COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model.

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via 13C-metabolic flux analysis (13C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated 13C labeling data were used with 13C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data.

Kinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces Kinetics-based Fluxomics Integration Tool (K-FIT), a robust kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model for E. coli (307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data.

Synechococcus elongatus UTEX 2973 (Synechococcus 2973) has the shortest reported doubling time (2.1 h) among cyanobacteria, making it a promising platform for the solar-based production of biochemicals. In this meta-analysis, its intracellular flux distribution was recomputed using genome-scale isotopic nonstationary 13C-metabolic flux analysis given the labeling dynamics of 13 metabolites reported in an earlier study. To achieve this, a genome-scale mapping model, namely imSyu593, was constructed using the imSyn617 mapping model for Synechocystis sp. PCC 6803 (Synechocystis 6803) as the starting point encompassing 593 reactions. The flux elucidation revealed nearly complete conversion (greater than 96%) of the assimilated carbon into biomass in Synechococcus 2973. In contrast, Synechocystis 6803 achieves complete conversion of only 86% of the assimilated carbon. This high biomass yield was enabled by the reincorporation of the fixed carbons lost in anabolic and photorespiratory pathways in conjunction with flux rerouting through a nondecarboxylating reaction such as phosphoketolase. This reincorporation of lost CO2 sustains a higher flux through the photorespiratory C2 cycle that fully meets the glycine and serine demands for growth. In accordance with the high carbon efficiency drive, acetyl-coenzyme A was entirely produced using the carbon-efficient phosphoketolase pathway. Comparison of the Synechococcus 2973 flux map with that of Synechocystis 6803 revealed differences in the use of Calvin cycle and photorespiratory pathway reactions. The two species used different reactions for the synthesis of metabolites such as fructose-6-phosphate, glycine, sedoheptulose-7-phosphate, and Ser. These findings allude to a highly carbon-efficient metabolism alongside the fast carbon uptake rate in Synechococcus 2973, which explains its faster growth rate.

Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models.

Completeness and accuracy of metabolic mapping models impacts the reliability of flux estimation in photoautotrophic systems. In this study, metabolic fluxes under photoautotrophic growth conditions in the widely-used cyanobacterium Synechocystis PCC 6803 are quantified by re-analyzing an existing dataset using genome-scale isotopic instationary 13C-Metabolic Flux Analysis (INST-MFA). The reconstructed carbon mapping model imSyn617 and implemented algorithmic updates afforded an approximately 48% reduction in computation time. The mapping model encompasses 18 novel carbon paths spanning Calvin-Benson-Bassham cycle, photorespiration, an expanded glyoxylate metabolism, and corrinoid biosynthetic pathways and 190 additional metabolites absent in core models currently used for MFA. Flux elucidation reveals that 98% of the fixed carbons is routed towards biomass production with small amounts diverted towards organic acids and glycogen storage. 12% of the fixed carbons are oxidized to CO2 in the TCA cycle and anabolic reactions in peripheral metabolism. Flux elucidation using instationary MFA reveals that these carbons are not re-fixed by RuBisCO and are instead off-gassed as CO2. A newly discovered modality is the bifurcated topology of glycine metabolism using parts of photorespiration and the phosphoserine pathways to avoid carbon losses associated with glycine oxidation. The TCA cycle is shown to be incomplete with a bifurcated topology. Inactivity of futile cycles and alternate routes results in pathway usage and (in)dispensability predictions consistent with experimental findings. The resolved flux map is consistent with the maximization of biomass yield from fixed carbons as the cellular objective function. Flux prediction departures from the ones obtained with the core model demonstrate the importance of constructing mapping models with global coverage to reliably glean new biological insights using labeled substrates.

Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis.

BACKGROUND: Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973's fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, 13C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions. RESULTS: The Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO2 conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions. CONCLUSIONS: This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for CO2 assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.

Reversing methanogenesis to capture methane for liquid biofuel precursors.

BACKGROUND: Energy from remote methane reserves is transformative; however, unintended release of this potent greenhouse gas makes it imperative to convert methane efficiently into more readily transported biofuels. No pure microbial culture that grows on methane anaerobically has been isolated, despite that methane capture through anaerobic processes is more efficient than aerobic ones. RESULTS: Here we engineered the archaeal methanogen Methanosarcina acetivorans to grow anaerobically on methane as a pure culture and to convert methane into the biofuel precursor acetate. To capture methane, we cloned the enzyme methyl-coenzyme M reductase (Mcr) from an unculturable organism, anaerobic methanotrophic archaeal population 1 (ANME-1) from a Black Sea mat, into M. acetivorans to effectively run methanogenesis in reverse. Starting with low-density inocula, M. acetivorans cells producing ANME-1 Mcr consumed up to 9 ± 1 % of methane (corresponding to 109 ± 12 µmol of methane) after 6 weeks of anaerobic growth on methane and utilized 10 mM FeCl3 as an electron acceptor. Accordingly, increases in cell density and total protein were observed as cells grew on methane in a biofilm on solid FeCl3. When incubated on methane for 5 days, high-densities of ANME-1 Mcr-producing M. acetivorans cells consumed 15 ± 2 % methane (corresponding to 143 ± 16 µmol of methane), and produced 10.3 ± 0.8 mM acetate (corresponding to 52 ± 4 µmol of acetate). We further confirmed the growth on methane and acetate production using (13)C isotopic labeling of methane and bicarbonate coupled with nuclear magnetic resonance and gas chromatography/mass spectroscopy, as well as RNA sequencing. CONCLUSIONS: We anticipate that our metabolically-engineered strain will provide insights into how methane is cycled in the environment by Archaea as well as will possibly be utilized to convert remote sources of methane into more easily transported biofuels via acetate.

Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans.

BACKGROUND: Methanosarcina acetivorans is a model archaeon with renewed interest due to its unique reversible methane production pathways. However, the mechanism and relevant pathways implicated in (co)utilizing novel carbon substrates in this organism are still not fully understood. This paper provides a comprehensive inventory of thermodynamically feasible routes for anaerobic methane oxidation, co-reactant utilization, and maximum carbon yields of major biofuel candidates by M. acetivorans. RESULTS: Here, an updated genome-scale metabolic model of M. acetivorans is introduced (iMAC868 containing 868 genes, 845 reactions, and 718 metabolites) by integrating information from two previously reconstructed metabolic models (i.e., iVS941 and iMB745), modifying 17 reactions, adding 24 new reactions, and revising 64 gene-protein-reaction associations based on newly available information. The new model establishes improved predictions of growth yields on native substrates and is capable of correctly predicting the knockout outcomes for 27 out of 28 gene deletion mutants. By tracing a bifurcated electron flow mechanism, the iMAC868 model predicts thermodynamically feasible (co)utilization pathway of methane and bicarbonate using various terminal electron acceptors through the reversal of the aceticlastic pathway. CONCLUSIONS: This effort paves the way in informing the search for thermodynamically feasible ways of (co)utilizing novel carbon substrates in the domain Archaea.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations.

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

13C metabolic flux analysis at a genome-scale.

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Inferred ranges for 81% of the reactions in the genome-scale metabolic (GSM) model varied less than one-tenth of the basis glucose uptake rate (95% confidence test). This is because as many as 411 reactions in the GSM are growth coupled meaning that the single measurement of biomass formation rate locks the reaction flux values. This implies that accurate biomass formation rate and composition are critical for resolving metabolic fluxes away from central metabolism and suggests the importance of biomass composition (re)assessment under different genetic and environmental backgrounds. In addition, the loss of information associated with mapping fluxes from MFA on a core model to a GSM model is quantified.

Redistribution of metabolic fluxes in Chlorella protothecoides by variation of media nitrogen concentration.

In this study, the Elementary Metabolite Unit (EMU) algorithm was employed to calculate intracellular fluxes for Chlorella protothecoides using previously generated growth and mass spec data. While the flux through glycolysis remained relatively constant, the pentose phosphate pathway (PPP) flux increased from 3% to 20% of the glucose uptake during nitrogen-limited growth. The TCA cycle flux decreased from 94% to 38% during nitrogen-limited growth while the flux of acetyl-CoA into lipids increased from 58% to 109% of the glucose uptake, increasing total lipid accumulation. Phosphoenolpyruvate carboxylase (PEPCase) activity was higher during nitrogen-sufficient growth. The glyoxylate shunt was found to be partially active in both cases, indicating the nutrient nature has an impact on flux distribution. It was found that the total NADPH supply within the cell remained almost constant under both conditions. In summary, algal cells substantially reorganize their metabolism during the switch from carbon-limited (nitrogen-sufficient) to nitrogen-limited (carbon-sufficient) growth.

Methane oxidation by anaerobic archaea for conversion to liquid fuels.

Given the recent increases in natural gas reserves and associated drawbacks of current gas-to-liquids technologies, the development of a bioconversion process to directly convert methane to liquid fuels would generate considerable industrial interest. Several clades of anaerobic methanotrophic archaea (ANME) are capable of performing anaerobic oxidation of methane (AOM). AOM carried out by ANME offers carbon efficiency advantages over aerobic oxidation by conserving the entire carbon flux without losing one out of three carbon atoms to carbon dioxide. This review highlights the recent advances in understanding the key enzymes involved in AOM (i.e., methyl-coenzyme M reductase), the ecological niches of a number of ANME, the putative metabolic pathways for AOM, and the syntrophic consortia that they typically form.