Genome-scale metabolic models in plant stress physiology: implications for future climate resilience.

Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.

Machine learning applications in genome-scale metabolic modeling

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.

Genome-Scale metabolic models (GEMs) are a relevant tool in systems biology for in silico strain optimisation and drug discovery. An easier way to reconstruct a model is to use available GEMs as templates to create the initial draft, which can be curated up until a simulation-ready model is obtained. This approach is implemented in merlin’s BiGG Integration Tool, which reconstructs models from existing GEMs present in the BiGG Models database. This study aims to assess draft models generated using models from BiGG as templates for three distinct organisms, namely, Streptococcus thermophilus, Xylella fastidiosa and Mycobacterium tuberculosis. Several draft models were reconstructed using the BiGG Integration Tool and different templates (all, selected and random). The variability of the models was assessed using the reactions and metabolic functions associated with the model’s genes. This analysis showed that, even though the models shared a significant portion of reactions and metabolic functions, models from different organisms are still differentiated. Moreover, there also seems to be variability among the templates used to generate the draft models to a lower extent. This study concluded that the BiGG Integration Tool provides a fast and reliable alternative for draft reconstruction for bacteria.

Genome-scale metabolic models (GEMs) are powerful tools for predicting metabolic flux distributions, understanding complex cell physiology, and guiding the improvement of cell metabolism and production. Yarrowia lipolytica is known for its ability to accumulate lipids and has been widely employed to produce many important metabolites as an ideal host microorganism. There are six GEMs reconstructed for this strain by different research groups, which, however, may cause confusion for model users. It is therefore necessary to understand and analyze the existing models comprehensively. Different simulation results of the published GEMs of Y. lipolytica were analyzed based on experimental data, in order to understand the differences among models and identify whether there were common problems in model construction. First, specific growth rates (μ) under various culture conditions were simulated by different models, showing that the biomass generation equation in models had significant influence on the accuracy of simulation results. In addition, simulation and analysis of intracellular flux distributions revealed several inaccurate descriptions on the reversibility of reactions involving currency metabolites in the models. Finally, specific metabolite formation rates were predicted for different target products, and large discrepancies among the different models were observed. The corresponding solutions were then proposed according to the findings of the above model problems. We have corrected the existing GEMs of Y. lipolytica and the prediction performances of the models have been significantly improved. Several suggestions for better construction and refinement of genome-scale metabolic network models were also provided.

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.

Constraint-based methods (CBMs) are promising tools for the analysis of metabolic networks, as they do not require detailed knowledge of the biochemical reactions. Some of these methods only need information about the stoichiometric coefficients of the reactions and their reversibility types, i.e., constraints for steady-state conditions. Nevertheless, CBMs have their own limitations. For example, these methods may be sensitive to missing information in the models. Additionally, they may be slow for the analysis of genome-scale metabolic models. As a result, some studies prefer to consider substructures of networks, instead of complete models. Some other studies have focused on better implementations of the CBMs. In Chapter 2, the sensitivity of flux coupling analysis (FCA) to missing reactions is studied. Genome-scale metabolic reconstructions are comprehensive, yet incomplete, models of real-world metabolic networks. While FCA has proved an appropriate method for analyzing metabolic relationships and for detecting functionally related reactions in such models, little is known about the impact of missing reactions on the accuracy of FCA. Note that having missing reactions is equivalent to deleting reactions, or to deleting columns from the stoichiometric matrix. Based on an alternative characterization of flux coupling relations using elementary flux modes, we study the changes that flux coupling relations may undergo due to missing reactions. In particular, we show that two uncoupled reactions in a metabolic network may be detected as directionally, partially or fully coupled in an incomplete version of the same network. Even a single missing reaction can cause significant changes in flux coupling relations. In case of two consecutive E. coli genome-scale networks, many fully-coupled reaction pairs in the incomplete network become directionally coupled or even uncoupled in the more complete reconstruction. In this context, we found gene expression correlation values being significantly higher for the pairs that remained fully coupled than for the uncoupled or directionally coupled pairs. Our study clearly suggests that FCA results are indeed sensitive to missing reactions. Since the currently available genome-scale metabolic models are incomplete, we advise to use FCA results with care. In Chapter 3, a different, but related problem is considered. Due to the large size of genome-scale metabolic networks, some studies suggest to analyze subsystems, instead of original genome-scale models. Note that analysis of a subsystem is equivalent to deletion of some rows from the stoichiometric matrix, or identically, assuming some internal metabolites to be external. We show mathematically that analysis of a subsystem instead of the original model can lead the flux coupling relations to undergo certain changes. In particular, a pair of (fully, partially or directionally) coupled reactions may be detected as uncoupled in the chosen subsystem. Interestingly, this behavior is the opposite of the flux coupling changes that may happen due to the existence of missing reactions, or equivalently, deletion of reactions. We also show that analysis of organelle subsystems

BackgroundConstraint-based analysis of genome-scale metabolic models has become a key methodology to gain insights into functions, capabilities, and properties of cellular metabolism. Since their inception, the size and complexity of genome-scale metabolic reconstructions has significantly increased, with a concomitant increase in computational effort required for their analysis. Many stoichiometric methods cannot be applied to large networks comprising several thousand reactions. Furthermore, basic principles of an organism’s metabolism can sometimes be easier studied in smaller models focusing on central metabolism. Therefore, an automated and unbiased reduction procedure delivering meaningful core networks from well-curated genome-scale reconstructions is highly desirable.ResultsHere we present NetworkReducer, a new algorithm for an automated reduction of metabolic reconstructions to obtain smaller models capturing the central metabolism or other metabolic modules of interest. The algorithm takes as input a network model and a list of protected elements and functions (phenotypes) and applies a pruning step followed by an optional compression step. Network pruning removes elements of the network that are dispensable for the protected functions and delivers a subnetwork of the full system. Loss-free network compression further reduces the network size but not the complexity (dimension) of the solution space. As a proof of concept, we applied NetworkReducer to the iAF1260 genome-scale model of Escherichia coli (2384 reactions, 1669 internal metabolites) to obtain a reduced model that (i) allows the same maximal growth rates under aerobic and anaerobic conditions as in the full model, and (ii) preserves a protected set of reactions representing the central carbon metabolism. The reduced representation comprises 85 metabolites and 105 reactions which we compare to a manually derived E. coli core model. As one particular strength of our approach, NetworkReducer derives a condensed biomass synthesis reaction that is consistent with the full genome-scale model. In a second case study, we reduced a genome-scale model of the cyanobacterium Synechocystis sp. PCC 6803 to obtain a small metabolic module comprising photosynthetic core reactions and the Calvin-Benson cycle allowing synthesis of both biomass and a biofuel (ethanol).ConclusionAlthough only genome-scale models provide a complete description of an organism’s metabolic capabilities, an unbiased stoichiometric reduction of large-scale metabolic models is highly useful. We are confident that the NetworkReducer algorithm provides a valuable tool for the application of computationally expensive analyses, for educational purposes, as well to identify core models for kinetic modeling and isotopic tracer experiments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0191-x) contains supplementary material, which is available to authorized users.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On–Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

The current investigation is aimed at the reconstruction and analysis of genome-scale metabolic models. Specifically, it is focused on the use of mathematical-computational simulations to predict the cellular metabolism behavior towards bio-products production. The photosynthetic cyanobacterium Synechococcus elongatus PCC7942 was studied as biological system. This prokaryotic has been used in several studies as a biological platform for the synthesis of several substances for industrial interest. These studies are based on the advantage of autotrophic systems, which basically requires light and CO2 for growth. The main objective of this thesis is the integration of different types of biological information, whose interaction can be extract applicable knowledge for economic interests. To this end, our study was addressed to the use of methods for modeling, analyzing and predicting the behavior of metabolic phenotypes of cyanobacterium. The work has been divided into chapters organized sequentially, where the starting point was the in silico metabolic network reconstruction. This process intent to join in a metabolic model of all chemical reactions codified in genome. The stoichiometric coefficients of each reactions, can be arranged into a sparse matrix (stoichiometric matrix), where the columns corresponds to reactions and rows to metabolites. As a result of this process the first model was obtained (iSyf646) than later was updated to another (iSyf715). Both were generated from data -omics published in databases, scientific reviews as well as textbooks. To validate them, each one of the stoichiometric matrix together with relevant constraints were used by simulation techniques based on linear programming. These reconstructions have to be flexible enough to allow autotrophic growth under which the organism grows in nature. Once the reconstructions were validated, environmental variations can be simulated and we were able to study its effects through changes in outline system parameters. Subsequently, synthetic capabilities were evaluated from the in silico models in order to design metabolic engineering strategies. To do this a genetic variation was simulated in reactions network, where the disturbed stoichiometric matrix was the object of the quadratic optimization methods. As a result sets of optimal solutions were generated to enhanced production of various metabolites of energetic and industrial interest such as: ethanol, higher chain alcohols, lipids and hydrogen. Qualitatively distinct patterns of metabolic pathway utilization were identified by generation of phenotypic phase planes for biomass growth and synthesis of the bio-products as objective functions. We analyzed the variations of CO2 and light uptakes rates over the genome-scale metabolic network. Finally, genome-scale metabolic models allow us to establish criteria to integrate different types of data to help of find important points of regulation that may be subject to genetic modification. These regulatory centers have been investigated under drastic changes of CO2 concentration on ambient and have been inferred operational principles of cyanobacterium metabolism. In general, this thesis presents the metabolic capabilities of photosynthetic cyanobacterium Synechococcus elongatus PCC7942 to produce substances of interest, being a potential biological platform for clean and sustainable production.

ABSTRACTConstruction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.