GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization

Biotechnology is currently evolving through the era of big data, thanks to advances in the high-throughput technologies for rapid and inexpensive genome sequencing and other genome-wide studies [1]. With the daunting amount of data, it has been possible to put them together into a coherently organized biological network that provides counterintuitive insights on biological systems [2]. Among such biological networks, a genome-scale metabolic network model is expected to play an increasingly important role in the biopharmaceutical industry [3]. Before enumerating their specific strengths, it is important to note that principles underlying genome-scale metabolic network models are consistent with the holistic perspective of systems biology, the aim of which is to unveil hidden factors causing diseases and to find relevant treatment strategies [4]. Despite the importance of metabolism in a biological system, studies on diseases in relation to metabolism were far fewer in number than those performed on signaling and transcriptional regulatory networks [5]. However, metabolism, highly linked with observable phenotypes, is a biological network that is more comprehensively characterized when compared with the other two types of networks [6]. Metabolism is, therefore, amenable to large-scale mathematical modeling and simulation. It is with this motivation that the genome-scale metabolic simulation deserves more attention in drug discovery campaigns and optimization of a host strain for the production of biopharmaceuticals. Reconstruction and application of genome-scale metabolic network models have been forged as a major research strategy of systems biology. Over the last decade, genome-scale metabolic models have been built for almost all biologically important organisms across the domains of archea, bacteria and eukaryotes [3]. They range from simple micro organisms such as Escherichia coli [7] and Saccharomyces cerevisiae [8] to higher organisms including Chinese hamster ovary (CHO) cells [9,10] and a generic human cell [11,12]. It should be noted that all these organisms that have been subjected to metabolic modeling are important cellular hosts for biopharmaceutical production or medically meaningful organisms that need to be cured (e.g., specific cancer cells) or destroyed (e.g., pathogens). A recent notable development of importance in the genomescale metabolic modeling would be the newly updated human metabolic network Recon 2 [12]. Recon 2 is a result of efforts from a group of researchers, going over a vast amount of literature and biochemical data and reconciling conflicting information. Scope of the hitherto reconstructed genome-scale metabolic models manifest high expectations for their potential contributions to biopharmaceutical industry. Genome-scale metabolic network models are not just a simple pileup of biochemical reactions, but allow mathematical simulation under precisely defined conditions of constraints [13]. Once the experimentally Applications of genome-scale metabolic network models in the biopharmaceutical industry

BackgroundThe availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.ResultsWe present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production.ConclusionAlthough not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models.

Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available 'omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

iMet: A graphical user interface software tool to merge metabolic networks

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.

The genome-scale metabolic network model (GEM) is a mathematical framework based on gene-protein-reaction associations combined with stoichiometric balance and is capable of facilitating the computation and prediction of multiscale phenotypes by optimizing the objective function of interest. It has been increasingly used as an important tool for understanding cellular metabolism and characterizing cell phenotypes. In cells, metabolism is tightly controlled by intricate regulatory mechanisms at the different system levels and is strictly regulated to ensure the dynamic adaptation of biochemical reaction fluxes for maintaining cell homeostasis to ultimately achieve optimal metabolic fitness. Advances in high-throughput screening and analysis technologies have generated massive amounts of genome sequences, along with transcriptomic, proteomic and metabolomic data, providing quantitative regulatory information to gain insights into cellular metabolism; however, integrating the available omics data into constraint-based metabolic models and quantitatively profiling genotype-phenotype relationships remains an outstanding challenge for computational biology. Here, we describe the recent developments in introducing macromolecular expression into GEMs and generating metabolic expression (ME) models, which increase the complexity and predictive capability of computational frameworks. Various algorithms employ different approaches to combine additional layers of omics data to limit the cone of allowable flux distributions in the metabolic model. In this review, we categorize all methods by five different grouping criteria and evaluate their practical perspectives. The first category of methods utilizes a threshold to distinguish active and inactive states of the corresponding reactions based on the gene expression measurement data. The second uses omics data to build cell- and tissue-specific models of human metabolism by removing unexpressed reactions from the global human metabolic network. The third category of methods involves modifying reaction bounds on the basis of mRNA and protein abundance, in which the width of the “flux cone” is adjusted via the maximum possible flux in the upper bound of the FBA optimization problem dependent on gene and protein expression levels. The imposition of constraints further defines the associated solution space of the model to improve the prediction accuracy. The fourth model incorporates transcriptional regulation networks (TRNs), which describe the phenomenological interactions between different biomolecules in response to genetic and environmental perturbation, into GEMs and avoids the obstacles of information formulation to achieve comprehensive knowledge regarding the metabolic and regulatory events occurring inside the cell. The last category integrates time-series transcriptomics data with flux-based bilevel optimization to comprehend the interplay between metabolism and regulation in time-dependent processes. We compare the advantages and limitations of different categories and explore the application areas of integrated models in analyzing metabolic characteristics, interpreting phenotypic states and the consequences of environmental and genetic perturbations while discovering potential drug targets and screening anti-metabolic drugs for cancer treatment. Finally, we also highlight the future perspectives and challenges for GEM-based reconstruction with omics data integration.

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.

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.

Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr.

Due to the increasing demand for microbially manufactured products in various industries, it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products. Recently, with the gradual cross-fertilization between computer science and bioinformatics fields, machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models (GSMMs) based on constrained optimization methods, and many high-quality related works have been published. Therefore, this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering, with special emphasis on GSMMs. Specifically, the development history of GSMMs is first reviewed. Then, the analysis methods of GSMMs based on constraint optimization are presented. Next, this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models. In addition, the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

Genome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method and applications of metabolic network reconstruction as well as some related sources. In section 1.2, we describe omics data and high-throughput technologies for gaining these data. In section 1.3, we present different ways for metabolic network modeling. In section 1.4, we summarize the history of genome-scale modeling as one of the most important method for metabolic network modeling. In sections 1.5 and 1.6, we elucidate how genome-scale metabolic models could be generated and what are their applications. Finally, in section 1.7, we review biochemical pathways and genome annotation databases.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models