Genome-scale Stoichiometric Models Research Articles

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective.

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PPi) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PPi sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PPi synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PPi-generating cycles. In all cases, the metabolic cost of PPi synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PPi could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PPi sources likely account for most PPi production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PPi levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

NetFlow: A tool for isolating carbon flows in genome-scale metabolic networks

1. Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.

NetRed, an algorithm to reduce genome-scale metabolic networks and facilitate the analysis of flux predictions

Flux balance analysis (FBA) of large, genome-scale stoichiometric models (GSMs) is a powerful and popular method to predict cell-wide metabolic activity. FBA typically generates a flux vector containing O(1,000) fluxes. The interpretation of such a flux vector is difficult, even for expert users, because of the large size and complex topology of the underlying metabolic network. This interpretation could be simplified by condensing the network to a reduced, yet fully representative version. Toward this goal we report NetRed, an algorithm that systematically reduces a stoichiometric matrix and a corresponding flux vector to a more easily interpretable form. The reduction offered by NetRed is transparent because it relies purely on matrix algebra and not on optimization. Uniquely, it involves zero information loss; therefore, the original unreduced network can be easily recovered from the reduced network. The inputs to NetRed are (i) a stoichiometric matrix, (ii) a flux vector with numerical flux values, and (iii) a list of “protected” metabolites recommended by the user to remain in the reduced network. NetRed outputs a reduced metabolic network containing a reduced number of metabolites, of which the protected metabolites are a subset. The algorithm also generates a corresponding reduced flux vector. Due to its simplified presentation and easier interpretability, the reduced network allows the user to quickly find fluxes through metabolites and reaction modes or pathways of interest. In this manuscript, we first demonstrate NetRed on a simple network consisting of glycolysis and the pentose phosphate pathway (PPP), wherein NetRed reduced the PPP to a single net reaction. We followed this with applications of NetRed to E. coli and yeast GSMs. NetRed reduced the size of an E. coli GSM by 20- to 30-fold and enabled a comprehensive comparison of aerobic and anaerobic metabolism. The application of NetRed to a yeast GSM allowed for easy mechanistic interpretation of a double-gene knockout that rerouted flux toward dihydroartemisinic acid. When applied to an E. coli strain engineered for enhanced valine production, NetRed allowed for a holistic interpretation of the metabolic rerouting resulting from multiple genetic interventions.

A Genome-Scale Metabolic Model of Soybean (Glycine max) Highlights Metabolic Fluxes in Seedlings.

Until they become photoautotrophic juvenile plants, seedlings depend upon the reserves stored in seed tissues. These reserves must be mobilized and metabolized, and their breakdown products must be distributed to the different organs of the growing seedling. Here, we investigated the mobilization of soybean (Glycine max) seed reserves during seedling growth by initially constructing a genome-scale stoichiometric model for this important crop plant and then adapting the model to reflect metabolism in the cotyledons and hypocotyl/root axis (HRA). A detailed analysis of seedling growth and alterations in biomass composition was performed over 4 d of postgerminative growth and used to constrain the stoichiometric model. Flux balance analysis revealed marked differences in metabolism between the two organs, together with shifts in primary metabolism occurring during different periods postgermination. In particular, from 48 h onward, cotyledons were characterized by the oxidation of fatty acids to supply carbon for the tricarboxylic acid cycle as well as production of sucrose and glutamate for export to the HRA, while the HRA was characterized by the use of a range of imported amino acids in protein synthesis and catabolic processes. Overall, the use of flux balance modeling provided new insight into well-characterized metabolic processes in an important crop plant due to their analysis within the context of a metabolic network and reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Development and validation of an updated computational model of Streptomyces coelicolor primary and secondary metabolism

BackgroundStreptomyces species produce a vast diversity of secondary metabolites of clinical and biotechnological importance, in particular antibiotics. Recent developments in metabolic engineering, synthetic and systems biology have opened new opportunities to exploit Streptomyces secondary metabolism, but achieving industry-level production without time-consuming optimization has remained challenging. Genome-scale metabolic modelling has been shown to be a powerful tool to guide metabolic engineering strategies for accelerated strain optimization, and several generations of models of Streptomyces metabolism have been developed for this purpose.ResultsHere, we present the most recent update of a genome-scale stoichiometric constraint-based model of the metabolism of Streptomyces coelicolor, the major model organism for the production of antibiotics in the genus. We show that the updated model enables better metabolic flux and biomass predictions and facilitates the integrative analysis of multi-omics data such as transcriptomics, proteomics and metabolomics.ConclusionsThe updated model presented here provides an enhanced basis for the next generation of metabolic engineering attempts in Streptomyces.

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes.

The commercially important essential oils of peppermint (Mentha × piperita) and its relatives in the mint family (Lamiaceae) are accumulated in specialized anatomical structures called glandular trichomes (GTs). A genome-scale stoichiometric model of secretory phase metabolism in peppermint GTs was constructed based on current biochemical and physiological knowledge. Fluxes through the network were predicted based on metabolomic and transcriptomic data. Using simulated reaction deletions, this model predicted that two processes, the regeneration of ATP and ferredoxin (in its reduced form), exert substantial control over flux toward monoterpenes. Follow-up biochemical assays with isolated GTs indicated that oxidative phosphorylation and ethanolic fermentation were active and that cooperation to provide ATP depended on the concentration of the carbon source. We also report that GTs with high flux toward monoterpenes express, at very high levels, genes coding for a unique pair of ferredoxin and ferredoxin-NADP+ reductase isoforms. This study provides, to our knowledge, the first evidence of how bioenergetic processes determine flux through monoterpene biosynthesis in GTs.

Integrated molecular, physiological and in silico characterization of two Halomonas isolates from industrial brine.

Two haloalkaliphilic bacteria isolated from industrial brine solutions were characterized via molecular, physiological, and in silico metabolic pathway analyses. Genomes from the organisms, designated Halomonas BC1 and BC2, were sequenced; 16S ribosomal subunit-based phylogenetic analysis revealed a high level of similarity to each other and to Halomonas meridiana. Both strains were moderate halophiles with near optimal specific growth rates (≥60% μ max) observed over <0.1-5% (w/v) NaCl and pH ranging from 7.4 to 10.2. Isolate BC1 was further characterized by measuring uptake or synthesis of compatible solutes under different growth conditions; in complex medium, uptake and accumulation of external glycine betaine was observed while ectoine was synthesized de novo in salts medium. Transcriptome analysis of isolate BC1 grown on glucose or citrate medium measured differences in glycolysis- and gluconeogenesis-based metabolisms, respectively. The annotated BC1 genome was used to build an in silico, genome-scale stoichiometric metabolic model to study catabolic energy strategies and compatible solute synthesis under gradients of oxygen and nutrient availability. The theoretical analysis identified energy metabolism challenges associated with acclimation to high salinity and high pH. The study documents central metabolism data for the industrially and scientifically important haloalkaliphile genus Halomonas.

Biomass composition: the "elephant in the room" of metabolic modelling.

Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast’s biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0819-2) contains supplementary material, which is available to authorized users.

Dynamic flux balancing elucidates NAD(P)H production as limiting response to furfural inhibition in Saccharomyces cerevisiae.

Achieving efficient and economical lignocellulose-based bioprocess requires a robust organism tolerant to furfural, a major inhibitory compound present in lignocellulosic hydrolysate. The aim of this study was to develop a model that could generate quantitative descriptions of cell metabolism for elucidating the cell's adaptive response to furfural. Such a modelling tool could provide strategies for the design of more robust cells. A dynamic flux balance (dFBA) model of Saccharomyces cerevisiae was created by coupling a kinetic fermentation model with a previously published genome-scale stoichiometric model. The dFBA model was used for studying intracellular and extracellular flux responses to furfural perturbations under steady state and dynamic conditions. The predicted effects of furfural on dynamic flux profiles agreed well with previously published experimental results. The model showed that the yeast cell adjusts its metabolism in response to furfural challenge by increasing fluxes through the pentose phosphate pathway, TCA cycle, and proline and serine biosynthesis in order to meet the high demand of NAD(P)H cofactors. The model described here can be used to aid in systematic optimization of the yeast, as well as of the fermentation process, for efficient lignocellulosic ethanol production.

Fermentative production of the diamine putrescine: system metabolic engineering of corynebacterium glutamicum.

Corynebacterium glutamicum shows great potential for the production of the glutamate-derived diamine putrescine, a monomeric compound of polyamides. A genome-scale stoichiometric model of a C. glutamicum strain with reduced ornithine transcarbamoylase activity, derepressed arginine biosynthesis, and an anabolic plasmid-addiction system for heterologous expression of E. coli ornithine decarboxylase gene speC was investigated by flux balance analysis with respect to its putrescine production potential. Based on these simulations, enhancing glycolysis and anaplerosis by plasmid-borne overexpression of the genes for glyceraldehyde 3-phosphate dehydrogenase and pyruvate carboxylase as well as reducing 2-oxoglutarate dehydrogenase activity were chosen as targets for metabolic engineering. Changing the translational start codon of the chromosomal gene for 2-oxoglutarate dehydrogenase subunit E1o to the less preferred TTG and changing threonine 15 of OdhI to alanine reduced 2-oxoglutarate dehydrogenase activity about five fold and improved putrescine titers by 28%. Additional engineering steps improved further putrescine production with the largest contributions from preventing the formation of the by-product N-acetylputrescine by deletion of spermi(di)ne N-acetyltransferase gene snaA and from overexpression of the gene for a feedback-resistant N-acetylglutamate kinase variant. The resulting C. glutamicum strain NA6 obtained by systems metabolic engineering accumulated two fold more putrescine than the base strain, i.e., 58.1 ± 0.2 mM, and showed a specific productivity of 0.045 g·g−1·h−1 and a yield on glucose of 0.26 g·g−1.

Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure.

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

A Data Integration and Visualization Resource for the Metabolic Network of Synechocystis sp. PCC 6803

Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight in addition to statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis sp. PCC 6803, an important model cyanobacterium for sustainable biofuel production. We illustrate how this metabolic map can be used to integrate experimental and computational data for Synechocystis sp. PCC 6803 systems biology and metabolic engineering studies. Additionally, we discuss how this map, and the software infrastructure that we supply with it, can be used in the development of other organism-specific metabolic network visualizations. In addition to the Python console package VoNDA (http://vonda.sf.net), we provide a working demonstration of the interactive metabolic map and the associated Synechocystis sp. PCC 6803 genome-scale stoichiometric model, as well as various ready-to-visualize microarray data sets, at http://f-a-m-e.org/synechocytis.

Temporal Expression-based Analysis of Metabolism

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states.

Hybrid metabolic flux analysis: combining stoichiometric and statistical constraints to model the formation of complex recombinant products

BackgroundStoichiometric models constitute the basic framework for fluxome quantification in the realm of metabolic engineering. A recurrent bottleneck, however, is the establishment of consistent stoichiometric models for the synthesis of recombinant proteins or viruses. Although optimization algorithms for in silico metabolic redesign have been developed in the context of genome-scale stoichiometric models for small molecule production, still rudimentary knowledge of how different cellular levels are regulated and phenotypically expressed prevents their full applicability for complex product optimization.ResultsA hybrid framework is presented combining classical metabolic flux analysis with projection to latent structures to further link estimated metabolic fluxes with measured productivities. We first explore the functional metabolic decomposition of a baculovirus-producing insect cell line from experimental data, highlighting the TCA cycle and mitochondrial respiration as pathways strongly associated with viral replication. To reduce uncertainty in metabolic target identification, a Monte Carlo sampling method was used to select meaningful associations with the target, from which 66% of the estimated fluxome had to be screened out due to weak correlations and/or high estimation errors. The proposed hybrid model was then validated using a subset of preliminary experiments to pinpoint the same determinant pathways, while predicting the productivity of independent cultures.ConclusionsOverall, the results indicate our hybrid metabolic flux analysis framework is an advantageous tool for metabolic identification and quantification in incomplete or ill-defined metabolic networks. As experimental and computational solutions for constructing comprehensive global cellular models are in development, the contribution of hybrid metabolic flux analysis should constitute a valuable complement to current computational platforms in bridging the metabolic state with improved cell culture performance.

Environments that Induce Synthetic Microbial Ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.

Computational reconstruction of tissue‐specific metabolic models: application to human liver metabolism

The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

Quantitative assignment of reaction directionality in constraint-based models of metabolism: Application to Escherichia coli

Constraint-based modeling is an approach for quantitative prediction of net reaction flux in genome-scale biochemical networks. In vivo, the second law of thermodynamics requires that net macroscopic flux be forward, when the transformed reaction Gibbs energy is negative. We calculate the latter by using (i) group contribution estimates of metabolite species Gibbs energy, combined with (ii) experimentally measured equilibrium constants. In an application to a genome-scale stoichiometric model of Escherichia coli metabolism, iAF1260, we demonstrate that quantitative prediction of reaction directionality is increased in scope and accuracy by integration of both data sources, transformed appropriately to in vivo pH, temperature and ionic strength. Comparison of quantitative versus qualitative assignment of reaction directionality in iAF1260, assuming an accommodating reactant concentration range of 0.02–20 mM, revealed that quantitative assignment leads to a low false positive, but high false negative, prediction of effectively irreversible reactions. The latter is partly due to the uncertainty associated with group contribution estimates. We also uncovered evidence that the high intracellular concentration of glutamate in E. coli may be essential to direct otherwise thermodynamically unfavorable essential reactions, such as the leucine transaminase reaction, in an anabolic direction.