Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.Plants are of vital significance as a source of food (Grusak and DellaPenna, 1999; Rogalski and Carrer, 2011), feed (Lu et al., 2011), energy (Tilman et al., 2006; Parmar et al., 2011), and feedstocks for the chemical industry (Metzger and Bornscheuer, 2006; Kinghorn et al., 2011). Given the close connection between plant metabolism and the usability of plant products, there is a growing interest in understanding and predicting the behavior and regulation of plant metabolic processes. In order to increase crop quality and yield, there is a need for methods guiding the rational redesign of the plant metabolic network (Schwender, 2009).Mathematical modeling of plant metabolism offers new approaches to understand, predict, and modify complex plant metabolic processes. In plant research, the issue of metabolic modeling is constantly gaining attention, and different modeling approaches applied to plant metabolism exist, ranging from highly detailed quantitative to less complex qualitative approaches (for review, see Giersch, 2000; Morgan and Rhodes, 2002; Poolman et al., 2004; Rios-Estepa and Lange, 2007).A widely used modeling approach is flux balance analysis (FBA), which allows the prediction of metabolic capabilities and steady-state fluxes under different environmental and genetic backgrounds using (non)linear optimization (Orth et al., 2010). Assuming steady-state conditions, FBA has the advantage of not requiring the knowledge of kinetic parameters and, therefore, can be applied to model detailed, large-scale systems. In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays; Dal’Molin et al., 2010; Saha et al., 2011), barley (Hordeum vulgare; Grafahrend-Belau et al., 2009b; Melkus et al., 2011; Rolletschek et al., 2011), rice (Oryza sativa; Lakshmanan et al., 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al., 2009; de Oliveira Dal’Molin et al., 2010; Radrich et al., 2010; Williams et al., 2010; Mintz-Oron et al., 2012; Cheung et al., 2013), and rapeseed (Brassica napus; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011), as well as algae (Boyle and Morgan, 2009; Cogne et al., 2011; Dal’Molin et al., 2011) and photoautotrophic bacteria (Knoop et al., 2010; Montagud et al., 2010; Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies (Dal’Molin et al., 2010; Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al., 2009b; Dal’Molin et al., 2010; Melkus et al., 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al., 2009; Knoop et al., 2010; Rolletschek et al., 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al., 2010; Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.Given that many plant metabolic functions are based on interactions between different subcellular compartments, cell types, tissues, and organs, the reconstruction of organ-specific models and the integration of these models into interacting multiorgan and/or whole-plant models is a prerequisite to get insight into complex plant metabolic processes organized on a whole-plant scale (e.g. source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009; Knoop et al., 2010; Montagud et al., 2010; Cogne et al., 2011; Dal’Molin et al., 2011), one tissue or one organ (Grafahrend-Belau et al., 2009b; Hay and Schwender, 2011a, 2011b; Pilalis et al., 2011; Mintz-Oron et al., 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis (Dal’Molin et al., 2010). So far, no model representing metabolism at the whole-plant scale exists.Considering whole-plant metabolism raises the problem of taking into account temporal and environmental changes in metabolism during plant development and growth. Although classical static FBA is unable to predict the dynamics of metabolic processes, as the network analysis is based on steady-state solutions, time-dependent processes can be taken into account by extending the classical static FBA to a dynamic flux balance analysis (dFBA), as proposed by Mahadevan et al. (2002). The static (SOA) and dynamic optimization approaches introduced in this work provide a framework for analyzing the transience of metabolism by integrating kinetic expressions to dynamically constrain exchange fluxes. Due to the requirement of knowing or estimating a large number of kinetic parameters, so far dFBA has only been applied to a plant metabolic model once, to study the photosynthetic metabolism in the chloroplasts of C3 plants by a simplified model of five biochemical reactions (Luo et al., 2009). Integrating a dynamic model into a static FBA model is an alternative approach to perform dFBA.In this study, a multiscale metabolic modeling (MMM) approach was applied with the aim of achieving a spatiotemporal resolution of cereal crop plant metabolism. To provide a framework for the in silico analysis of the metabolic dynamics of barley on a whole-plant scale, the MMM approach integrates a static multiorgan FBA model and a dynamic whole-plant multiscale functional plant model (FPM) to perform dFBA. The performance of the novel whole-plant MMM approach was tested by studying source-sink interactions during the seed developmental phase of barley plants.     

L-色氨酸生物合成的代谢流量分析

建立了谷氨酸棒杆菌合成L色氨酸(LTry)的代谢流量平衡模型,应用该模型计算出发酵中后期的代谢流分布并通过MATLAB软件线性规划得到Try理想代谢流分布。结果表明75.15%的碳架进入糖酵解,24.85%的碳架进入HMP途径;但与理想代谢流相比,应从遗传改造和发酵控制方面降低 TCA循环的代谢流,减少副产氨基酸的生成,摸索最适的溶氧控制对提高Try产率至关重要。     

杂交瘤细胞的代谢流量分析

应用代谢流量平衡模型定量分析了杂交瘤细胞的代谢流量分布。结果表明,在连续培养的杂交瘤细胞中,当葡萄糖和谷氨酰胺的流加浓度分别为13.8和2.6 mmol·L^-1时,86.2%的葡萄糖通过糖酵解生成乳酸,7.5%的生成脂类,进入TCA循环的仅占0.83%;谷氨酰胺中的氮有3%用于核酸的合成,54.5%生成氨,另有38.2%生成非必需氨基酸,碳骨架61.6%生成非必需氨基酸,34.1%进入TCA循环。     

L-色氨酸生物合成的代谢流量分析

建立了谷氨酸棒杆菌合成L-色氨酸(L-Try)的代谢流量平衡模型,应用该模型计算出发酵中后期的代谢流分布并通过MATLAB软件线性规划得到Try理想代谢流分布。结果表明75．15％的碳架进入糖酵解,24．85％的碳架进入HMP途径;但与理想代谢流相比,应从遗传改造和发酵控制方面降低TCA循环的代谢流,减少副产氨基酸的生成,摸索最适的溶氧控制对提高Try产率至关重要。     

Analysis of Metabolic Subnetworks by Flux Cone Projection

Background

Analysis of elementary modes (EMs) is proven to be a powerful constraint-based method in the study of metabolic networks. However, enumeration of EMs is a hard computational task. Additionally, due to their large number, EMs cannot be simply used as an input for subsequent analysis. One possibility is to limit the analysis to a subset of interesting reactions. However, analysing an isolated subnetwork can result in finding incorrect EMs which are not part of any steady-state flux distribution of the original network. The ideal set to describe the reaction activity in a subnetwork would be the set of all EMs projected to the reactions of interest. Recently, the concept of "elementary flux patterns" (EFPs) has been proposed. Each EFP is a subset of the support (i.e., non-zero elements) of at least one EM.

Results

We introduce the concept of ProCEMs (Projected Cone Elementary Modes). The ProCEM set can be computed by projecting the flux cone onto a lower-dimensional subspace and enumerating the extreme rays of the projected cone. In contrast to EFPs, ProCEMs are not merely a set of reactions, but projected EMs. We additionally prove that the set of EFPs is included in the set of ProCEM supports. Finally, ProCEMs and EFPs are compared for studying substructures of biological networks.

Conclusions

We introduce the concept of ProCEMs and recommend its use for the analysis of substructures of metabolic networks for which the set of EMs cannot be computed.     

鸟苷发酵过程代谢流迁移的分析

以典型的代谢控制发酵产品鸟苷为例说明了一种基于过程参数的相关分析来研究发酵过程中代谢流迁移的方法。通过对发酵过程多参数的相关性分析,结合生物合成代谢途径、氨基酸和有机酸积累的分析,确认了发酵过程代谢流向EMP途径的迁移,认为造成这种代谢流迁移的原因可能是过程铵离子积累。在此基础上,通过对过程参数实时检测分析和及时调整EMP和HMP代谢通量使产率提高了35％。      

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.     

大肠杆菌L-色氨酸合成的代谢流分析简

目的：从代谢流的层面研究育种过程中基因操作对色氨酸积累的影响,为色氨酸菌种选育的设计思路提供理论指导和验证。方法：根据实验菌株的代谢特点构建￡一色氨酸代谢网络图,对出发菌株TRTH0709,及其重组菌株TRTH1013、TRTH1105和TRTH1107在30L发酵罐中进行分批流加发酵试验,在发酵进入稳定期后的26．28h,分别检测主要胞外代谢物的浓度并计算变化速率。结果和结论：得到了各菌株在拟稳态下的代谢流分布图。转酮酶基因（tktA）和磷酸烯醇式丙酮酸合成酶基因（ppsA）过表达能显著影响中心代谢途径,使代谢流向有利于色氨酸合成的方向改变,贮碳因子基因（csrA）敲除的影响较小,但在tktA和ppsA过表达质粒存在的情况下对色氨酸合成的代谢流有明显的促进作用。进一步的菌种改造仍有待进行,葡萄糖转运系统的替代和三羧酸循环的减弱是主要方向。     

L-精氨酸生物合成的代谢流量分析

建立并完善了谷氨酸棒杆菌GWY020及其2个逐步叠加不同遗传标记的突变株HUI821和GUI089合成L-精氨酸的中心代谢网络。分别测定了它们在特定培养时段(50 h~52 h)L-精氨酸等代谢物的胞外浓度, 由此计算这一时段这些代谢物在发酵液中积累(或消耗)的速率, 分别作出这3株菌在拟稳态下的代谢流量分布图, 进而研究育种过程中不同遗传标记的叠加对代谢网络中L-精氨酸合成流量分布的影响。结果表明遗传标记的引入使流量分配发生了重大变化, 节点处的流量分配朝着有利于L-精氨酸合成的方向改变。从代谢流量分析角度上, 证明结构类似物抗性和敏感性突变是代谢流导向和设计育种的有效手段, 代谢流量分析将成为设计育种的提供新思路。     

L-精氨酸生物合成的代谢流量分析

建立并完善了谷氨酸棒杆菌GWY020及其2个逐步叠加不同遗传标记的突变株HUI821和GUI089合成L-精氨酸的中心代谢网络.分别测定了它们在特定培养时段(50 h～52 h)L-精氨酸等代谢物的胞外浓度,由此计算这一时段这些代谢物在发酵液中积累(或消耗)的速率,分别作出这3株菌在拟稳态下的代谢流量分布图,进而研究育种过程中不同遗传标记的叠加对代谢网络中L-精氨酸合成流量分布的影响.结果表明遗传标记的引入使流量分配发生了重大变化,节点处的流量分配朝着有利于L-精氨酸合成的方向改变.从代谢流量分析角度上,证明结构类似物抗性和敏感性突变是代谢流导向和设计育种的有效手段,代谢流量分析将成为设计育种的提供新思路.     

L-缬氨酸合成的代谢流量分析

分别测定谷氨酸棒杆菌（Corynebacterium glutamicum）AS1-495及其3个逐个叠加不同遗传标记的突变株AA361、AAT231和AATV341在特定培养时段（26～28h）L缬氨酸等代谢物的胞外浓度,由此计算这一时段这些代谢物在发酵液中积累（或消耗）的速率,分别做出这4株菌在拟稳态下的代谢流量分布图,进而研究育种过程中不同遗传标记的叠加对代谢网络中L-缬氨酸合成流量分布的影响。结果表明遗传标记的引入使流量分配发生了重大变化,节点处的流量分配朝着有利于L缬氨酸合成的方向改变。6-磷酸葡萄糖节点处流入EMP途径和HMP途径的流量分配由17.0∶83.0变为24.3∶75.7;丙酮酸节点处流入L-缬氨酸合成途径和其他途径的流量分配由15.8∶842变为76.7∶23.3/L-缬氨酸合成的分支途径上的流量由最初的5.37增大为37.3,乳酸合成途径的流量从11.1最后降为1.16,L-缬氨酸产量由4g/L提高到24.5 g/L。代谢流量分布的变化趋势与L缬氨酸产量的变化趋势是互相吻合的。以2-噻唑丙氨酸抗性突变（2TAr）和L天冬氨酸氧肟酸盐超敏性突变（LAAHss）有效地进行代谢流遗传导向的事实,在代谢流量分析的层面上,证明结构类似物抗性突变和结构类似物超敏性突变是代谢流导向和设计育种的十分有效的手段,代谢流量分析会成为设计育种的校正方法。     

mzDB: A File Format Using Multiple Indexing Strategies for the Efficient Analysis of Large LC-MS/MS and SWATH-MS Data Sets

The analysis and management of MS data, especially those generated by data independent MS acquisition, exemplified by SWATH-MS, pose significant challenges for proteomics bioinformatics. The large size and vast amount of information inherent to these data sets need to be properly structured to enable an efficient and straightforward extraction of the signals used to identify specific target peptides. Standard XML based formats are not well suited to large MS data files, for example, those generated by SWATH-MS, and compromise high-throughput data processing and storing.We developed mzDB, an efficient file format for large MS data sets. It relies on the SQLite software library and consists of a standardized and portable server-less single-file database. An optimized 3D indexing approach is adopted, where the LC-MS coordinates (retention time and m/z), along with the precursor m/z for SWATH-MS data, are used to query the database for data extraction.In comparison with XML formats, mzDB saves ∼25% of storage space and improves access times by a factor of twofold up to even 2000-fold, depending on the particular data access. Similarly, mzDB shows also slightly to significantly lower access times in comparison with other formats like mz5. Both C++ and Java implementations, converting raw or XML formats to mzDB and providing access methods, will be released under permissive license. mzDB can be easily accessed by the SQLite C library and its drivers for all major languages, and browsed with existing dedicated GUIs. The mzDB described here can boost existing mass spectrometry data analysis pipelines, offering unprecedented performance in terms of efficiency, portability, compactness, and flexibility.The continuous improvement of mass spectrometers (1–4) and HPLC systems (5–10) and the rapidly increasing volumes of data they produce pose a real challenge to software developers who constantly have to adapt their tools to deal with different types and increasing sizes of raw files. Indeed, the file size of a single MS analysis evolved from a few MB to several GB in less than 10 years. The introduction of high throughput, high mass accuracy MS analyses in data dependent acquisitions (DDA)¹ and the adoption of Data Independent Acquisition (DIA) approaches, for example, SWATH-MS (11), were significant factors in this development. The management of these huge data files is a major issue for laboratories and raw file public repositories, which need to regularly upgrade their storage solutions and capacity.The availability of XML (eXtensible Markup Language) standard formats (12, 13) enhanced data exchange among laboratories. However, XMLs causes the inflation of raw file size by a factor of two to three times compared with their original size. Vendor files, although lighter, are proprietary formats, often not compatible with operating systems other than Microsoft Windows. They do not generally interface with many open source software tools, and do not offer a viable solution for data exchange. In addition to size inflation, other disadvantages associated with the use of XML for the representation of raw data have been previously described in the literature (14–17). These include the verbosity of language syntax, the lack of support for multidimensional chromatographic analyses, and the low performance showed during data processing. Although XML standards were originally conceived as a format for enabling data sharing in the community, they are commonly used as the input for MS data analysis. Latest software tools (18, 19) are usually only compatible with mzML files, limiting de facto the throughput of proteomic analyses.To tackle these issues, some independent laboratories developed open formats relying on binary specifications (14, 17, 20, 21), to optimize both file size and data processing performance. Similar efforts started already more than ten years ago, and, among the others, the NetCDF version 4, first described in 2004, added the support for a new data model called HDF5. Because it is particularly well suited to the representation of complex data, HDF5 was used in several scientific projects to store and efficiently access large volumes of bytes, as for the mz5 format (17). Compared with XML based formats, mz5 is much more efficient in terms of file size, memory footprint, and access time. Thus, after replacing the JCAMP text format more than 10 years ago, netCDF is nowadays a suitable alternative to XML based formats. Nonetheless, solutions for storing and indexing large amounts of data in a binary file are not limited to netCDF. For instance, it has been demonstrated that a relational model can represent raw data, as in YAFMS format (14), which is based on SQLite, a technology that allows implementing a portable, self-contained, single file database. Similarly to mz5, YAFMS is definitely more efficient in terms of file size and access times than XML.Despite their improvements, a limitation of these new binary formats relies on the lack of a multi-indexing model to represent the bi-dimensional structure of LC-MS data. The inherently 2D indexing of LC-MS data can indeed be very useful when working with LC-MS/MS acquisition files. At the state-of-the-art, three main raw data access strategies can be identified across DDA and DIA approaches:

• (1) Sequential reading of whole m/z spectra, for a systematic processing of the entire raw file. Use cases: file format conversion, peak picking, analysis of MS/MS spectra, and MS/MS peak list generation.
• (2) Systematic processing of the data contained in specific m/z windows, across the entire chromatographic gradient. Use cases: extraction of XICs on the whole chromatographic gradient and MS features detection.
• (3) Random access to a small region of the LC-MS map (a few spectra or an m/z window of consecutive spectra). Use cases: data visualization, targeted extraction of XICs on a small time range, and targeted extraction of a subset of spectra.

The adoption of a certain data access strategy depends upon the particular data analysis algorithms, which can perform signal extraction mainly by unsupervised or supervised approaches. Unsupervised approaches (18, 22–25) recognize LC-MS features on the basis of patterns like the theoretical isotope distribution, the shape of the elution peaks, etc. Conversely, supervised approaches (29–33) implement the peak picking as driven data access, using the a priori knowledge on peptide coordinates (m/z, retention time, and m/z precursor for DIA), which are provided by appropriate extraction lists given by the identification search engine or the transition lists in targeted proteomics (34). Data access overhead can vary significantly, according to the specific algorithm, data size, and length of the extraction list. In the unsupervised approach, feature detection is based first on the analysis of the full set of MS spectra and then on the grouping of the peaks detected in adjacent MS scans; thus, optimized sequential spectra access is required. In the supervised approach, peptide XICs are extracted using their a priori coordinates and therefore sequential spectra access is not a suitable solution; for instance, MS spectra shared by different peptides would be loaded multiple times leading to highly redundant data reloading. Even though sophisticated caching mechanisms can reduce the impact of this issue, they would increase memory consumption. It is thus preferable to perform a targeted access to specific MS spectra by leveraging an index in the time dimension. However, it would still be a sub-optimal solution because of redundant loads of full MS spectra, whereas only a small spectral window centered on the peptide m/z is of interest. Thus the quantification of dozens of thousands of peptides (32, 33) requires appropriate data access methods to cope with the repetitive and high load of MS data.We therefore deem that an ideal file format should show comparable efficiency regardless of the particular use case. In order to achieve this important flexibility and efficiency on any data access, we developed a new solution featuring multiple indexing strategies: the mzDB format (i.e. m/z database). As the YAFMS format, mzDB is implemented using SQLite, which is commonly adopted in several computational projects and is compatible with most programming languages. In contrast to mz5 and YAFMS formats, where each spectrum is referred by a single index entry, mzDB has an internal data structure allowing a multidimensional data indexing, and thus results in efficient queries along both time and m/z dimensions. This makes mzDB specifically suited to the processing of large-scale LC-MS/MS data. In particular, the multidimensional data-indexing model was extended for SWATH-MS data, where a third index is given by the m/z of the precursor ion, in addition to the RT and m/z of the fragment ions.In order to show its efficiency for all described data access strategies, mzDB was compared with the mzML format, which is the official XML standard, and the latest mz5 binary format, which has already been compared with many existing file formats (17). Results show that mzDB outperforms other formats on most comparisons, except in sequential reading benchmarks where mz5 and mzDB are comparable. mzDB access performance, portability, and compactness, as well as its compliance to the PSI controlled vocabulary make it complementary to existing solutions for both the storage and exchange of mass spectrometry data and will eventually address the issues related to data access overhead during their processing. mzDB can therefore enhance existing mass spectrometry data analysis pipelines, offering unprecedented performance and therefore possibilities.     

L-缬氨酸产生菌的选育及其代谢流分析

以谷氨酸棒杆菌(Corynebacterium glutamicum)AS1.495(Leu^-)为出发菌株,通过多次亚硝基胍(NTG)诱变,给AS1．495(Leu^-)依次叠加L-AAH^as,2-TA^r,Vd^-的遗传标记,得到突变株AATV341(Leu^-,L-AAH^as,2-TA^r,Vd^-),可在8％的葡萄糖培养基积累L-缬氨酸24．5g／L,比出发菌株提高了5．13倍。同时运用代谢流量分析理论,测定出发菌株AS1．495及其突变株AATV341在L-缬氨酸合成阶段的代谢流量,并初步进行比较和分析,发现遗传标记的引入使流量分配发生了重大变化,流量分配朝着有利于L-缬氨酸合成的方向改变。     

A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, ¹³C Metabolic Flux Analysis (¹³C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during ¹³C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of ¹³C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based ¹³C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.     

Structural Control of Metabolic Flux

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions'' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions.     

L-谷氨酰胺产生菌的选育和代谢流量分析

目的:建立并完善嗜乙酰乙酸棒杆菌YL012及其突变株LCHA0082合成L-谷氨酰胺的中心代谢网络.方法:分别测定了它们在特定培养时段(48h～50h)L-谷氨酰胺等代谢物的胞外浓度,由此计算这一时段这些代谢物在发酵液中积累(或消耗)的速率,分别作出这两株菌在拟稳态下的代谢流量分布图,进而研究诱变育种过程中不同诱变标记对代谢网络中L-谷氨酰胺合成流量分布的影响.结果:育种操作使流量分配朝着有利于L-谷氨酰胺合成的方向改变,流入谷氨酸节点的流量由29.198mmol/L·h上升到44.854mmol/L·h,提高到原来的1.5倍左右,合成L-谷氨酰胺的流量由18.138mmol/L·h上升至31.065mmol/L·h,效果明显.结论:从代谢流量分析角度上,证明诱变育种对代谢流量的改变起到明显的作用,代谢流量分析也为新的设计育种提供了思路.