Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis

Anaerobic digestion is a key biological process for renewable energy, yet the mechanistic knowledge on its hidden microbial dynamics is still limited. The present work charted the interaction network in the anaerobic digestion microbiome via the full characterization of pairwise interactions and the associated metabolite exchanges. To this goal, a novel collection of 836 genome-scale metabolic models was built to represent the functional capabilities of bacteria and archaea species derived from genome-centric metagenomics. Dominant microbes were shown to prefer mutualistic, parasitic and commensalistic interactions over neutralism, amensalism and competition, and are more likely to behave as metabolite importers and profiteers of the coexistence. Additionally, external hydrogen injection positively influences microbiome dynamics by promoting commensalism over amensalism. Finally, exchanges of glucogenic amino acids were shown to overcome auxotrophies caused by an incomplete tricarboxylic acid cycle. Our novel strategy predicted the most favourable growth conditions for the microbes, overall suggesting strategies to increasing the biogas production efficiency. In principle, this approach could also be applied to microbial populations of biomedical importance, such as the gut microbiome, to allow a broad inspection of the microbial interplays.     

Application of dynamic metabolic flux analysis for process modeling: Robust flux estimation with regularization,confidence bounds,and selection of elementary modes

In macroscopic dynamic models of fermentation processes, elementary modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followed by an estimation of their reaction rates and of the associated confidence bounds. In this paper, we present a method for the computation of reaction rates of cellular reactions and EM as well as an algorithm for the selection of EM for process modeling. The method is based on the dynamic metabolic flux analysis (DMFA) proposed by Leighty and Antoniewicz (2011, Metab Eng, 13(6), 745–755) with additional constraints, regularization and analysis of uncertainty. Instead of using estimated uptake or secretion rates, concentration measurements are used directly to avoid an amplification of measurement errors by numerical differentiation. It is shown that the regularized DMFA for EM method is significantly more robust against measurement noise than methods using estimated rates. The confidence intervals for the estimated reaction rates are obtained by bootstrapping. For the selection of a set of EM for a given st oichiometric model, the DMFA for EM method is combined with a multiobjective genetic algorithm. The method is applied to real data from a CHO fed-batch process. From measurements of six fed-batch experiments, 10 EM were identified as the smallest subset of EM based upon which the data can be described sufficiently accurately by a dynamic model. The estimated EM reaction rates and their confidence intervals at different process conditions provide useful information for the kinetic modeling and subsequent process optimization.     

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol⁻¹, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.     

Modulation of nutrient precursors for controlling metabolic inhibitors by genome-scale flux balance analysis

Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by-products. Generic CHO metabolic model was tailored and integrated with CHO fed-batch metabolomic data to obtain a cell line- and process-specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n-acetylputrescine—all of which were previously reported to generate from endogenous cell metabolism—by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed-batch feeding conditions one against another, from which the results were validated via a dose-dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by-products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors.     

Dynamic flux balance analysis of biomass and lipid production by Antarctic thraustochytrid Oblongichytrium sp. RT2316-13

Production of biomass and lipids in batch cultures of the Antarctic thraustochytrid Oblongichytrium sp. RT2316-13, is reported. The microorganism proved capable of producing nearly 67% docosahexaenoic acid (DHA) and 15% eicosapentaenoic acid (EPA) in its total lipid fraction. Biomass with a maximum total lipid content of 33.5% (wt/wt) could be produced at 15°C in batch culture using a medium containing glucose (20 g/L), yeast extract (10.5 g/L), and other minor components. A lower culture temperature (5°C) reduced biomass and lipid productivities compared to culture at 15°C, but enhanced the DHA and EPA content of the lipids by 6.4- and 3.3-fold, respectively. Both a simple minimally structured mathematical model and a more complex genome-scale metabolic model (GEM) allowed the fermentation profiles in batch cultures to be satisfactorily simulated, but the GEM provided much greater insight in the biochemical and physiological phenomena underlying the observed behavior. Unlike the simpler model, the GEM could be interrogated for the possible effects of various external factors such as oxygen supply, on the expected outcomes. In silico predictions of oxygen effects were consistent with literature observations for DHA producing thraustochytrids.     

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.     

Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis

Flux balance analysis (FBA) has been widely used in calculating steady‐state flux distributions that provide important information for metabolic engineering. Several thermodynamics‐based methods, for example, quantitative assignment of reaction directionality and energy balance analysis have been developed to improve the prediction accuracy of FBA. However, these methods can only generate a thermodynamically feasible range, rather than the most thermodynamically favorable solution. We therefore developed a novel optimization method termed as thermodynamic optimum searching (TOS) to calculate the thermodynamically optimal solution, based on the second law of thermodynamics, the minimum magnitude of the Gibbs free energy change and the maximum entropy production principle (MEPP). Then, TOS was applied to five physiological conditions of Escherichia coli to evaluate its effectiveness. The resulting prediction accuracy was found significantly improved (10.7–48.5%) by comparing with the ¹³C‐fluxome data, indicating that TOS can be considered an advanced calculation and prediction tool in metabolic engineering. Biotechnol. Bioeng. 2013; 110: 914–923. © 2012 Wiley Periodicals, Inc.     

基于约束的基因组规模代谢网络模型构建方法研究进展

基因组规模代谢网络模型(Genome-scale metabolic network model,GSMM)正成为细胞代谢特性研究的重要工具,经过多年发展相关理论方法取得了诸多进展.近年来,在基础GSMM模型基础上,通过整合基因组、转录组、蛋白组和热力学数据,实现基于各种约束的GSMM构建,在基因靶点识别、系统代谢工程...     

In-depth characterization of genome-scale network reconstructions for the in vitro synthesis in cell-free systems

Cell-free systems containing multiple enzymes are becoming an increasingly interesting tool for one-pot syntheses of biochemical compounds. To extensively explore the enormous wealth of enzymes in the biological space, we present methods for assembling and curing data from databases to apply them for the prediction of pathway candidates for directed enzymatic synthesis. We use Kyoto Encyclopedia of Genes and Genomes to establish single organism models and a pan-organism model that is combining the available data from all organisms listed there. We introduce a filtering scheme to remove data that are not suitable, for example, generic metabolites and general reactions. In addition, a valid stoichiometry of reactions is required for acceptance. The networks created are analyzed by graph theoretical methods to identify a set of metabolites that are potentially reachable from a defined set of starting metabolites. Thus, metabolites not connected to such starting metabolites cannot be produced unless new starting metabolites or reactions are introduced. The network models also comprise stoichiometric and thermodynamic data that allow the definition of constraints to identify potential pathways. The resulting data can be directly applied using existing or future pathway finding tools.     

The net carbon flux due to deforestation and forest re-growth in the Brazilian Amazon: analysis using a process-based model

We developed a process‐based model of forest growth, carbon cycling and land‐cover dynamics named CARLUC (for CARbon and Land‐Use Change) to estimate the size of terrestrial carbon pools in terra firme (nonflooded) forests across the Brazilian Legal Amazon and the net flux of carbon resulting from forest disturbance and forest recovery from disturbance. Our goal in building the model was to construct a relatively simple ecosystem model that would respond to soil and climatic heterogeneity that allows us to study the impact of Amazonian deforestation, selective logging and accidental fire on the global carbon cycle. This paper focuses on the net flux caused by deforestation and forest re‐growth over the period from 1970 to 1998. We calculate that the net flux to the atmosphere during this period reached a maximum of ～0.35 PgC yr^?1 (1 PgC= 1 × 10¹⁵ gC) in 1990, with a cumulative release of ～7 PgC from 1970 to 1998. The net flux is higher than predicted by an earlier study ( Houghton et al., 2000 ) by a total of 1 PgC over the period 1989–1998 mainly because CARLUC predicts relatively high mature forest carbon storage compared with the datasets used in the earlier study. Incorporating the dynamics of litter and soil carbon pools into the model increases the cumulative net flux by～1 PgC from 1970 to 1998, while different assumptions about land‐cover dynamics only caused small changes. The uncertainty of the net flux, calculated with a Monte‐Carlo approach, is roughly 35% of the mean value (1 SD).     

Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.     

Mitigating the greenhouse gas balance of ruminant production systems through carbon sequestration in grasslands

Soil carbon sequestration (enhanced sinks) is the mechanism responsible for most of the greenhouse gas (GHG) mitigation potential in the agriculture sector. Carbon sequestration in grasslands can be determined directly by measuring changes in soil organic carbon (SOC) stocks and indirectly by measuring the net balance of C fluxes. A literature search shows that grassland C sequestration reaches on average 5 ± 30 g C/m2 per year according to inventories of SOC stocks and -231 and 77 g C/m2 per year for drained organic and mineral soils, respectively, according to C flux balance. Off-site C sequestration occurs whenever more manure C is produced by than returned to a grassland plot. The sum of on- and off-site C sequestration reaches 129, 98 and 71 g C/m2 per year for grazed, cut and mixed European grasslands on mineral soils, respectively, however with high uncertainty. A range of management practices reduce C losses and increase C sequestration: (i) avoiding soil tillage and the conversion of grasslands to arable use, (ii) moderately intensifying nutrient-poor permanent grasslands, (iii) using light grazing instead of heavy grazing, (iv) increasing the duration of grass leys; (v) converting grass leys to grass-legume mixtures or to permanent grasslands. With nine European sites, direct emissions of N2O from soil and of CH4 from enteric fermentation at grazing, expressed in CO2 equivalents, compensated 10% and 34% of the on-site grassland C sequestration, respectively. Digestion inside the barn of the harvested herbage leads to further emissions of CH4 and N2O by the production systems, which were estimated at 130 g CO2 equivalents/m2 per year. The net balance of on- and off-site C sequestration, CH4 and N2O emissions reached 38 g CO2 equivalents/m2 per year, indicating a non-significant net sink activity. This net balance was, however, negative for intensively managed cut sites indicating a source to the atmosphere. In conclusion, this review confirms that grassland C sequestration has a strong potential to partly mitigate the GHG balance of ruminant production systems. However, as soil C sequestration is both reversible and vulnerable to disturbance, biodiversity loss and climate change, CH4 and N2O emissions from the livestock sector need to be reduced and current SOC stocks preserved.     

Development of a mathematical model for evaluating the dynamics of normal and apoptotic Chinese hamster ovary cells

A metabolic flux based methodology was developed for modeling the metabolism of a Chinese hamster ovary cell line. The elimination of insignificant fluxes resulted in a simplified metabolic network which was the basis for modeling the significant metabolites. Employing kinetic rate expressions for growing and non-growing subpopulations, a logistic model was developed for cell growth and dynamic models were formulated to describe culture composition and monoclonal antibody (MAb) secretion. The model was validated for a range of nutrient concentrations. Good agreement was obtained between model predictions and experimental data. The ultimate goal of this study is to establish a comprehensive dynamic model which may be used for model-based optimization of the cell culture for MAb production in both batch and fed-batch systems.     

A method for accounting for maintenance costs in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions

Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade‐off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper‐osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper‐osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.     

A kinetic model describes metabolic response to perturbations and distribution of flux control in the benzenoid network of Petunia hybrida

In recent years there has been much interest in the genetic enhancement of plant metabolism; however, attempts at genetic modification are often unsuccessful due to an incomplete understanding of network dynamics and their regulatory properties. Kinetic modeling of plant metabolic networks can provide predictive information on network control and response to genetic perturbations, which allow estimation of flux at any concentration of intermediate or enzyme in the system. In this research, a kinetic model of the benzenoid network was developed to simulate whole network responses to different concentrations of supplied phenylalanine (Phe) in petunia flowers and capture flux redistributions caused by genetic manipulations. Kinetic parameters were obtained by network decomposition and non‐linear least squares optimization of data from petunia flowers supplied with either 75 or 150 mm ²H₅‐Phe. A single set of kinetic parameters simultaneously accommodated labeling and pool size data obtained for all endogenous and emitted volatiles at the two concentrations of supplied ²H₅‐Phe. The generated kinetic model was validated using flowers from transgenic petunia plants in which benzyl CoA:benzyl alcohol/phenylethanol benzoyltransferase (BPBT) was down‐regulated via RNAi. The determined in vivo kinetic parameters were used for metabolic control analysis, in which flux control coefficients were calculated for fluxes around the key branch point at Phe and revealed that phenylacetaldehyde synthase activity is the primary controlling factor for the phenylacetaldehyde branch of the benzenoid network. In contrast, control of flux through the β‐oxidative and non‐β‐oxidative pathways is highly distributed.     

Terrestrial carbon balance in a drier world: the effects of water availability in southwestern North America

Global modeling efforts indicate semiarid regions dominate the increasing trend and interannual variation of net CO₂ exchange with the atmosphere, mainly driven by water availability. Many semiarid regions are expected to undergo climatic drying, but the impacts on net CO₂ exchange are poorly understood due to limited semiarid flux observations. Here we evaluated 121 site‐years of annual eddy covariance measurements of net and gross CO₂ exchange (photosynthesis and respiration), precipitation, and evapotranspiration (ET) in 21 semiarid North American ecosystems with an observed range of 100 – 1000 mm in annual precipitation and records of 4–9 years each. In addition to evaluating spatial relationships among CO₂ and water fluxes across sites, we separately quantified site‐level temporal relationships, representing sensitivity to interannual variation. Across the climatic and ecological gradient, photosynthesis showed a saturating spatial relationship to precipitation, whereas the photosynthesis–ET relationship was linear, suggesting ET was a better proxy for water available to drive CO₂ exchanges after hydrologic losses. Both photosynthesis and respiration showed similar site‐level sensitivity to interannual changes in ET among the 21 ecosystems. Furthermore, these temporal relationships were not different from the spatial relationships of long‐term mean CO₂ exchanges with climatic ET. Consequently, a hypothetical 100‐mm change in ET, whether short term or long term, was predicted to alter net ecosystem production (NEP) by 64 gCm^?2 yr^?1. Most of the unexplained NEP variability was related to persistent, site‐specific function, suggesting prioritization of research on slow‐changing controls. Common temporal and spatial sensitivity to water availability increases our confidence that site‐level responses to interannual weather can be extrapolated for prediction of CO₂ exchanges over decadal and longer timescales relevant to societal response to climate change.     

Simulating the effects of harvest and biofuel production on the forest system carbon balance of the Midwest,USA

Forests of the Midwestern United States are an important source of fiber for the wood and paper products industries. Scientists, land managers, and policy makers are interested in using woody biomass and/or harvest residue for biofuel feedstocks. However, the effects of increased biomass removal for biofuel production on forest production and forest system carbon balance remain uncertain. We modeled the carbon (C) cycle of the forest system by dividing it into two distinct components: (1) biological (net ecosystem production, net primary production, autotrophic and heterotrophic respiration, vegetation, and soil C content) and (2) industrial (harvest operations and transportation, production, use, and disposal of major wood products including biofuel and associated C emissions). We modeled available woody biomass feedstock and whole‐system carbon balance of 220 000 km² of temperate forests in the Upper Midwest, USA by coupling an ecosystem process model to a collection of greenhouse gas life‐cycle inventory models and simulating seven forest harvest scenarios in the biological ecosystem and three biofuel production scenarios in the industrial system for 50 years. The forest system was a carbon sink (118 g C m^?2 yr^?1) under current management practices and forest product production rates. However, the system became a C source when harvest area was doubled and biofuel production replaced traditional forest products. Total carbon stores in the vegetation and soil increased by 5–10% under low‐intensity management scenarios and current management, but decreased up to 3% under high‐intensity harvest regimes. Increasing harvest residue removal during harvest had more modest effects on forest system C balance and total biomass removal than increasing the rate of clear‐cut harvests or area harvested. Net forest system C balance was significantly, and negatively correlated (R² = 0.67) with biomass harvested, illustrating the trade‐offs between increased C uptake by forests and utilization of woody biomass for biofuel feedstock.