Using Genome-scale Models to Predict Biological Capabilities

Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

Escherichia coli is capable of growing in many environments, each of which requires a different collection of enzymes to metabolize the nutrients within that environment. Each individual enzyme requires its own set of amino acids and oftentimes cofactors, which are accessory molecules essential for the enzyme to function. Thus, the composition of the micronutrients (amino acids, cofactors, etc.) within a cell will differ depending on its metabolic needs. The presented work is the first effort to employ metabolic models to probe the connection between E. coli’s diverse growth environments and its biomass composition. We first show how differences in model-predicted enzyme use for aerobic or anaerobic growth results in distinct amino acid and cofactor usage. Alternatively, we show that the metabolic models can predict how modifying the cell’s biomass composition will affect growth. For example, by modeling the exposure of E. coli to trimethoprim or sulfamethoxazole—two antibiotics that target folate (vitamin B9) synthesis—we predicted how E. coli could adapt to grow under folate-limited conditions. This work demonstrates how models can be used to study antibiotic resistance of drugs that target amino acid or cofactor synthesis.

A forecast for large-scale, predictive biology: Lessons from meteorology

Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of ¹³C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

There is an increasing demand for protein pharmaceuticals, especially monoclonal antibodies. Chinese Hamster Ovary (CHO) are currently the leading production host due to their ability to perform human-like post-translational modifications. However, it typically takes several months of trial-and-error approaches to develop a high-producer cell line. Metabolic modelling has the potential to make cell line and process development faster and cheaper by predicting targeted modifications to the cell line genome, cultivation medium or bioprocess. In fact, genome-scale metabolic reconstructions of CHO are already available, and ready for use in cell line development. However, in order to successfully use these models, we need to make sure that they are able to accurately predict metabolic phenotypes. Here we use genome-scale metabolic models of CHO to evaluate the models’ ability to correctly predict intracellular flux distributions. We find that a crucial key ingredient for the correct estimation of central carbon fluxes is the non-growth associated maintenance energy (mATP). We estimated mATP computationally and confirmed it experimentally. Adding this single constraint leads to significantly better predictions of intracellular fluxes, especially in glycolysis and the tricarboxylic acid cycle.

From omics to Cellular mechanisms in mammalian cell factory development

Mammalian cells have been used widely as biopharmaceutical cell factories due to their ability to make complex biotherapeutic proteins with human-compatible modifications. However, their application for some products has been hampered by low protein yields. Numerous studies have aimed to characterize cellular bottlenecks in the hope of boosting protein productivity, but the complexity of the underlying pathways and the diversity of the modifications have complicated cell engineering when relying solely on traditional methodologies. Incorporating omics-based and systems approaches into cell engineering can provide valuable insights into desirable phenotypes of cell factories. Here, we discuss cell engineering strategies for enhancing protein productivity in mammalian cell factories, particularly CHO and HEK293, and the opportunities and limitations of the genome-wide screening and multi-omics approaches for guiding cell engineering. Systems biology strategies will also be discussed to show how they refine our understanding of the cellular mechanisms which will aid in effective engineering strategies.

The Axes of Life: A roadmap for understanding dynamic multiscale systems

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

The quantitative metabolome is shaped by abiotic constraints

Living systems formed and evolved under constraints that govern their interactions with the inorganic world. These interactions are definable using basic physico-chemical principles. Here, we formulate a comprehensive set of ten governing abiotic constraints that define possible quantitative metabolomes. We apply these constraints to a metabolic network of Escherichia coli that represents 90% of its metabolome. We show that the quantitative metabolomes allowed by the abiotic constraints are consistent with metabolomic and isotope-labeling data. We find that: (i) abiotic constraints drive the evolution of high-affinity phosphate transporters; (ii) Charge-, hydrogen- and magnesium-related constraints underlie transcriptional regulatory responses to osmotic stress; and (iii) hydrogen-ion and charge imbalance underlie transcriptional regulatory responses to acid stress. Thus, quantifying the constraints that the inorganic world imposes on living systems provides insights into their key characteristics, helps understand the outcomes of evolutionary adaptation, and should be considered as a fundamental part of theoretical biology and for understanding the constraints on evolution.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis

Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor.

Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis

Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

Utilizing tandem mass spectrometry for metabolic flux analysis

Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.

In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

Nucleotide metabolism plays a central role in bacterial physiology, producing the nucleic acids necessary for DNA replication and RNA transcription. Recent studies demonstrate that nucleotide metabolism also proactively contributes to antibiotic-induced lethality in bacterial pathogens and that disruptions to nucleotide metabolism contributes to antibiotic treatment failure in the clinic. As antimicrobial resistance continues to grow unchecked, new approaches are needed to study the molecular mechanisms responsible for antibiotic efficacy. Here we review emerging technologies poised to transform understanding into why antibiotics may fail in the clinic. We discuss how these technologies led to the discovery that nucleotide metabolism regulates antibiotic drug responses and why these are relevant to human infections. We highlight opportunities for how studies into nucleotide metabolism may enhance understanding of antibiotic failure mechanisms.

multiTFA: a Python package for multi-variate thermodynamics-based flux analysis

MOTIVATION

We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables.

RESULTS

We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible.

AVAILABILITY AND IMPLEMENTATION

Our framework along with documentation is available on https://github.com/biosustain/multitfa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying and Engineering Bottlenecks of Autotrophic Isobutanol Formation in Recombinant C. ljungdahlii by Systemic Analysis

Clostridium ljungdahlii (C. ljungdahlii, CLJU) is natively endowed producing acetic acid, 2,3-butandiol, and ethanol consuming gas mixtures of CO2, CO, and H2 (syngas). Here, we present the syngas-based isobutanol formation using C. ljungdahlii harboring the recombinant amplification of the "Ehrlich" pathway that converts intracellular KIV to isobutanol. Autotrophic isobutanol production was studied analyzing two different strains in 3-L gassed and stirred bioreactors. Physiological characterization was thoroughly applied together with metabolic profiling and flux balance analysis. Thereof, KIV and pyruvate supply were identified as key "bottlenecking" precursors limiting preliminary isobutanol formation in CLJU[KAIA] to 0.02 g L-1. Additional blocking of valine synthesis in CLJU[KAIA]:ilvE increased isobutanol production by factor 6.5 finally reaching 0.13 g L-1. Future metabolic engineering should focus on debottlenecking NADPH availability, whereas NADH supply is already equilibrated in the current generation of strains.

Reconstruction and analysis of a genome-scale metabolic model for Agrobacterium tumefaciens

The plant pathogen Agrobacterium tumefaciens causes crown gall disease and is a widely used tool for generating transgenic plants owing to its virulence. The pathogenic process involves a shift from an independent to a living form within a host plant. However, comprehensive analyses of metabolites, genes, and reactions contributing to this complex process are lacking. To gain new insights about the pathogenicity from the viewpoints of physiology and cellular metabolism, a genome-scale metabolic model (GSMM) was reconstructed for A. tumefaciens. The model, referred to as iNX1344, contained 1,344 genes, 1,441 reactions, and 1,106 metabolites. It was validated by analyses of in silico cell growth on 39 unique carbon or nitrogen sources and the flux distribution of carbon metabolism. A. tumefaciens metabolic characteristics under three ecological niches were modelled. A high capacity to access and metabolize nutrients is more important for rhizosphere colonization than in the soil, and substantial metabolic changes were detected during the shift from the rhizosphere to tumour environments. Furthermore, by integrating transcriptome data for tumour conditions, significant alterations in central metabolic pathways and secondary metabolite metabolism were identified. Overall, the GSMM and constraint-based analysis could decode the physiological and metabolic features of A. tumefaciens as well as interspecific interactions with hosts, thereby improving our understanding of host adaptation and infection mechanisms.

Succinate dehydrogenase inhibitors: in silico flux analysis and in vivo metabolomics investigations show no severe metabolic consequences for rats and humans

Succinate dehydrogenase complex II inhibitors (SDHIs) are widely used fungicides since the 1960s. Recently, based on published in vitro cell viability data, potential health effects via disruption of the mitochondrial respiratory chain and tricarboxylic acid cycle have been postulated in mammalian species. As primary metabolic impact of SDH inhibition, an increase in succinate, and compensatory ATP production via glycolysis resulting in excess lactate levels was hypothesized. To investigate these hypotheses, genome-scale metabolic models of Rattus norvegicus and Homo sapiens were used for an in silico analysis of mammalian metabolism. Moreover, plasma samples from 28-day studies with the SDHIs boscalid and fluxapyroxad were subjected to metabolome analyses, to assess in vivo metabolite changes induced by SDHIs. The outcome of in silico analyses indicated that mammalian metabolic networks are robust and able to compensate different types of metabolic perturbation, e.g., partial or complete SDH inhibition. Additionally, the in silico comparison of rat and human responses suggested no noticeable differences between both species, evidencing that the rat is an appropriate testing organism for toxicity of SDHIs. Since no succinate or lactate accumulation were found in rats, such an accumulation is also not expected in humans as a result of SDHI exposure.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917

Escherichia coli Nissle 1917 (EcN) is an intestinal probiotic that is effective for the treatment of intestinal disorders, such as inflammatory bowel disease and ulcerative colitis. EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics.

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.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

A framework based on fundamental biochemical principles to engineer microbial community dynamics

Microbial communities are complex but there are basic principles we can apply to constrain the assumed stochasticity of their activity. By understanding the trade-offs behind the kinetic parameters that define microbial growth, we can explain how local interspecies dependencies arise and shape the emerging properties of a community. If we integrate these theoretical descriptions with experimental 'omics' data and bioenergetics analysis of specific environmental conditions, predictions on activity, assembly and spatial structure can be obtained reducing the a priori unpredictable complexity of microbial communities. This information can be used to define the appropriate selective pressures to engineer bioprocesses and propose new hypotheses which can drive experimental research to accelerate innovation in biotechnology.

Bacterial fitness landscapes stratify based on proteome allocation associated with discrete aero-types

The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define "aero-types", a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Impact of explicit area scaling on kinetic models involving multiple compartments

BACKGROUND

Computational modelling of cell biological processes is a frequently used technique to analyse the underlying mechanisms and to generally understand the behaviour of these processes in the context of a pathway, network or even the whole cell. The most common technique in this context is the usage of ordinary differential equations that describe the kinetics of the relevant processes in mechanistic detail. Here, it is usually assumed that the content of the cell is well-stirred and thus homogeneous - which is of course an over-simplification, but often worked in the past. However, many processes happen at membranes and thus not in 3D, but in 2D. The scaling of the rates of these processes poses a special problem, if volumes of compartments are changed. They will typically scale with an area, but not with the volume of the involved compartment. However, commonly, this is neglected when setting up models and/or volume scaling also sometimes automatically happens when using modelling software in the field.

RESULTS

Here, we investigate generic as well as specific, realistic cases to find out, how strong the impact of the wrong scaling is for the outcome of simulations. We show that the importance of correct area scaling depends on the architecture of the reaction site and its changes upon volume alterations and it is hard to foresee, if it has a significant impact or not just by looking at the original model set-up. Moreover, scaled rates might exhibit more or less control over the behaviour of the system and therefore, accordingly, incorrect scaling will have more or less influence.

CONCLUSIONS

Working with multi-compartment reactions requires a careful consideration of the correct scaling of the rates when changing the volumes of the involved compartments. The error following incorrect scaling - often done by scaling with the volume of the respective compartments can lead to significant aberrations of model behaviour.

Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia

Fibrosis is the pathophysiological hallmark of progressive chronic kidney disease (CKD). The kidney is a highly metabolically active organ, and it has been suggested that disruption in its metabolism leads to renal fibrosis. We developed a longitudinal mouse model of acute kidney injury leading to CKD and an in vitro model of epithelial to mesenchymal transition to study changes in metabolism, inflammation, and fibrosis. Using transcriptomics, metabolic modeling, and serum metabolomics, we observed sustained fatty acid metabolic dysfunction in the mouse model from early to late stages of CKD. Increased fatty acid biosynthesis and downregulation of catabolic pathways for triglycerides and diacylglycerides were associated with a marked increase in these lipids in the serum. We therefore suggest that the kidney may be the source of the abnormal lipid profile seen in patients with CKD, which may provide insights into the association between CKD and cardiovascular disease.

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Following AKI, markers of fibrosis and inflammation go up simultaneously

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AKI is associated with reduced fatty acid oxidation and oxidative phosphorylation

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Changes in metabolism persist as chronic kidney disease develops

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Changes in metabolism are associated with increased serum levels of triglycerides

Amino Acid Transporters on the Guard of Cell Genome and Epigenome

Tumorigenesis is driven by metabolic reprogramming. Oncogenic mutations and epigenetic alterations that cause metabolic rewiring may also upregulate the reactive oxygen species (ROS). Precise regulation of the intracellular ROS levels is critical for tumor cell growth and survival. High ROS production leads to the damage of vital macromolecules, such as DNA, proteins, and lipids, causing genomic instability and further tumor evolution. One of the hallmarks of cancer metabolism is deregulated amino acid uptake. In fast-growing tumors, amino acids are not only the source of energy and building intermediates but also critical regulators of redox homeostasis. Amino acid uptake regulates the intracellular glutathione (GSH) levels, endoplasmic reticulum stress, unfolded protein response signaling, mTOR-mediated antioxidant defense, and epigenetic adaptations of tumor cells to oxidative stress. This review summarizes the role of amino acid transporters as the defender of tumor antioxidant system and genome integrity and discusses them as promising therapeutic targets and tumor imaging tools.

Prediction of Cellular Burden with Host-Circuit Models

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.

Genome-Scale Metabolic Modeling of Escherichia coli and Its Chassis Design for Synthetic Biology Applications

Genome-scale metabolic modeling is and will continue to play a central role in computational systems metabolic engineering and synthetic biology applications for the productions of chemicals and antibiotics. To that end, a survey and workflows of methods used for the development of high-quality genome-scale metabolic models (GEMs) and chassis design for synthetic biology are described here. The chapter consists of two parts (a) the methods of development of GEMs (Escherichia coli as a case study) and (b) E. coli chassis design for synthetic production of 1,4-butanediol (BDO). The methods described here can guide existing and future development of GEMs coupled with host chassis design for synthetic productions of novel antibiotics.

Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes.

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

Metabolic modelling approaches for describing and engineering microbial communities

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies

Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of Escherichia coli K-12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, in situ characterization of microbial metabolism.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Integrative characterization of the near-minimal bacterium Mesoplasma florum

The near-minimal bacterium Mesoplasma florum is an interesting model for synthetic genomics and systems biology due to its small genome (~ 800 kb), fast growth rate, and lack of pathogenic potential. However, fundamental aspects of its biology remain largely unexplored. Here, we report a broad yet remarkably detailed characterization of M. florum by combining a wide variety of experimental approaches. We investigated several physical and physiological parameters of this bacterium, including cell size, growth kinetics, and biomass composition of the cell. We also performed the first genome-wide analysis of its transcriptome and proteome, notably revealing a conserved promoter motif, the organization of transcription units, and the transcription and protein expression levels of all protein-coding sequences. We converted gene transcription and expression levels into absolute molecular abundances using biomass quantification results, generating an unprecedented view of the M. florum cellular composition and functions. These characterization efforts provide a strong experimental foundation for the development of a genome-scale model for M. florum and will guide future genome engineering endeavors in this simple organism.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks

Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

The Bitome: digitized genomic features reveal fundamental genome organization

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Electron availability in CO2 , CO and H2 mixtures constrains flux distribution, energy management and product formation in Clostridium ljungdahlii

Acetogens such as Clostridium ljungdahlii can play a crucial role reducing the human CO2 footprint by converting industrial emissions containing CO2 , CO and H2 into valuable products such as organic acids or alcohols. The quantitative understanding of cellular metabolism is a prerequisite to exploit the bacterial endowments and to fine-tune the cells by applying metabolic engineering tools. Studying the three gas mixtures CO2  + H2 , CO and CO + CO2  + H2 (syngas) by continuously gassed batch cultivation experiments and applying flux balance analysis, we identified CO as the preferred carbon and electron source for growth and producing alcohols. However, the total yield of moles of carbon (mol-C) per electrons consumed was almost identical in all setups which underlines electron availability as the main factor influencing product formation. The Wood-Ljungdahl pathway (WLP) showed high flexibility by serving as the key NAD+ provider for CO2  + H2, whereas this function was strongly compensated by the transhydrogenase-like Nfn complex when CO was metabolized. Availability of reduced ferredoxin (Fdred ) can be considered as a key determinant of metabolic control. Oxidation of CO via carbon monoxide dehydrogenase (CODH) is the main route of Fdred formation when CO is used as substrate, whereas Fdred is mainly regenerated via the methyl branch of WLP and the Nfn complex utilizing CO2  + H2 . Consequently, doubled growth rates, highest ATP formation rates and highest amounts of reduced products (ethanol, 2,3-butanediol) were observed when CO was the sole carbon and electron source.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

SAMMI: a semi-automated tool for the visualization of metabolic networks

SUMMARY

Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models.

AVAILABILITY AND IMPLEMENTATION

SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.