Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Emerging methods for genome-scale metabolic modeling of microbial communities

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Metabolic model-based ecological modeling for probiotic design

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

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.

MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.

Systematically gap-filling the genome-scale metabolic model of CHO cells

OBJECTIVE

Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.

RESULTS

We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.

CONCLUSIONS

The present CHO model is an important step towards more complete metabolic models of CHO cells.

A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.

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1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes

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Life-stage-specific metabolic models reveal reprogramming of cellular function

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High agreement between blood/liver stage metabolic models and genetic screening data

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Essential metabolic pathways for parasite development and mechanistic origin revealed

Computational systems biology of cellular processes in Arabidopsis thaliana: an overview

Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.

Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning

Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale metabolic models. We generate an ensemble of models, each equally consistent with experimental data, then perform simulations with them. We apply machine learning to the simulation output to identify model structure variation that maximally influences simulations. These variants are high-priority candidates for curation through removal, addition, or reannotation in the model. We apply this approach, automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS), to 29 bacterial species to improve gene essentiality predictions. We explore targets for individual species and compile pan-species targets to improve the database used during model construction. AMMEDEUS is an automated and performance-driven recommendation system that complements intuition during curation of biochemical knowledgebases.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

MOTIVATION

Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.

RESULTS

As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.

AVAILABILITY AND IMPLEMENTATION

The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modelling approaches for studying the microbiome

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.

Combining multiple functional annotation tools increases coverage of metabolic annotation

Background

Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30–50% of genes without functional annotation, severely limiting our knowledge of the “parts lists” that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes.

Results

Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3–8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli.

Conclusions

Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5221-9) contains supplementary material, which is available to authorized users.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers

Background

Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.

Methods

We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.

Results

We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC.

Conclusions

Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0586-6) contains supplementary material, which is available to authorized users.

Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota

The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling

Malnutrition is a severe non-communicable disease, which is prevalent in children from low-income countries. Recently, a number of metagenomics studies have illustrated associations between the altered gut microbiota and child malnutrition. However, these studies did not examine metabolic functions and interactions between individual species in the gut microbiota during health and malnutrition. Here, we applied genome-scale metabolic modeling to model the gut microbial species, which were selected from healthy and malnourished children from three countries. Our analysis showed reduced metabolite production capabilities in children from two low-income countries compared with a high-income country. Additionally, the models were also used to predict the community-level metabolic potentials of gut microbes and the patterns of pairwise interactions among species. Hereby we found that due to bacterial interactions there may be reduced production of certain amino acids in malnourished children compared with healthy children from the same communities. To gain insight into alterations in the metabolism of malnourished (stunted) children, we also performed targeted plasma metabolic profiling in the first 2 years of life of 25 healthy and 25 stunted children. Plasma metabolic profiling further revealed that stunted children had reduced plasma levels of essential amino acids compared to healthy controls. Our analyses provide a framework for future efforts towards further characterization of gut microbial metabolic capabilities and their contribution to malnutrition.

How accurate is automated gap filling of metabolic models?

Background

Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions — anaerobic growth under four nutrients producing 53 biomass metabolites.

Results

The solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions.

Conclusions

Our conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0593-7) contains supplementary material, which is available to authorized users.

Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models

Summary

Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED’s ability to automatically reconstruct metabolic models with COBRApy’s advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms.

Availability and Implementation

This package works with Python 2.7, 3.4, and 3.5 on MacOS, Linux and Windows. The source code is available from https://github.com/mmundy42/mackinac.

Advances in analytical tools for high throughput strain engineering

The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.

Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.

Thermodynamic Constraints Improve Metabolic Networks

In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation.

Genomics of apicomplexan parasites

The increasing prevalence of infections involving intracellular apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium (the causative agents of malaria, toxoplasmosis, and cryptosporidiosis, respectively) represent a significant global healthcare burden. Despite their significance, few treatments are available; a situation that is likely to deteriorate with the emergence of new resistant strains of parasites. To lay the foundation for programs of drug discovery and vaccine development, genome sequences for many of these organisms have been generated, together with large-scale expression and proteomic datasets. Comparative analyses of these datasets are beginning to identify the molecular innovations supporting both conserved processes mediating fundamental roles in parasite survival and persistence, as well as lineage-specific adaptations associated with divergent life-cycle strategies. The challenge is how best to exploit these data to derive insights into parasite virulence and identify those genes representing the most amenable targets. In this review, we outline genomic datasets currently available for apicomplexans and discuss biological insights that have emerged as a consequence of their analysis. Of particular interest are systems-based resources, focusing on areas of metabolism and host invasion that are opening up opportunities for discovering new therapeutic targets.

Metabolic systems modeling for cell factories improvement

Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

Metabolism is the driving force behind all biological activity. Genome-scale metabolic network reconstructions (GENREs) are representations of metabolic systems that can be analyzed mathematically to make predictions about how a system will behave, as well as to design systems with new properties. GENREs have traditionally been reconstructed manually, which can require extensive time and effort. Recent software solutions automate the process (drastically reducing the required effort) but the resulting GENREs are of lower quality and produce less reliable predictions than the manually-curated versions. We present a novel method (“EnsembleFBA”) which accounts for uncertainties involved in automated reconstruction by pooling many different draft GENREs together into an ensemble. We tested EnsembleFBA by predicting the growth and essential genes of the common pathogen Pseudomonas aeruginosa. We found that when predicting growth or essential genes, ensembles of GENREs achieved much better precision or captured many more essential genes than any of the individual GENREs within the ensemble. By improving the predictions that can be made with automatically-generated GENREs, this approach enables the modeling of biochemical systems which would otherwise be infeasible.

Discovering missing reactions of metabolic networks by using gene co-expression data

Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

DEF: an automated dead-end filling approach based on quasi-endosymbiosis

Motivation

Gap filling for the reconstruction of metabolic networks is to restore the connectivity of metabolites via finding high-confidence reactions that could be missed in target organism. Current methods for gap filling either fall into the network topology or have limited capability in finding missing reactions that are indirectly related to dead-end metabolites but of biological importance to the target model.

Results

We present an automated dead-end filling (DEF) approach, which is derived from the wisdom of endosymbiosis theory, to fill gaps by finding the most efficient dead-end utilization paths in a constructed quasi-endosymbiosis model. The recalls of reactions and dead ends of DEF reach around 73% and 86%, respectively. This method is capable of finding indirectly dead-end-related reactions with biological importance for the target organism and is applicable to any given metabolic model. In the E. coli iJR904 model, for instance, about 42% of the dead-end metabolites were fixed by our proposed method.

Availabilty and Implementaion

DEF is publicly available at http://bis.zju.edu.cn/DEF/.

Contact

mchen@zju.edu.cn

Supplimentary Information

Supplementary data are available at Bioinformatics online.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.

IMPORTANCE

Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

Metabolic modeling with Big Data and the gut microbiome

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model

Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.

PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database

The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). This new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

Synthetic Ecology of Microbes: Mathematical Models and Applications

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

Metabolic network modeling of microbial communities

Genome-scale metabolic network reconstructions and constraint-based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome-scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the 'enzyme-soup' approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high-level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering.

Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput 'omics' technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.

Data-driven integration of genome-scale regulatory and metabolic network models

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert-a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

Genome-scale modeling for metabolic engineering

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Transparency in metabolic network reconstruction enables scalable biological discovery

Reconstructing metabolic pathways has long been a focus of active research. Now, draft models can be generated from genomic annotation and used to simulate metabolic fluxes of mass and energy at the whole-cell scale. This approach has led to an explosion in the number of functional metabolic network models. However, more models have not led to expanded coverage of metabolic reactions known to occur in the biosphere. Thus, there exists opportunity to reconsider the process of reconstruction and model derivation to better support the less-scalable investigative processes of biocuration and experimentation. Realizing this opportunity to improve our knowledge of metabolism requires developing new tools that make reconstructions more useful by highlighting metabolic network knowledge limitations to guide future research.