A Data Characterization Framework for Material Flow Analysis

The validity of material flow analyses (MFAs) depends on the available information base, that is, the quality and quantity of available data. MFA data are cross‐disciplinary, can have varying formats and qualities, and originate from heterogeneous sources, such as official statistics, scientific models, or expert estimations. Statistical methods for data evaluation are most often inadequate, because MFA data are typically isolated values rather than extensive data sets. In consideration of the properties of MFA data, a data characterization framework for MFA is presented. It consists of an MFA data terminology, a data characterization matrix, and a procedure for database analysis. The framework facilitates systematic data characterization by cell‐level tagging of data with data attributes. Data attributes represent data characteristics and metainformation regarding statistical properties, meaning, origination, and application of the data. The data characterization framework is illustrated in a case study of a national phosphorus budget. This work furthers understanding of the information basis of material flow systems, promotes the transparent documentation and precise communication of MFA input data, and can be the foundation for better data interpretation and comprehensive data quality evaluation.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

Automated Bayesian model development for frequency detection in biological time series

Background

The ability to perform quantitative studies using isotope tracers and metabolic flux analysis (MFA) is critical for detecting pathway bottlenecks and elucidating network regulation in biological systems, especially those that have been engineered to alter their native metabolic capacities. Mathematically, MFA models are traditionally formulated using separate state variables for reaction fluxes and isotopomer abundances. Analysis of isotope labeling experiments using this set of variables results in a non-convex optimization problem that suffers from both implementation complexity and convergence problems.

Results

This article addresses the mathematical and computational formulation of ¹³C MFA models using a new set of variables referred to as fluxomers. These composite variables combine both fluxes and isotopomer abundances, which results in a simply-posed formulation and an improved error model that is insensitive to isotopomer measurement normalization. A powerful fluxomer iterative algorithm (FIA) is developed and applied to solve the MFA optimization problem. For moderate-sized networks, the algorithm is shown to outperform the commonly used 13CFLUX cumomer-based algorithm and the more recently introduced OpenFLUX software that relies upon an elementary metabolite unit (EMU) network decomposition, both in terms of convergence time and output variability.

Conclusions

Substantial improvements in convergence time and statistical quality of results can be achieved by applying fluxomer variables and the FIA algorithm to compute best-fit solutions to MFA models. We expect that the fluxomer formulation will provide a more suitable basis for future algorithms that analyze very large scale networks and design optimal isotope labeling experiments.     

Tandem mass spectrometry: a novel approach for metabolic flux analysis

The goal of metabolic flux analysis (MFA) is the accurate estimation of intracellular fluxes in metabolic networks. Here, we introduce a new method for MFA based on tandem mass spectrometry (MS) and stable-isotope tracer experiments. We demonstrate that tandem MS provides more labeling information than can be obtained from traditional full scan MS analysis and allows estimation of fluxes with better precision. We present a modeling framework that takes full advantage of the additional labeling information obtained from tandem MS for MFA. We show that tandem MS data can be computed for any network model, any compound and any tandem MS fragmentation using linear mapping of isotopomers. The inherent advantages of tandem MS were illustrated in two network models using simulated and literature data. Application of tandem MS increased the observability of the models and improved the precision of estimated fluxes by 2- to 5-fold compared to traditional MS analysis.     

Movable finite automata (MFA) models for biological systems. II: Protein biosynthesis

A recently introduced class of models, the Movable Finite Automata (MFA) models, are used for simulating the elongation of the polypeptide chain in protein biosynthesis. The results of these simulations, all carried out on a microcomputer, are given.     

Predictions of methane emission levels and categories based on milk fatty acid profiles from dairy cows

Milk fatty acid (MFA) have already been used to model methane (CH₄) emissions from dairy cows. However, the data sets used to develop these models covered limited variation in dietary conditions, reducing the robustness of the predictions. In this study, a data set containing 140 observations from nine experiments (41 Holstein cows) was used to develop models predicting CH₄ expressed as g/day, g/kg dry matter intake (DMI) and g/kg milk. The data set was divided into a training (n=112) and a test data set (n=28) for model development and validation, respectively. A generalized linear mixed model was fitted to the data using the marginal R²_(m) and the Akaike information criterion to evaluate the models. The coefficient of determination of validation (R²_(v)) for different models developed ranged between 0.18 and 0.41. Form the intake-related parameters, only inclusion of total DMI improved the prediction (R²_(v)=0.58). In addition, in an attempt to further explore the relationships between MFA and CH₄ emissions, the data set was split into three categories according to CH₄ emissions: LOW (lowest 25% CH₄ emissions); HIGH (highest 25% CH₄ emissions); and MEDIUM (50% remaining observations). An ANOVA revealed that concentrations of several MFA differed for observations in HIGH compared with observations in LOW. Furthermore, the Gini coefficient was used to describe the MFA distribution for groups of MFA in each CH₄ emission category. The relative distribution of the MFA, particularly of the odd- and branched-chain fatty acids and mono-unsaturated fatty acids of observations in category HIGH differed from those in the other categories. Finally, in an attempt to validate the potential of MFA to identify cases of high or low emissions, the observations were re-classified into HIGH, MEDIUM and LOW according to the proportion of each individual MFA. The proportion of observations correctly classified were recorded. This was done for each individual MFA and for the calculated Gini coefficients, finding that a maximum of 67% of observations were correctly classified as HIGH CH₄ (trans-12 C18:1) and a maximum of 58% of observations correctly classified as LOW CH₄ (cis-9 C17:1). Gini coefficients did not improve this classification. These results suggest that MFA are not yet reliable predictors of specific amounts of CH₄ emitted by a cow, while holding a modest potential to differentiate cases of high or low emissions.     

3D-QSAR studies on antitubercular thymidine monophosphate kinase inhibitors based on different alignment methods

Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on deoxythymidine monophosphate (dTMP) derivatives inhibiting thymidine monophosphate kinase (TMPK) in Mycobacterium tuberculosis. Molecular field analysis (MFA) models with three different alignment techniques, namely, least squares, pharmacophore based and receptor based methods were developed. Receptor based MFA model showed better results when compared with least squares and pharmacophore based models. The results help us to understand the nature of substituents required for activity and thereby provide guidelines to design novel and potent inhibitors as antitubercular agents.     

Design of EGFR kinase inhibitors: a ligand-based approach and its confirmation with structure-based studies

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 100 anilinoquinazolines, inhibiting epidermal growth factor receptor (EGFR) kinase. The studies included molecular field analysis (MFA) and receptor surface analysis (RSA). The cross-validated r2 (r2cv) values are 0.81 and 0.79 for MFA and RSA, respectively. The predictive ability of these models was validated by 28 test set molecules. The results of the best QSAR model were further compared with structure-based investigations using docking studies with the crystal structure of EGFR kinase domain. The results helped to understand the nature of substituents at the 6- and 7-positions, thereby providing new guidelines for the design of novel inhibitors.     

物质流分析研究述评

物质流分析方法近年来在循环经济和可持续发展研究领域发展迅速。阐述了物质流分析的定义及其与自然生态系统物质流的区别,着重回顾了该研究方法的发展历程,阐明了物质流分析的主要观点、理论基础、研究思路及研究框架,详细阐译和对比分析了物质流分析的六大类指标及分析方法,并在物质流分析框架的基础上,建立循环经济及可持续发展的评价指标体系,并对物质流分析指标体系和方法学的研究意义及其在环境经济学中的地位进行了客观的评价,进而指出了物质流分析方法的不足之处。对物质流分析在不同层次的应用研究也进行了充分的阐述和分析。对物质流分析今后相关领域的进一步研究予以了讨论和展望。     

Cellulose microfibril angle variation in Picea crassifolia tree rings improves climate signals on the Tibetan plateau

Microfibril angle (MFA) is an important factor in determining the mechanical properties of individual cells and wood as a whole. While some studies have described the variation of MFA within trees, little work has been done on the extent to which MFA is influenced by climate, despite it being known to respond to climatic events. Year-to-year variation in MFA and ring width was measured at high resolution by SilviScan-3^? on 30 dated Picea crassifolia trees growing in the northeastern Tibetan plateau. The climate signals registered in MFA and ring width were analyzed using dendroclimatological methods. The response function of MFA accounted for 67% of total variance, of which 60% was explained by climate elements. The response function of ring width explained 57% total variance, 37% of which was explained by climate variables. MFA significantly responded to July–August temperatures, and to precipitation in March, May and September. Over the period 1987–2009 temperatures generally increase and appeared to have a greater influence on MFA. A decrease in the strength of the relationship between MFA and ring width over the period 1987–2009 was also observed. MFA offers the potential to build robust climate proxies. The strong climate sensitivity of MFA to increasing temperature or the observed changes in the MFA–ring width relationship may contribute to resolving the “divergence problem” in temperature reconstructions. As far as we are aware, this study is the first to show a strong climate response in MFA and suggests that it might be a useful climate proxy.     

Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity.
This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.     

从系统到景观:区域物质流分析的景观取向

物质流过程是考察系统属性的重要维度。区域物质流分析在研究框架、指标体系、数据集成、管理应用等方面的发展困境,都不同程度地反映了"黑箱假设"以及"系统隐喻"等产业生态学理论的应用局限性。基于整合复杂性科学、广义进化论的生态学组织层次理论,对区域物质流分析开展理论探讨,指出应在原有的"系统"思维之外引入"景观"概念,以拓展区域物质流分析的空间与认知维度。基于"从系统到景观"的理念,将景观生态学原理引入区域物质流分析,建构区域物质流分析的景观取向,并从空间结构与认知图式两个方面对这一取向的核心涵义做以解读。结合区域物质流分析的最新研究案例,从多尺度MFA的综合研究框架、物质流动过程的时空集成研究、物质流动过程的空间行为管理等几个方面,对区域物质流分析的景观取向做了进一步探讨。     

Parallel labeling experiments with [U-(13)C]glucose validate E. coli metabolic network model for (13)C metabolic flux analysis

(13)C-metabolic flux analysis (MFA) is a widely used method for measuring intracellular metabolic fluxes in living cells. (13)C MFA relies on several key assumptions: (1) the assumed metabolic network model is complete, in that it accounts for all significant enzymatic and transport reactions; (2) (13)C-labeling measurements are accurate and precise; and (3) enzymes and transporters do not discriminate between (12)C- and (13)C-labeled metabolites. In this study, we tested these inherent assumptions of (13)C MFA for wild-type E. coli by parallel labeling experiments with [U-(13)C]glucose as tracer. Cells were grown in six parallel cultures in custom-constructed mini-bioreactors, starting from the same inoculum, on medium containing different mixtures of natural glucose and fully labeled [U-(13)C]glucose, ranging from 0% to 100% [U-(13)C]glucose. Macroscopic growth characteristics of E. coli showed no observable kinetic isotope effect. The cells grew equally well on natural glucose, 100% [U-(13)C]glucose, and mixtures thereof. (13)C MFA was then used to determine intracellular metabolic fluxes for several metabolic network models: an initial network model from literature; and extended network models that accounted for potential dilution effects of isotopic labeling. The initial network model did not give statistically acceptable fits and produced inconsistent flux results for the parallel labeling experiments. In contrast, an extended network model that accounted for dilution of intracellular CO(2) by exchange with extracellular CO(2) produced statistically acceptable fits, and the estimated metabolic fluxes were consistent for the parallel cultures. This study illustrates the importance of model validation for (13)C MFA. We show that an incomplete network model can produce statistically unacceptable fits, as determined by a chi-square test for goodness-of-fit, and return biased metabolic fluxes. The validated metabolic network model for E. coli from this study can be used in future investigations for unbiased metabolic flux measurements.     

城市物质流分析框架及其指标体系构建

借鉴国内外物质流分析的研究成果,结合我国城市物质代谢特点,建立了城市物质流分析的框架及指标体系。该框架以城市社会经济系统物质通量的变化为核心,增加了对城市社会经济系统可持续能力的考察以及对城市和区域循环经济贡献的关注。论文识别了城市物质流分析中系统边界界定等关键问题,并提出了解决方法;指标体系在借鉴国家层面物质流分析指标体系的基础上,注重对城市经济系统内部循环及不同城市经济系统间的物质循环的考察,增加了再生资源输入量、内部资源回收量、可回收废物输出量、新鲜水输入量、中水回用量等指标,用于表征城市可持续发展的能力及实践成果。     

Dissecting metabolic flux in C<Subscript>4</Subscript> plants: experimental and theoretical approaches

C₄ photosynthesis is the carbon fixation pathway in specific plant species, so called C₄ plants including maize, sorghum and sugarcane. It is characterized by the carboxylation reaction that forms four-carbon (C₄) molecules, which are then used to transport CO₂ to the proximity of RubisCO in the bundle sheath cells. Since C₄ photosynthesis confers high photosynthetic as well as water and nitrogen use efficiency on plants, worldwide efforts have been made to understand the mechanisms of C₄ photosynthesis and to properly introduce the pathway into C₃ crops. Metabolic flux analysis (MFA) is a research field trying to analyze the metabolic pathway structure and activity (i.e., flux) in vivo. Constraint-based reconstruction and analysis tools theoretically study the distribution of metabolic flux in genome-scale network-based models. Different types of MFA and model-based analyses have been contributing to the discovery of C₄ photosynthetic pathways and to analyze its operation in C₄ plant species. This article reviews the studies to dissect the operation of C₄ photosynthesis and adjacent pathways, from the pioneer studies using radioisotope-based MFA to the recent stable isotope-based MFA and the model-based approaches. These studies indicate complex interconnections among metabolic pathways and the importance of the integration of experimental and theoretical approaches. Perspectives on the integrative approach and major obstacles are also discussed.