GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.     

Understanding the Adaptive Growth Strategy of Lactobacillus plantarum by In Silico Optimisation

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.     

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.     

A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium,iPS189

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.     

Predicting microbial growth dynamics in response to nutrient availability

Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker’s yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed.     

Differential Nutrient Limitation of Soil Microbial Biomass and Metabolic Quotients (qCO2): Is There a Biological Stoichiometry of Soil Microbes?

Background

Variation in microbial metabolism poses one of the greatest current uncertainties in models of global carbon cycling, and is particularly poorly understood in soils. Biological Stoichiometry theory describes biochemical mechanisms linking metabolic rates with variation in the elemental composition of cells and organisms, and has been widely observed in animals, plants, and plankton. However, this theory has not been widely tested in microbes, which are considered to have fixed ratios of major elements in soils.

Methodology/Principal Findings

To determine whether Biological Stoichiometry underlies patterns of soil microbial metabolism, we compiled published data on microbial biomass carbon (C), nitrogen (N), and phosphorus (P) pools in soils spanning the global range of climate, vegetation, and land use types. We compared element ratios in microbial biomass pools to the metabolic quotient qCO₂ (respiration per unit biomass), where soil C mineralization was simultaneously measured in controlled incubations. Although microbial C, N, and P stoichiometry appeared to follow somewhat constrained allometric relationships at the global scale, we found significant variation in the C∶N∶P ratios of soil microbes across land use and habitat types, and size-dependent scaling of microbial C∶N and C∶P (but not N∶P) ratios. Microbial stoichiometry and metabolic quotients were also weakly correlated as suggested by Biological Stoichiometry theory. Importantly, we found that while soil microbial biomass appeared constrained by soil N availability, microbial metabolic rates (qCO₂) were most strongly associated with inorganic P availability.

Conclusions/Significance

Our findings appear consistent with the model of cellular metabolism described by Biological Stoichiometry theory, where biomass is limited by N needed to build proteins, but rates of protein synthesis are limited by the high P demands of ribosomes. Incorporation of these physiological processes may improve models of carbon cycling and understanding of the effects of nutrient availability on soil C turnover across terrestrial and wetland habitats.     

Comparative Metabolic Systems Analysis of Pathogenic Burkholderia

Burkholderia cenocepacia and Burkholderia multivorans are opportunistic drug-resistant pathogens that account for the majority of Burkholderia cepacia complex infections in cystic fibrosis patients and also infect other immunocompromised individuals. While they share similar genetic compositions, B. cenocepacia and B. multivorans exhibit important differences in pathogenesis. We have developed reconciled genome-scale metabolic network reconstructions of B. cenocepacia J2315 and B. multivorans ATCC 17616 in parallel (designated iPY1537 and iJB1411, respectively) to compare metabolic abilities and contextualize genetic differences between species. The reconstructions capture the metabolic functions of the two species and give insight into similarities and differences in their virulence and growth capabilities. The two reconstructions have 1,437 reactions in common, and iPY1537 and iJB1411 have 67 and 36 metabolic reactions unique to each, respectively. After curating the extensive reservoir of metabolic genes in Burkholderia, we identified 6 genes essential to growth that are unique to iPY1513 and 13 genes uniquely essential to iJB1411. The reconstructions were refined and validated by comparing in silico growth predictions to in vitro growth capabilities of B. cenocepacia J2315, B. cenocepacia K56-2, and B. multivorans ATCC 17616 on 104 carbon sources. Overall, we identified functional pathways that indicate B. cenocepacia can produce a wider array of virulence factors compared to B. multivorans, which supports the clinical observation that B. cenocepacia is more virulent than B. multivorans. The reconciled reconstructions provide a framework for generating and testing hypotheses on the metabolic and virulence capabilities of these two related emerging pathogens.     

Metabolic modeling of endosymbiont genome reduction on a temporal scale

A fundamental challenge in Systems Biology is whether a cell‐scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network‐based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.     

A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegans development

Background

The iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.

Results

A new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.

Conclusions

Numerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.     

Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803

Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.     

Optimizing metabolic pathways by screening for feasible synthetic reactions

Background

Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.

Results

Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.

Conclusions

While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.     

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells – including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.     

Stoichiometric homeostasis in response to variable water and nutrient supply in a Robinia pseudoacacia plant–soil system

刺槐植物-土壤系统生态化学计量内稳性对水分和养分变异的响应特征 所有生物体都需要一定比例的元素来维持正常的生理代谢过程,它们的可塑性取决于它们利用外部资源的效率。阐明不同资源供应水平下植物、土壤和土壤微生物生物量生态化学计量特征之间的相互作用非常重要。本研究以一年生刺槐(Robinia pseudoacacia)幼苗为研究对象,测定不同水平水分、氮素和磷素处理下刺槐叶片、细根、土壤和微生物生物量C、N、P含量及其化学计量学指标。结果表明,刺槐叶片、细根、土壤和微生物生物量C、N、P含量及其化学计量特征会对其生存环境水分和养分条件的变化表现出一定程度的可塑性;方差分解分析结果表明,细根计量比解释了微生物生物量计量比方差的很大一部分;结构方程模型进一步揭示了细根计量比和叶片计量比是影响土壤微生物生物量C:N和C:P 的两个直接因素,而细根计量比具有较大的直接作用。此外,内稳性特征分析表明土壤微生物生物量C 和C:P对土壤养分变化较为敏感,其他指标均具有内稳性。这些结果明确了土壤微生物生物量化学计量的重要性,提高我们对不同生境水分和养分供应水平下植物-土壤系统养分循环机理的认识。     

Constraint-Based Model of Shewanella oneidensis MR-1 Metabolism: A Tool for Data Analysis and Hypothesis Generation

Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level.     

Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection

Background

Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions.

Results

The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants'' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current in silico predictions and hitherto unavailable experimental data.

Conclusions

These data address a fundamental deficiency in our understanding of the model eukaryote Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.     

Reconstruction of the Saccharopolyspora erythraea genome-scale model and its use for enhancing erythromycin production

Genome-scale metabolic reconstructions are routinely used for the analysis and design of metabolic engineering strategies for production of primary metabolites. The use of such reconstructions for metabolic engineering of antibiotic production is not common due to the lack of simple design algorithms in the absence of a cellular growth objective function. Here, we present the metabolic network reconstruction for the erythromycin producer Saccharopolyspora erythraea NRRL23338. The model was manually curated for primary and secondary metabolism pathways and consists of 1,482 reactions (2,075 genes) and 1,646 metabolites. As part of the model validation, we explored the potential benefits of supplying amino acids and identified five amino acids “compatible” with erythromycin production, whereby if glucose is supplemented with this amino acid on a carbon mole basis, the in silico model predicts that high erythromycin yield is possible without lowering biomass yield. Increased erythromycin titre was confirmed for four of the five amino acids, namely valine, isoleucine, threonine and proline. In bioreactor experiments, supplementation with 2.5?% carbon mole of valine increased the growth rate by 20?% and simultaneously the erythromycin yield on biomass by 50?%. The model presented here can be used as a framework for the future integration of high-throughput biological data sets in S. erythraea and ultimately to realise strain designs capable of increasing erythromycin production closer to the theoretical yield.     

BioMog: A Computational Framework for the De Novo Generation or Modification of Essential Biomass Components

The success of genome-scale metabolic modeling is contingent on a model''s ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism''s biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog''s de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite''s essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer''s toolbox.     

In silico analysis of bioethanol production from glucose/xylose mixtures during fed-batch fermentation of co-culture and mono-culture systems

This study presents a detailed in silico analysis of bioethanol production from glucose/xylose mixtures of various compositions by fed-batch co-culture and mono-culture fermentation of specialized microbes. The mono-culture consists of recombinant Saccharomyces cerevisise that can metabolize both hexose and pentose sugars while the co-culture system consists of substrate-selective microbes. Dynamic flux balance models based on available genome-scale reconstructions of the microorganisms have been used to analyze bioethanol production in fed-batch culture with constant feed rates and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. The simulation results clearly point to the superior performance of fed-batch fermentation of microbial co-culture against fed-batch fermentation of mono-culture for bioethanol production from glucose/xylose mixtures. A set of potential genetic engineering strategies for enhancement of S. cerevisiae and Escherichia coli strains performance have been identified. Such in silico predictions using genome-scale models provide valuable guidance for conducting in vivo metabolic engineering experiments.