the correlation between computational and experimental fluxes on pathways and macromolecule syntheses, the metabolic requirements of cells are determined: sporulation requires maximization of ATP production and macromolecule syntheses in the early phase followed by maximization of carbohydrate breakdown and minimization of ATP production in the middle and late stages. Our computational models are validated by in silico deletion of enzymes known to be essential for sporulation. Finally, the models are used to predict novel metabolic genes required for sporulation. This study indicates that yeast cells have distinct metabolic requirements at different phases of meiosis, which may reflect regulation that realizes the optimal outcome of sporulation. Our meiosis-specific network models provide a framework for an in-depth understanding of the roles of enzymes and reactions, and may open new avenues for engineering metabolic pathways to improve sporulation efficiency.

Parallel exploitation of diverse host nutrients enhances Salmonella virulence

Pathogen access to host nutrients in infected tissues is fundamental for pathogen growth and virulence, disease progression, and infection control. However, our understanding of this crucial process is still rather limited because of experimental and conceptual challenges. Here, we used proteomics, microbial genetics, competitive infections, and computational approaches to obtain a comprehensive overview of Salmonella nutrition and growth in a mouse typhoid fever model. The data revealed that Salmonella accessed an unexpectedly diverse set of at least 31 different host nutrients in infected tissues but the individual nutrients were available in only scarce amounts. Salmonella adapted to this situation by expressing versatile catabolic pathways to simultaneously exploit multiple host nutrients. A genome-scale computational model of Salmonella in vivo metabolism based on these data was fully consistent with independent large-scale experimental data on Salmonella enzyme quantities, and correctly predicted 92% of 738 reported experimental mutant virulence phenotypes, suggesting that our analysis provided a comprehensive overview of host nutrient supply, Salmonella metabolism, and Salmonella growth during infection. Comparison of metabolic networks of other pathogens suggested that complex host/pathogen nutritional interfaces are a common feature underlying many infectious diseases.

Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity

Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

Multi-scale modeling for sustainable chemical production

With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.

Reconciling a Salmonella enterica metabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant

The in silico reconstruction of metabolic networks has become an effective and useful systems biology approach to predict and explain many different cellular phenotypes. When simulation outputs do not match experimental data, the source of the inconsistency can often be traced to incomplete biological information that is consequently not captured in the model. To address this problem, general approaches continue to be needed that can suggest experimentally testable hypotheses to reconcile inconsistencies between simulation and experimental data. Here, we present such an approach that focuses specifically on correcting cases in which experimental data show a particular gene to be essential but model simulations do not. We use metabolic models to predict efficient compensatory pathways, after which cloning and overexpression of these pathways are performed to investigate whether they restore growth and to help determine why these compensatory pathways are not active in mutant cells. We demonstrate this technique for a ppc knockout of Salmonella enterica serovar Typhimurium; the inability of cells to route flux through the glyoxylate shunt when ppc is removed was correctly identified by our approach as the cause of the discrepancy. These results demonstrate the feasibility of our approach to drive biological discovery while simultaneously refining metabolic network reconstructions.

Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells

Small intestinal epithelial cells (sIECs) have a significant share in whole body metabolism as they perform enzymatic digestion and absorption of nutrients. Furthermore, the diet plays a key role in a number of complex diseases including obesity and diabetes. The impact of diet and altered genetic backgrounds on human metabolism may be studied by using computational modeling. A metabolic reconstruction of human sIECs was manually assembled using the literature. The resulting sIEC model was subjected to two different diets to obtain condition-specific metabolic models. Fifty defined metabolic tasks evaluated the functionalities of these models, along with the respective secretion profiles, which distinguished between impacts of different dietary regimes. Under the average American diet, the sIEC model resulted in higher secretion flux for metabolites implicated in metabolic syndrome. In addition, enzymopathies were analyzed in the context of the sIEC metabolism. Computed results were compared with reported gastrointestinal (GI) pathologies and biochemical defects as well as with biomarker patterns used in their diagnosis. Based on our simulations, we propose that (i) sIEC metabolism is perturbed by numerous enzymopathies, which can be used to study cellular adaptive mechanisms specific for such disorders, and in the identification of novel co-morbidities, (ii) porphyrias are associated with both heme synthesis and degradation and (iii) disturbed intestinal gamma-aminobutyric acid synthesis may be linked to neurological manifestations of various enzymopathies. Taken together, the sIEC model represents a comprehensive, biochemically accurate platform for studying the function of sIEC and their role in whole body metabolism.

Biotechnological potential of respiring Zymomonas mobilis: a stoichiometric analysis of its central metabolism

The active, yet energetically inefficient electron transport chain of the ethanologenic bacterium Zymomonas mobilis could be used in metabolic engineering for redox-balancing purposes during synthesis of certain products. Although several reconstructions of Z. mobilis metabolism have been published, important aspects of redox balance and aerobic catabolism have not previously been considered. Here, annotated genome sequences and metabolic reconstructions have been combined with existing biochemical evidence to yield a medium-scale model of Z. mobilis central metabolism in the form of COBRA Toolbox model files for flux balance analysis (FBA). The stoichiometric analysis presented here suggests the feasibility of several metabolic engineering strategies for obtaining high-value products, such as glycerate, succinate, and glutamate that would use the electron transport chain to oxidize the excess NAD(P)H, generated during synthesis of these metabolites. Oxidation of the excess NAD(P)H would also be needed for synthesis of ethanol from glycerol. Maximum product yields and the byproduct spectra have been estimated for each product, with glucose, xylose, or glycerol as the carbon substrates. These novel pathways represent targets for future metabolic engineering, as they would exploit both the rapid Entner-Doudoroff glycolysis, and the energetically uncoupled electron transport of Z. mobilis.

A community-driven global reconstruction of human metabolism

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Systems biology for molecular life sciences and its impact in biomedicine

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

Reconstruction and analysis of the industrial strain Bacillus megaterium WSH002 genome-scale in silico metabolic model

A genome-scale metabolic model of Bacillus megaterium WSH002, an industrial bacterium widely used in the vitamin C industry, was reconstructed on the basis of the genome annotation and data from the literature and biochemical databases. It comprises 1112 reactions, 993 metabolites, and 1055 genes, including 43 new annotated genes. This model was able to predict qualitatively and quantitatively the growth of B. megaterium on a range of carbon and nitrogen sources, and the results agreed well with experimental data. A gene essentiality analysis predicted a core metabolic essential gene set of 57 genes on three different media. Furthermore, constraint-based analysis revealed that B. megaterium WSH002 is capable of producing and exporting several key metabolites, which could promote the growth of Ketogulonicigenium vulgare and 2-keto-l-gulonic acid (2-KLG) production. Here, the model represents a helpful tool for understanding and exploring this important industrial organism.

A systems-level approach for investigating Pseudomonas aeruginosa biofilm formation

Prevention of the initiation of biofilm formation is the most important step for combating biofilm-associated pathogens, as the ability of pathogens to resist antibiotics is enhanced 10 to 1000 times once biofilms are formed. Genes essential to bacterial growth in the planktonic state are potential targets to treat biofilm-associated pathogens. However, the biofilm formation capability of strains with mutations in these essential genes must be evaluated, since the pathogen might form a biofilm before it is eliminated. In order to address this issue, this work proposes a systems-level approach to quantifying the biofilm formation capability of mutants to determine target genes that are essential for bacterial metabolism in the planktonic state but do not induce biofilm formation in their mutants. The changes of fluxes through the reactions associated with the genes positively related to biofilm formation are used as soft sensors in the flux balance analysis to quantify the trend of biofilm formation upon the mutation of an essential gene. The essential genes whose mutants are predicted not to induce biofilm formation are regarded as gene targets. The proposed approach was applied to identify target genes to treat Pseudomonas aeruginosa infections. It is interesting to find that most essential gene mutants exhibit high potential to induce the biofilm formation while most non-essential gene mutants do not. Critically, we identified four essential genes, lysC, cysH, adk, and galU, that constitute gene targets to treat P. aeruginosa. They have been suggested by existing experimental data as potential drug targets for their crucial role in the survival or virulence of P. aeruginosa. It is also interesting to find that P. aeruginosa tends to survive the essential-gene mutation treatment by mainly enhancing fluxes through 8 metabolic reactions that regulate acetate metabolism, arginine metabolism, and glutamate metabolism.

Microbial engineering strategies to improve cell viability for biochemical production

Efficient production of biochemicals using engineered microbes as whole-cell biocatalysts requires robust cell viability. Robust viability leads to high productivity and improved bioprocesses by allowing repeated cell recycling. However, cell viability is negatively affected by a plethora of stresses, namely chemical toxicity and metabolic imbalances, primarily resulting from bio-synthesis pathways. Chemical toxicity is caused by substrates, intermediates, products, and/or by-products, and these compounds often interfere with important metabolic processes and damage cellular infrastructures such as cell membrane, leading to poor cell viability. Further, stresses on engineered cells are accentuated by metabolic imbalances, which are generated by heavy metabolic resource consumption due to enzyme overexpression, redistribution of metabolic fluxes, and impaired intracellular redox state by co-factor imbalance. To address these challenges, herein, we discuss a range of key microbial engineering strategies, substantiated by recent advances, to improve cell viability for commercially sustainable production of biochemicals from renewable resources.

Fast thermodynamically constrained flux variability analysis

MOTIVATION

Flux variability analysis (FVA) is an important tool to further analyse the results obtained by flux balance analysis (FBA) on genome-scale metabolic networks. For many constraint-based models, FVA identifies unboundedness of the optimal flux space. This reveals that optimal flux solutions with net flux through internal biochemical loops are feasible, which violates the second law of thermodynamics. Such unbounded fluxes may be eliminated by extending FVA with thermodynamic constraints.

RESULTS

We present a new algorithm for efficient flux variability (and flux balance) analysis with thermodynamic constraints, suitable for analysing genome-scale metabolic networks. We first show that FBA with thermodynamic constraints is NP-hard. Then we derive a theoretical tractability result, which can be applied to metabolic networks in practice. We use this result to develop a new constraint programming algorithm Fast-tFVA for fast FVA with thermodynamic constraints (tFVA). Computational comparisons with previous methods demonstrate the efficiency of the new method. For tFVA, a speed-up of factor 30-300 is achieved. In an analysis of genome-scale metabolic networks in the BioModels database, we found that in 485 of 716 networks, additional irreversible or fixed reactions could be detected.

AVAILABILITY AND IMPLEMENTATION

Fast-tFVA is written in C++ and published under GPL. It uses the open source software SCIP and libSBML. There also exists a Matlab interface for easy integration into Matlab. Fast-tFVA is available from page.mi.fu-berlin.de/arnem/fast-tfva.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism

Systems biology is a computational field that has been used for several years across different scientific areas of biological research to uncover the complex interactions occurring in living organisms. Applications of systems concepts at the mammalian genome level are quite challenging, and new complimentary computational/experimental techniques are being introduced. Most recent work applying modern systems biology techniques has been conducted on bacteria, yeast, mouse, and human genomes. However, these concepts and tools are equally applicable to other species including ruminants (e.g., livestock). In systems biology, both bottom-up and top-down approaches are central to assemble information from all levels of biological pathways that must coordinate physiological processes. A bottom-up approach encompasses draft reconstruction, manual curation, network reconstruction through mathematical methods, and validation of these models through literature analysis (i.e., bibliomics). Whereas top-down approach encompasses metabolic network reconstructions using ‘omics’ data (e.g., transcriptomics, proteomics) generated through DNA microarrays, RNA-Seq or other modern high-throughput genomic techniques using appropriate statistical and bioinformatics methodologies. In this review we focus on top-down approach as a means to improve our knowledge of underlying metabolic processes in ruminants in the context of nutrition. We also explore the usefulness of tissue specific reconstructions (e.g., liver and adipose tissue) in cattle as a means to enhance productive efficiency.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

A compendium of inborn errors of metabolism mapped onto the human metabolic network

Inborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.

Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.

Retrosynthetic design of heterologous pathways

Tools from metabolic engineering and synthetic biology are synergistically used in order to develop high-performance cell factories. However, the number of successful applications has been limited due to the complexity of exploring efficiently the metabolic space for the discovery of candidate heterologous pathways. To address this challenge, retrosynthetic biology provides an integrated framework to formalize and rationalize the problem of importing biosynthetic pathways into a chassis organism using methods at the interface from bottom-up and top-down strategies. Here, we describe step by step the process of implementing a retrosynthetic framework for the design of heterologous biosynthetic pathways in a chassis organism. The method consists of the following steps: choosing the chassis and the target, selection of an in silico model for the chassis, definition of the metabolic space, pathway enumeration, gene selection, estimation of yields, toxicity prediction of pathway metabolites, definition of an objective function to select the best pathway candidates, and pathway implementation and verification.

Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino acids, and vitamins. In this study, we assembled and validated a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron (iAH991), a prominent representative of the human gut microbiota, consisting of 1488 reactions, 1152 metabolites, and 991 genes. To create a comprehensive metabolic model of host-microbe interactions, we integrated iAH991 with a previously published mouse metabolic reconstruction, which was extended for intestinal transport and absorption reactions. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes. The resulting model consists of 7239 reactions, 5164 metabolites, and 2769 genes. We simultaneously modeled growth of mouse and B. thetaiotaomicron on five different diets varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa. This systems approach represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease.

Metabolic model refinement using phenotypic microarray data

Phenotypic microarray (PM) is a standardized, high-throughput technology for profiling phenotypes of microorganisms, which allows for characterization on around 2,000 different media conditions. The data generated using PM can be incorporated into genome-scale metabolic models to improve their predictive capability. In addition, a comparison of phenotypic profiles of wild-type and gene knockout mutants can give essential information about gene functions of unknown genes. In this chapter, we present a protocol to refine preconstructed metabolic models using the PM data. Both manual refinement and algorithmic approaches for integrating the PM data into metabolic models have been discussed.

Genome-scale model management and comparison

Spurred by recent innovations in genome sequencing, the reconstruction of genome-scale models has increased in recent years. Genome-scale models are now available for a wide range of organisms, and models have been successfully applied to a number of research topics including metabolic engineering, genome annotation, biofuel production, and interpretation of omics data sets. The challenge is how to manage the large amount of data in genome-scale models and perform comparative analysis to gain new biological insights. In this chapter, important standards for genome-scale modeling are outlined. Furthermore, management strategies as well as existing repository and construction tools are discussed. As the comparison of models is an important aspect during the development and analysis stages, available methods are presented and existing software solutions are reviewed.

Linking genome-scale metabolic modeling and genome annotation

Genome-scale metabolic network reconstructions, assembled from annotated genomes, serve as a platform for integrating data from heterogeneous sources and generating hypotheses for further experimental validation. Implementing constraint-based modeling techniques such as flux balance analysis (FBA) on network reconstructions allows for interrogating metabolism at a systems level, which aids in identifying and rectifying gaps in knowledge. With genome sequences for various organisms from prokaryotes to eukaryotes becoming increasingly available, a significant bottleneck lies in the structural and functional annotation of these sequences. Using topologically based and biologically inspired metabolic network refinement, we can better characterize enzymatic functions present in an organism and link annotation of these functions to candidate transcripts; both steps can be experimentally validated.

Fumaric acid production in Saccharomyces cerevisiae by in silico aided metabolic engineering

Fumaric acid (FA) is a promising biomass-derived building-block chemical. Bio-based FA production from renewable feedstock is a promising and sustainable alternative to petroleum-based chemical synthesis. Here we report on FA production by direct fermentation using metabolically engineered Saccharomyces cerevisiae with the aid of in silico analysis of a genome-scale metabolic model. First, FUM1 was selected as the target gene on the basis of extensive literature mining. Flux balance analysis (FBA) revealed that FUM1 deletion can lead to FA production and slightly lower growth of S. cerevisiae. The engineered S. cerevisiae strain obtained by deleting FUM1 can produce FA up to a concentration of 610±31 mg L^–1 without any apparent change in growth in fed-batch culture. FT-IR and ¹H and ¹³C NMR spectra confirmed that FA was synthesized by the engineered S. cerevisiae strain. FBA identified pyruvate carboxylase as one of the factors limiting higher FA production. When the RoPYC gene was introduced, S. cerevisiae produced 1134±48 mg L^–1 FA. Furthermore, the final engineered S. cerevisiae strain was able to produce 1675±52 mg L^–1 FA in batch culture when the SFC1 gene encoding a succinate–fumarate transporter was introduced. These results demonstrate that the model shows great predictive capability for metabolic engineering. Moreover, FA production in S. cerevisiae can be efficiently developed with the aid of in silico metabolic engineering.

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Emerging engineering principles for yield improvement in microbial cell design

Metabolic Engineering has undertaken a rapid transformation in the last ten years making real progress towards the production of a wide range of molecules and fine chemicals using a designed cellular host. However, the maximization of product yields through pathway optimization is a constant and central challenge of this field. Traditional methods used to improve the production of target compounds from engineered biosynthetic pathways in non-native hosts include: codon usage optimization, elimination of the accumulation of toxic intermediates or byproducts, enhanced production of rate-limiting enzymes, selection of appropriate promoter and ribosome binding sites, application of directed evolution of enzymes, and chassis re-circuit. Overall, these approaches tend to be specific for each engineering project rather than a systematic practice based on a more generalizable strategy. In this mini-review, we highlight some novel and extensive approaches and tools intended to address the improvement of a target product formation, founded in sophisticated principles such as dynamic control, pathway genes modularization, and flux modeling.

Contextualization procedure and modeling of monocyte specific TLR signaling

Innate immunity is the first line of defense against invasion of pathogens. Toll-like receptor (TLR) signaling is involved in a variety of human diseases extending far beyond immune system–related diseases, affecting a number of different tissues and cell-types. Computational models often do not account for cell-type specific differences in signaling networks. Investigation of these differences and its phenotypic implications could increase understanding of cell signaling and processes such as inflammation. The wealth of knowledge for TLR signaling has been recently summarized in a stoichiometric signaling network applicable for constraint-based modeling and analysis (COBRA). COBRA methods have been applied to investigate tissue-specific metabolism using omics data integration. Comparable approaches have not been conducted using signaling networks. In this study, we present ihsTLRv2, an updated TLR signaling network accounting for the association of 314 genes with 558 network reactions. We present a mapping procedure for transcriptomic data onto signaling networks and demonstrate the generation of a monocyte-specific TLR network. The generated monocyte network is characterized through expression of a specific set of isozymes rather than reduction of pathway contents. While further tailoring the network to a specific stimulation condition, we observed that the quantitative changes in gene expression due to LPS stimulation affected the tightly connected set of genes. Differential expression influenced about one third of the entire TLR signaling network, in particular, NF-B activation. Thus, a cell-type and condition-specific signaling network can provide functional insight into signaling cascades. Furthermore, we demonstrate the energy dependence of TLR signaling pathways in monocytes.

Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach

Background

Constraint-based computational approaches, such as flux balance analysis (FBA), have proven successful in modeling genome-level metabolic behavior for conditions where a set of simple cellular objectives can be clearly articulated. Recently, the necessity to expand the current range of constraint-based methods to incorporate high-throughput experimental data has been acknowledged by the proposal of several methods. However, these methods have rarely been used to address cellular metabolic responses to some relevant perturbations such as antimicrobial or temperature-induced stress. Here, we present a new method for combining gene-expression data with FBA (GX-FBA) that allows modeling of genome-level metabolic response to a broad range of environmental perturbations within a constraint-based framework. The method uses mRNA expression data to guide hierarchical regulation of cellular metabolism subject to the interconnectivity of the metabolic network.

Results

We applied GX-FBA to a genome-scale model of metabolism in the gram negative bacterium Yersinia pestis and analyzed its metabolic response to (i) variations in temperature known to induce virulence, and (ii) antibiotic stress. Without imposition of any a priori behavioral constraints, our results show strong agreement with reported phenotypes. Our analyses also lead to novel insights into how Y. pestis uses metabolic adjustments to counter different forms of stress.

Conclusions

Comparisons of GX-FBA predicted metabolic states with fluxomic measurements and different reported post-stress phenotypes suggest that mass conservation constraints and network connectivity can be an effective representative of metabolic flux regulation in constraint-based models. We believe that our approach will be of aid in the in silico evaluation of cellular goals under different conditions and can be used for a variety of analyses such as identification of potential drug targets and their action.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Metabolic modelling in the development of cell factories by synthetic biology

Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

Software applications toward quantitative metabolic flux analysis and modeling

Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches.

Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress

The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E. coli to paraquat-induced oxidative stress is globally conserved and coordinated.

A systems biology approach to studying the role of microbes in human health

Host-microbe interactions play a crucial role in human health and disease. Of the various systems biology approaches, reconstruction of genome-scale metabolic networks combined with constraint-based modeling has been particularly successful at in silico predicting the phenotypic characteristics of single organisms. Here, we summarize recent studies, which have applied this approach to investigate microbe-microbe and host-microbe metabolic interactions. This approach can be also expanded to investigate the properties of an entire microbial community, as well as single organisms within the community. We illustrate that the constraint-based modeling approach is suitable to model host-microbe interactions at molecular resolution and will enable systematic investigation of metabolic links between the human host and its microbes. Such host-microbe models, combined with experimental data, will ultimately further our understanding of how microbes influence human health.

Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction

Reaction directionality is a key constraint in the modeling of genome-scale metabolic networks. We thermodynamically constrained reaction directionality in a multicompartmental genome-scale model of human metabolism, Recon 1, by calculating, in vivo, standard transformed reaction Gibbs energy as a function of compartment-specific pH, electrical potential, and ionic strength. We show that compartmental pH is an important determinant of thermodynamically determined reaction directionality. The effects of pH on transport reaction thermodynamics are only seen to their full extent when metabolites are represented as pseudoisomer groups of multiple protonated species. We accurately predict the irreversibility of 387 reactions, with detailed propagation of uncertainty in input data, and manually curate the literature to resolve conflicting directionality assignments. In at least half of all cases, a prediction of a reversible reaction directionality is due to the paucity of compartment-specific quantitative metabolomic data, with remaining cases due to uncertainty in estimation of standard reaction Gibbs energy. This study points to the pressing need for 1), quantitative metabolomic data, and 2), experimental measurement of thermochemical properties for human metabolites.

Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli's metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors.

Mathematical optimization applications in metabolic networks

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

A constraint-based model of Scheffersomyces stipitis for improved ethanol production

BACKGROUND

As one of the best xylose utilization microorganisms, Scheffersomyces stipitis exhibits great potential for the efficient lignocellulosic biomass fermentation. Therefore, a comprehensive understanding of its unique physiological and metabolic characteristics is required to further improve its performance on cellulosic ethanol production.

RESULTS

A constraint-based genome-scale metabolic model for S. stipitis CBS 6054 was developed on the basis of its genomic, transcriptomic and literature information. The model iTL885 consists of 885 genes, 870 metabolites, and 1240 reactions. During the reconstruction process, 36 putative sugar transporters were reannotated and the metabolisms of 7 sugars were illuminated. Essentiality study was conducted to predict essential genes on different growth media. Key factors affecting cell growth and ethanol formation were investigated by the use of constraint-based analysis. Furthermore, the uptake systems and metabolic routes of xylose were elucidated, and the optimization strategies for the overproduction of ethanol were proposed from both genetic and environmental perspectives.

CONCLUSIONS

Systems biology modelling has proven to be a powerful tool for targeting metabolic changes. Thus, this systematic investigation of the metabolism of S. stipitis could be used as a starting point for future experiment designs aimed at identifying the metabolic bottlenecks of this important yeast.

Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

Network context and selection in the evolution to enzyme specificity

Enzymes are thought to have evolved highly specific catalytic activities from promiscuous ancestral proteins. By analyzing a genome-scale model of Escherichia coli metabolism, we found that 37% of its enzymes act on a variety of substrates and catalyze 65% of the known metabolic reactions. However, it is not apparent why these generalist enzymes remain. Here, we show that there are marked differences between generalist enzymes and specialist enzymes, known to catalyze a single chemical reaction on one particular substrate in vivo. Specialist enzymes (i) are frequently essential, (ii) maintain higher metabolic flux, and (iii) require more regulation of enzyme activity to control metabolic flux in dynamic environments than do generalist enzymes. Furthermore, these properties are conserved in Archaea and Eukarya. Thus, the metabolic network context and environmental conditions influence enzyme evolution toward high specificity.