Calculation of Empirical and True Maintenance Coefficients by Flux Balance Analysis

The stoichiometric matrix of a simplified metabolic network inBacillus Subtillis was constructed from the flux balance equations,which were used for reconciliation of the measured rates anddetermination of the inner metabolic rates. Thus more reliableresults of the true and empirical maintenance coefficients wereobtained. The true maintenance coefficient is linearly related to thespecific growth rate and changes with the P/O ratio. The measuredbiomass yield of adenosine triphosphate (ATP) is also linearlyrelated to the P/O ratio.

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.

Elementary Flux Mode Analysis for Optimized Ethanol Yield in Anaerobic Fermentation of Glucose with Saccharomyces cerevisiae

Elementary flux mode （EFM） analysis was used in the metabolic analysis of central carbon metabolism in Saccharomyces cerevisiae based on constructed cellular network. Calculated from the metabolic model, the ethanol-producing pathway No. 37 furthest converts the substrate into ethanol among the 78 elementary flux modes. The in silico metabolic phenotypes predicted based on this analysis fit well with the fermentation performance of the engineered strains, KAM3 and KAMll, which confirmed that EFM analysis is valid to direct the construction of Saccharomyces cerevisiae engineered strains, to increase the ethanol yield.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning(ML)are crucial components of the evolving next-generation tools in systems and synthetic biology,aiming to unravel the intricate relationship between genotype,phenotype,and the environment.Nonetheless,the comprehensive exploration of integrating these two frameworks,and fully harnessing the potential of fluxomic data,remains an unexplored territory.In this study,we present,rigorously evaluate,and compare ML-based techniques for data integration.The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production.Specifically,we investigated the influence of succinate dehydrogenase(SDH)on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments.The findings indicate a noticeable increase in ethanol yield,ranging from 6%to 10%,in SDH subunit gene knockout strains compared to the wild-type strain.Moreover,in pursuit of a high-yielding strain for ethanol production,dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase(GPD)and SDH.The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1,Δsdh5Δgpd1,Δsdh6Δgpd1,Δsdh4Δgpd2,Δsdh5Δgpd2,and Δsdh6Δgpd2,with improvements of 21.6%,27.9%,and 22.7%,respectively.Overall,the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions.In addition,the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes,thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Flux Balance Analysis Reveals Potential Anti-HIV-1 Metabolic Targets

Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.

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.

深时源-汇系统要素的常用定量分析方法

源-汇系统研究是构造地质学、沉积学和层序地层学的综合,因其整体性、动态化和半定量-定量的特点受到了广泛关注。首先阐述了目前深时(前第四纪)源-汇系统的关键问题是物质平衡的定量表征及搬运过程对沉积物的控制,由于地层记录缺失和参数获取困难等原因,研究仍极具挑战。随后综述了深时源-汇系统定量分析方法,可分为地质年代学法、将今论古法和沉积学法。各方法通过获取地貌要素、水力学参数、侵蚀速率、沉积通量等信息,建立“源”“汇”之间的定量关系,进而重建盆地沉积充填演化史。通过系统介绍不同方法的基本原理、相关参数,对比其优越性及局限性,认为地质年代学法应用较广,核心在于物源示踪;将今论古法关键是地质背景的类比及地质参数的选择;沉积学法受多变量控制,需兼顾构造-气候背景及研究尺度。最后对深时源-汇系统定量分析的发展进行了展望,在“将今论古”这一重要思想指导下,需着眼于物源体系、沉积物搬运路径、沉积物分配关系、系统内的各要素及其耦合作用,需注重多时间尺度的定量表征、多学科交叉的动态研究。而相较于大陆边缘源-汇系统,陆相湖盆源-汇系统模式与预测模型有待进一步完善。

Effects of the Soil Heat Flux Estimates on Surface Energy Balance Closure over a Semi-Arid Grassland

Soil heat flux is important for surface energy balance （SEB）, and inaccurate estimation of soil heat flux often leads to surface energy imbalance. In this paper, by using observations of surface radiation fluxes and soil temperature gradients at a semi-arid grassland in Xilingguole, Inner Mongolia, China from June to September 2008, the characters of the SEB for the semi-arid grassland were analyzed. Firstly, monthly averaged diurnal variations of SEB components were revealed. A 30-min forward phase displacement of soil heat flux （G） observed by a fluxplate at the depth of 5-em below the soil surface was conducted and its effect on the SEB was studied. Secondly, the surface soil heat flux （Gs） was computed by using harmonic analysis and the effect of the soil heat storage between the surface and the fluxplate on the SEB was examined. The results show that with the 30-min forward phase displacement of observed G, the slope of the ordinary linear regression （OLR） of turbulent fluxes （H＋LE） against available energy （Rn G） increased from 0.835 to 0.842, i.e., the closure ratio of SEB increased by 0.7%, yet energy imclosure of 15.8% still existed in the SEB. When Gs, instead of G was used in the SEB equation, the slope of corresponding OLR of （H＋LE） against （Rn-Gs） reached 0.979, thereby the imelosure ratio of SEB was reduced to only 2.1%.

Using high-throughput data and dynamic flux balance modeling techniques to identify points of constraint in xylose utilization in Saccharomyces cerevisiae

Background Several enzymes and cofactors have been identified as contributing to the slow utilization of xylose by xylose-fermenting strains of Saccharomyces cerevisiae.However,there has been no consensus on which of these possible bottle-necks are the most important to address.A previous strain characterization study from our lab suggested that insufficient NAD+limits fermentation and may be the most important bottleneck affecting utilization of xylose for the production of ethanol.The development and validation of a genome scale dynamic flux balance model would help to verify the existence and extent of this and other metabolic bottlenecks and suggest solutions to guide future strain development thereby minimiz-ing bottleneck impact on process economics.Results A dynamic flux balance model was developed to identify bottlenecks in several strains of S.cerevisiae,both with wild-type pentose phosphate pathway expression and with the pathway over expressed.ZWF1 was found to be limiting in the oxidative portion of the pentose phosphate pathway under oxygen replete conditions.This pathway is used to regenerate NADPH.Under oxygen limiting conditions,respiration of xylose was limited by the lack of oxygen as a terminal electron acceptor.Ethanol production was also limited under these conditions due to the inability to balance NAD+/NADH.The model suggests the use of the anaplerotic glyoxylate pathway to improve NAD+/NADH balance,increasing ethanol produc-tion by 50%while producing succinate as a coproduct at upwards of 20 g/l.Conclusion In the production of high value chemicals from biomass,the use of the respiratory metabolism is a waste of feedstock carbon.Bottlenecks previously identified in the oxidative pentose phosphate pathway are currently only relevant under oxygen-replete conditions and cannot impact the partitioning of carbon between the respiratory and fermentative pathways.Focusing future efforts on the non-respiratory balancing of NAD+/NADH,perhaps through the glyoxylate pathway,would improve the economics of ethanol production both directly and through coproduct formation.

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.

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.

轮作稻麦田水热通量及影响因素分析

轮作稻麦田水热通量及影响因素研究可为田间灌水管理和作物高效用水提供依据。该文利用波文比能量平衡监测系统测定的轮作稻麦田的水热通量数据及通径分析方法,分析了2种不同农田的水热通量变化规律及影响因素。结果表明轮作稻麦田的水热通量呈单峰曲线日变化。能量主要被潜热通量所消耗,全生育期小麦田潜热通量占可供能量的比例为71%,而2016和2017年水稻全生育期潜热通量占可供能量的比例分别为106%和122%,表明水稻冠层吸收了感热通量以进行水分消耗。净辐射对轮作稻麦田潜热通量的影响主要为直接作用,而1.5 m高气温、饱和水汽压差和风速主要通过净辐射路径对潜热通量产生间接影响。水稻田潜热通量受净辐射的直接作用小于小麦田,而受饱和水汽压差的直接作用大于小麦田。水稻田各影响因子对潜热通量影响的间接作用比小麦田更大。

半干旱草原下垫面能量平衡特征及土壤热通量对能量闭合率的影响

土壤热通量在半干旱草原下垫面能量平衡研究中极为重要,土壤热通量估计不够准确是导致地表能量不平衡的一个重要原因。利用2008年6—9月锡林郭勒草原主生长期地表辐射、通量和土壤温度梯度观测资料,研究中纬度半干旱草原下垫面地表能量平衡特征。首先,在分析能量平衡各分量月平均日变化特征的基础上,通过对土壤热流量板观测的5 cm深度土壤热通量(G)的相位前移,研究了土壤热通量相位滞后对地表能量平衡产生的影响;其次,利用谐波分析方法,通过计算地表土壤热通量(Gs),分析了地表到热流量板之间的土壤热量储存对地表能量平衡的影响。结果表明:(1)将土壤热通量相位前移30 min,湍流通量与可利用能量(Rn-G)线性回归的斜率从0.835增加到0.842,地表能量闭合率提高了0.7%,但仍有15.8%的能量不闭合;(2)考虑了地表到热流量板之间的土壤热量储存之后,湍流通量与可利用能量之间的回归斜率达到0.979,能量不闭合程度仅为2.1%。

黄土高原半干旱区冬小麦田土壤热通量的计算方法研究

利用2010年6月平凉陆面过程与灾害天气观测研究站陆面过程观测资料,对比分析了温度积分法、温度预报校正法和谐波法三种土壤热通量计算方法以及这三种方法的计算结果对地表能量平衡的影响,并探讨了土壤水分运动对土壤热通量的影响。结果表明,研究区土壤垂直非均质性明显,由谐波法计算得到的0.05~0.10 m土壤热扩散系数κ是0.10~0.20 m土壤层的3倍左右。通过对比三种方法计算得到的0.05 m土壤热通量和热通量板的实测结果表明,温度积分法、温度预报校正法和谐波法计算的土壤热通量峰值都比实测值偏小,分别偏小约23.6%、25.0%和12.8%。与不考虑土壤热存储的情况相比,利用温度积分法和温度预报校正法将土壤热通量板的实测结果校正到地表,这样得到的陇东黄土高原雨后晴天农田地表能量闭合率分别提高了7.96%和4.54%;由谐波法计算的地表土壤热通量可使地表能量闭合率提高约5.65%。在夏季雨后晴天,发生在农田土壤中的水汽输送及相应的相变过程对土壤热通量有一定影响。

基于冠心病血瘀证心肌细胞能量代谢网络模型的流平衡分析

目的探讨冠心病(coronary artery heart disease,CHD)急、慢性血瘀证心肌细胞能量代谢的变化特点。方法 (1)运用Flux Explorer软件平台构建起的CHD血瘀证心肌细胞能量代谢网络模型;(2)应用GC-MS技术和血气自动分析仪从健康对照、CHD慢性血瘀证、CHD急性血瘀证三组大鼠模型中取冠状动脉血检测葡萄糖、脂肪酸及血氧含量;(3)检测结果代入网络模型,通过流平衡分析CHD急、慢性血瘀证状态下参数的特点。结果 (1)三组的糖酵解、脂代谢和三羧酸循环均已被激活,但网络运算后各组代谢物的数值则有明显差别,均呈现出健康对照组>CHD慢瘀证组>CHD急瘀证组的趋势。(2)三组心肌细胞能量代谢的血糖、脂肪酸和血氧含量及ATP的产量均呈递减的趋势,这与三组中糖酵解的关键酶(HK、pfk、PK)、三羧酸循环的关键酶(Cs、Icdh、Od)的浓度呈一致性变化。结论 CHD急性血瘀和慢性血瘀不同证候的形成可能与机体内心肌细胞能量代谢的水平密切相关。

基于氧碳平衡的重组巴氏毕赤酵母碳源代谢分析

基于氧元素和碳元素平衡对重组巴氏毕赤酵母 (Pichiapastoris)的不同碳源代谢进行了分析 .结果表明 :以甘油为惟一碳源 ,当比生长速率由 0 0 6 4h-1增大到 0 2 5 7h-1时 ,用于细胞生长的甘油代谢比速率由1 36mmol·g-1·h-1增大到 5 4 6mmol·g-1·h-1,而用于细胞维持代谢流比例降低了 10 4 % ,同时甘油用于合成代谢和分解代谢的比速率均增大 ,但是分解代谢流比例逐渐下降 ,而合成代谢流比例却升高了 5 9% ;以甲醇为碳源进行表达重组人血清白蛋白恒化培养时 ,当比生长速率由 0 0 19h-1增大至 0 0 4 6h-1时 ,用于产物重组人血清白蛋白形成的比速率呈先增大后减少的趋势 ,在比生长速率为 0 0 2 7h-1时最大 ,为 0 31mmol·g-1·h-1,占甲醇总代谢流的 13 2 % .

代谢通量分析在酶合成过程中的应用研究进展

代谢通量分析可以有效预测微生物的代谢状况,指导生物合成过程,已在多个领域得到成功应用。本文概括分析了三大代谢通量分析技术(基于物料平衡、基于同位素示踪技术以及基于基因组尺度规模网络)在工业生物技术领域的应用,阐述了相关技术的优势及局限性。在此基础上重点论述了基于物料平衡及同位素示踪这两类通量分析技术在酶合成领域的研究进展,分析了代谢通量分析应用于复杂代谢产物可能存在的问题,指出如何将信号转导、转录调控等信息纳入代谢网络将成为未来酶合成过程代谢分析的研究重点。

杂交瘤细胞代谢流分析模型及其在批式培养中的应用

通过物料衡算和线性规划方法建立了进行代谢流分析的数学模型 ,并首次应用于批式培养过程 .分析结果表明 ,细胞对葡萄糖等底物的代谢效率很低 ,细胞对谷氨酰胺等氨基酸浓度的变化响应迟缓 ,在底物充裕的迟滞期及谷氨酰胺限制条件下的代谢效率高于对数生长期 .另外限制谷氨酰胺等氨基酸进入能量代谢 ,既可减少氨的积累 ,也会减少乳酸的生成 .

时滞KdV-Burgers方程的行波解

利用流量松弛方法导出了时滞KdV-Burgers方程,并利用(1/G)-展开法,求得时滞KdV-Burgers及KdV-Burgers方程的行波解。结合所求得的解,对时滞KdV-Burgers方程行波约化后所得的常微分方程组(ODEs)进行了定性分析。研究表明:当时间特征常数τ与行波波速c的平方之积等于耗散系数α(即τc2=α)时,时滞KdV-Burgers方程出现了椭圆余弦波解和钟状孤波解,而KdV-Burgers方程没有此类解。另外,时滞的存在还影响到孤立波的振幅和波宽。

使用太阳模拟器进行热平衡试验的附加外热流影响分析

航天器的结构复杂化与表面热光学性能差异,使得航天器热平衡试验中对大型太阳模拟器的需求越来越大。文章根据离轴式太阳模拟器的结构,分析了使用太阳模拟器进行热平衡试验时,真空容器中附加外热流的来源及其对试验的影响,并通过建立热控星模型和容器与热控星的联合模型进行仿真计算,给出温度分布结果,进而提出相应的试验设计改进建议。