Noise effect in metabolic networks

Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Large-Scale Kinetic Parameter Identification of Metabolic Network Model of <i>E. coli</i>Using PSO

In metabolic network modelling, the accuracy of kinetic parameters has become more important over the last two decades. Even a small perturbation in kinetic parameters may cause major changes in a model’s response. The focus of this study is to identify the kinetic parameters, using two distinct approaches: firstly, a One-at-a-Time Sensitivity Measure, performed on 185 kinetic parameters, which represent glycolysis, pentose phosphate, TCA cycle, gluconeogenesis, glycoxylate pathways, and acetate formation. Time profiles for sensitivity indices were calculated for each parameter. Seven kinetic parameters were found to be highly affected in the model response;secondly, particle swarm optimization was applied for kinetic parameter identification of a metabolic network model. The simulation results proved the effectiveness of the proposed method.

FunHoP:Enhanced Visualization and Analysis of Functionally Homologous Proteins in Complex Metabolic Networks

Cytoscape is often used for visualization and analysis of metabolic pathways.For example,based on KEGG data,a reader for KEGG Markup Language(KGML)is used to load files into Cytoscape.However,although multiple genes can be responsible for the same reaction,the KGMLreader KEGGScape only presents the first listed gene in a network node for a given reaction.This can lead to incorrect interpretations of the pathways.Our new method,FunHoP,shows all possible genes in each node,making the pathways more complete.FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq.Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads,and weighting the reads on gene length and ratio,a new expression value is calculated for the node as a whole.Differential expression at node level is then applied to the networks.Using prostate cancer as model,we integrate RNA-seq data from two patient cohorts with metabolism data from literature.Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically.Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.

Progress in the applications of flux analysis of metabolic networks

The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization (ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by integrating metabolic flux analysis, network topological characteristics and dynamic modeling.

Expanded flux variability analysis on metabolic network of Escherichia coli

Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

Systemic omics analysis of the hub genes, proteins, metabolites and metabolic pathways related to the hypoxia preconditioning in mice

Hypoxia preconditioning （HPC） is associated with many complicated pathophysiological and biochemical processes that integrated and regulated via molecular levels. HPC could protect cells, tissues, organs and systems from hypoxia injury, but up to date, the molecular mechanism still remained unclear. The acute and repetitive hy- poxia preconditioning model was constructed and the related parameters were observed. The high-throughput mi- croarray analysis and multiple bioinformatics were used to explore the differentially expressed genes in HPC mice brain and the related gene network, pathways and biological processes related to HPC. The 2D-DIGE coupled with MALDI-TOF/TOF-MS was performed to identify these proteins that were differentially expressed during HPC. The UPLC-HRMS based metabolomics method was utilized to explore the key endogenous metabolites and metabolic pathways related to HPC. The results showed that （1） 1175 differentially expressed genes in HPC mice brain were identified. Fourteen of these genes were the related hub genes for HPC, including Cacna2dl, Grin2a, Npylr, Mef2c, Epha4, Rxfpl, Chrm3, Pdela, Atp2b4, Glral, Idil , Fgfl, Grin2b and Cda. The change trends of all the detected genes by RT-PCR were consistent with the data of gene chips. There were 113 significant functions up- regulated and 138 significant functions down-regulated in HPC mice. （2） About 2100 proteins were revealed via the gel imaging and spot detection. 66, 45 and 70 of proteins were found to have significantly difference between the control group and three times of HPC group, the control and six times of HPC, and the three times of HPC and six times of HPC group. （3）Some endogenous metabolites such as phenylalanine, valine, proline, leucine and glu- tamine were increased, while ereatine was decreased, both in HPC brain and heart; in addition, y-aminobutyric acid was markedly decreased in brain. The sphingolipid metabolic pathways were noticed due to the low p-value and high pathway impact. Especially, the sphingolipid compound sphingomyelin, ceramide, glucosyleeramide, galactosylceramide and laetosylceramide were mapping in this metabolic pathway. Interestingly, these sphingolipid metabolites with olefinic bond in the long fatty chain were up-regulated, while those sphingolipids without olefinic bond were down-regulated. The functions of these differentially expressed genes mainly involved the cellular proces- ses including MAPK pathway, ion transport, neurotransmitter transport and neuropeptide signal pathway. The pro- tein levels related the ATP synthesis and citric acid cycle decreased while the proteins with the glycolysis and oxy- gen-binding increased. Glutathione, GNBP-1 and GPD1L were related to preventing hypoxic damage. The results indicated that C24：l-Cers played a critical role in HPC and had potential in endogenous protective mechanism. The combinations of the system omies data of the different molecules were sufficient to give a further understanding of the molecular pathways affected by HPC. Our data provided an important insight to reveal the protection mechanism of HPC.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Transcriptome-based analysis of key genes and pathways affecting the linoleic acid content in chickens

Linoleic acid is an essential polyunsaturated fatty acid that cannot be synthesized by humans or animals themselves and can only be obtained externally.The amount of linoleic acid present has an impact on the quality and flavour of meat and indirectly affects consumer preference.However,the molecular mechanisms influencing the deposition of linoleic acid in organisms are not clear.As the molecular mechanisms of linoleic acid deposition are not well understood,to investigate the main effector genes affecting the linoleic acid content,this study aimed to screen for hub genes in slow-type yellow-feathered chickens by transcriptome sequencing(RNA-Seq)and weighted gene coexpression network analysis(WGCNA).We screened for candidate genes associated with the linoleic acid content in slow-type yellow-feathered broilers.A total of 399 Tiannong partridge chickens were slaughtered at 126 days of age,fatty acid levels were measured in pectoral muscle,and pectoral muscle tissue was collected for transcriptome sequencing.Transcriptome sequencing results were combined with phenotypes for WGCNA to screen for candidate genes.KEGG enrichment analysis was also performed on the genes that were significantly enriched in the modules with the highest correlation.A total of 13310 genes were identified after quality control of transcriptomic data from 399 pectoral muscle tissues.WGCNA was performed,and a total of 26 modules were obtained,eight of which were highly correlated with the linoleic acid content.Four key genes,namely,MDH2,ATP5B,RPL7A and PDGFRA,were screened according to the criteria|GS|>0.2 and|MM|>0.8.The functional enrichment results showed that the genes within the target modules were mainly enriched in metabolic pathways.In this study,a large-sample-size transcriptome analysis revealed that metabolic pathways play an important role in the regulation of the linoleic acid content in Tiannong partridge chickens,and MDH2,ATP5B,RPL7A and PDGFRA were screened as important candidate genes affecting the linoleic acid content.The results of this study provide a theoretical basis for selecting molecular markers and comprehensively understanding the molecular mechanism affecting the linoleic acid content in muscle,providing an important reference for the breeding of slow-type yellowfeathered broiler chickens.

融合原子交换特征信息的代谢路径预测

为获取从任意起始代谢物到目标代谢物的生化相关性较好的代谢路径,提出一种融合原子交换特征信息的基于约束的代谢路径预测算法PVA。结合代谢网络中具有的原子交换特征信息,建立一种基于约束的混合整数线性规划(MILP)代谢路径预测模型,以搜索从任意起始代谢物到目标代谢物并包含特定原子交换信息的代谢路径。实验结果表明,与同类方法相比,PVA能够有效地发现生化相关性更好的代谢路径。

喀斯特山地城市“三生”空间格局演化及碳代谢研究——以贵阳市为例

以贵阳市为例探索喀斯特山地城市“三生”空间格局演变与城市碳代谢之间的耦合关系,有助于“三生”空间资源协同优化,为未来低碳城市规划提供参考。通过构建“三生”空间及碳源(汇)属性分类体系,利用转移矩阵分析贵阳市2010-2020年“三生”空间格局演化特征,并运用碳流模型和生态网络分析方法探讨“三生”空间演化的碳代谢过程及其生态关系。结果表明:①2010-2020年,贵阳市“三生”空间格局变化显著,工业生产空间、城镇生活空间呈持续增长态势,农村生活空间呈现先增后减的态势,其余空间用地则呈现减少态势;②贵阳市垂直方向碳通量和水平方向净碳流值均为负,“三生”空间格局演化造成了碳排放量增加,碳收支存在明显不平衡现象;③整个研究期间掠夺限制关系始终占据主导地位,互惠共生关系也明显增加,随着“三生”空间格局演化程度愈发剧烈,竞争关系也随之出现。贵阳市“三生”空间格局演化对促进彼此绿色发展生态关系具有积极影响,但同时也存在问题,需进一步打造低碳城镇化建设布局,促进绿色发展。

Calculation of Empirical and True Maintenance Coefficients by Flux Balance Analysis

The stoichiometric matrix of a simplified metabolic network in Bacillus Subtillis was contructed from the flux balance equations, which were used for reconciliation of the measured rates and determination of the inner metabolic rates. Thus more reliable results of the true and empirical maintenance coefficients were obtained. The true maintenance coefficient is linearly related to the specific growth rate and changes with the P/O ratiol. The neasured biomass yield of adenosine triphosphate (ATP) is also linearly related to the P/O ratio.

2型糖尿病合并轻度认知障碍患者脑葡萄糖代谢连接网络拓扑结构分析

目的:分析2型糖尿病(T2DM)合并轻度认知障碍(MCI)(T2DM&MCI)患者脑葡萄糖代谢连接网络拓扑结构的改变情况。方法:选择阿尔茨海默病神经影像学倡议(ADNI)数据库收录的T2DM&MCI受试者(观察组)和健康受试者(对照组),每组35例。从数据库获取2组简易智能精神状态检查量表(MMSE)评分、患者报告日常认知量表的记忆评分(EcogPtMem)、患者亲属报告日常认知量表的记忆评分(EcogSPMem)以及氟代脱氧葡萄糖-电子发射断层扫描(FDG-PET)图像。利用SPM12将FDG-PET图像标准化到蒙特利尔神经病学研究所(MNI)空间脑模板上。以小脑作为参考区,提取每个受试者解剖自动标记法(AAL)模块脑区的标准摄取值比率(SUVR),构建葡萄糖代谢连接脑网络。采用GRETNA软件分析全局效率、度中心性、节点局部效率等脑网络拓扑属性;采用Spearman相关性分析差异脑区度中心性与EcogPtMem评分以及EcogSPMem评分的相关性、节点局部效率与EcogPtMem评分以及EcogSPMem评分的相关性。结果:与对照组比较,观察组MMSE评分明显更低,EcogPtMem和EcogSPMem评分均明显更高,差异具有统计学意义(P<0.05)。与对照组比较,观察组的全局效率组间差异无统计学意义(P>0.05)。与对照组比较,观察组左侧海马旁回(P=0.005)、左侧海马(P=0.013)、左侧顶下缘角回(P=0.031)、左侧角回(P=0.034)、右侧角回(P=0.013)、右侧顶下缘角回(P=0.044)、右侧海马旁回(P=0.024)度中心性明显降低;左侧直回(P=0.028)、左侧海马旁回(P=0.044)、左侧海马(P=0.026)、左侧顶下缘角回(P=0.031)、左侧角回(P=0.024)、右侧角回(P=0.004)、右侧顶下缘角回(P=0.039)节点局部效率明显降低。相关性分析结果显示,观察组左侧海马度中心性(r=-0.273,P=0.022)和左侧海马旁回度中心性(r=-0.341,P=0.004)与EcogPtMem评分呈负相关关系;左侧海马度中心性(r=-0.391,P=0.001)、左侧海马旁回度中心性(r=-0.410,P<0.001)、右侧海马旁回度中心性(r=-0.240,P=0.045)与EcogSPMem评分呈负相关关系。观察组左侧海马节点局部效率(r=-0.257,P=0.032)、左侧海马旁回节点局部效率(r=-0.251,P=0.036)、右侧角回节点局部效率(r=-0.265,P=0.027)与EcogPtMem评分呈负相关关系;左侧海马节点局部效率(r=-0.363,P=0.002)和左侧海马旁回节点局部效率(r=-0.362,P=0.002)与EcogSPMem评分呈负相关关系。结论:T2DM&MCI患者脑葡萄糖代谢连接网络拓扑结构异常可能是引起其记忆功能下降的原因之一,这可为探究T2DM&MCI神经机制提供参考。

基于尿液代谢组学和生物网络分析整合策略研究益肾固络合剂治疗慢性肾小球肾炎作用机制

目的:基于尿液代谢组学和生物网络分析整合策略研究益肾固络合剂治疗慢性肾小球肾炎(CGN)的关键代谢途径和潜在靶点。方法:建立阳离子化牛血清白蛋白(C-BSA)诱导的CGN大鼠模型,采用超高效液相色谱-质谱联用(UPLC-MS)代谢组学筛选益肾固络合剂治疗CGN的尿液内源性代谢标志物和代谢途径。整合生物网络分析工具对益肾固络合剂调节的生物标志物进行网络分析,挖掘益肾固络合剂治疗CGN的生物功能和潜在靶点。结果:基于尿液代谢组学技术筛选出花生四烯酸等20个益肾固络合剂治疗CGN的内源性代谢标志物。对益肾固络合剂调控的关键内源性代谢物生物网络分析结果表明,花生四烯酸代谢是益肾固络合剂发挥治疗CGN作用的潜在关键通路,其代谢途径上的细胞色素P4504A11(CYP4A11)等10个靶点为益肾固络合剂治疗CGN的潜在靶点。结论:本研究初步揭示了益肾固络合剂治疗CGN的作用机制,为益肾固络合剂临床推广应用提供科学理论依据。

厌氧消化的胞内代谢通量与关键节点特征分析

文章依据厌氧消化1号模型建立便于实施代谢通量分析的代谢网络,以pH为扰动因子,研究不同pH值下厌氧消化(AD)过程中各代谢途径通量的分布信息,明确了AD胞内代谢的主要途径和关键节点的刚柔性。结果显示,糖代谢阶段经过EMP途径的6-磷酸葡萄糖通量约为95.14%,表明EMP途径在AD糖代谢中占主导。在丙酮酸代谢过程中,主要的代谢途径为86.53%的丙酮酸代谢为乙酰CoA,再由54.66%的乙酰CoA代谢为乙酸。在产甲烷阶段,66.63%的甲烷来源于乙酸,其余由氢还原CO_(2)形成,乙酸转换为AD主要的产甲烷途径。同时根据不同pH条件下,关键节点上通量分配比的变化特征可知,6-磷酸葡萄糖、乙酰CoA和乙酸为AD过程中的柔性节点,丙酮酸为刚性节点,可以针对柔性节点实施调控,以达到提高AD处理效率的目的。

网络视角下城市物质代谢过程解析——以京津冀13城市为例

城市作为物质吞吐量的集中区,往往存在一定的资源消耗和环境问题。京津冀是中国北方经济规模最大的地区,负载着巨大的资源环境压力。以京津冀地区为例,在计算城市物质输入与输出的基础上,运用生态网络分析方法对代谢系统内部的具体路径和节点关系进行深入探究,并分析其变化。结果表明,京津冀地区97%的流量都集中于各城市内部,各城市之间的优势互补有待加强。2017年京津冀13城市代谢总流量增长为2000年的3.3倍,其中承德、秦皇岛和唐山增长最为迅速,而衡水是唯一一个流量下降的城市。各城市的关键路径中有71.8%呈现流量上升态势,加工制造业为涉及关键路径最多的部门,其中加工制造业-环境为大多数所有城市共有的关键路径。掠夺/控制是京津冀13城市的主导关系类型;2000—2017年间13城市总体的生态关系良性转变少于劣性转变,致使共生关系数量下降,需要从交通运输业、采掘业和加工制造业等重点部门上寻求突破。关键路径及生态关系等代谢网络特征及其变化的识别,有效打破了城市代谢的“黑箱”,为缓解不同城市物质利用所带来的资源环境问题提供可靠理论依据。

Mapping theArabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic genome-wide association studies （mGWAS）, whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309Arabidopsis accessions were grown under two independent environmental conditions （control and stress） and subjected to untargeted LC-MS- based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two- condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci （p ≤ 1.0E-08）, 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was fol- lowed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between struc- tural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different ablotic environments for identifying metabollte-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.

FluxExplorer: A general platform for modeling and analyses of metabolic net-works based on stoichiometry

Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

产朊假丝酵母分批发酵生产谷胱甘肽的代谢通量分析

根据产朊假丝酵母利用葡萄糖作为惟一碳源生物合成谷胱甘肽的代谢网络,利用代谢通量分析方法分析了谷胱甘肽分批发酵不同阶段各代谢途径碳通量的分布和变化规律.在此基础上,通过分阶段温度控制和L-半胱氨酸添加等策略,调节谷胱甘肽分批发酵过程中的代谢通量分布,谷胱甘肽生物合成的产量明显提高,最大增加幅度分别达到23%和91%·同时对这些策略下谷胱甘肽的过量合成机理进行了解释.

生物信息学用于代谢网络研究的进展与展望

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