Metabolic flux-based modeling of mAb production during batch and fed-batch operations

This paper proposes mathematical models that predict the physiology, growth behavior and productivity of hybridoma cells in both batch and fed-batch systems. Murine hybridoma 130-8F producing anti-F-glycoprotein monoclonal antibody was employed as a model system. A systematic approach based on metabolic flux analysis (MFA) was utilized to yield a dynamic model for predicting the concentration of significant metabolites over time. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations. The coefficients of the model equation were estimated by employing the Metropolis–Hastings algorithm. The Metabolites Model was combined with the Biomass Model to get an Integrated Model capable of predicting concentration values for substrates, extracellular metabolites, and viable and dead cell concentration by utilizing only starting concentrations as input. The prediction accuracy of the model was tested by using experimental data.     

Analysis of metabolic pathways and fluxes in a newly discovered thermophilic and ethanol‐tolerant Geobacillus strain

A recently discovered thermophilic bacterium, Geobacillus thermoglucosidasius M10EXG, ferments a range of C5 (e.g., xylose) and C6 sugars (e.g., glucose) and is tolerant to high ethanol concentrations (10%, v/v). We have investigated the central metabolism of this bacterium using both in vitro enzyme assays and ¹³C‐based flux analysis to provide insights into the physiological properties of this extremophile and explore its metabolism for bio‐ethanol or other bioprocess applications. Our findings show that glucose metabolism in G. thermoglucosidasius M10EXG proceeds via glycolysis, the pentose phosphate pathway, and the TCA cycle; the Entner–Doudoroff pathway and transhydrogenase activity were not detected. Anaplerotic reactions (including the glyoxylate shunt, pyruvate carboxylase, and phosphoenolpyruvate carboxykinase) were active, but fluxes through those pathways could not be accurately determined using amino acid labeling. When growth conditions were switched from aerobic to micro‐aerobic conditions, fluxes (based on a normalized glucose uptake rate of 100 units (g DCW)^?1 h^?1) through the TCA cycle and oxidative pentose phosphate pathway were reduced from 64 ± 3 to 25 ± 2 and from 30 ± 2 to 19 ± 2, respectively. The carbon flux under micro‐aerobic growth was directed to ethanol, L ‐lactate (>99% optical purity), acetate, and formate. Under fully anerobic conditions, G. thermoglucosidasius M10EXG used a mixed acid fermentation process and exhibited a maximum ethanol yield of 0.38 ± 0.07 mol mol^?1 glucose. In silico flux balance modeling demonstrates that lactate and acetate production from G. thermoglucosidasius M10EXG reduces the maximum ethanol yield by approximately threefold, thus indicating that both pathways should be modified to maximize ethanol production. Biotechnol. Bioeng. 2009;102: 1377–1386. © 2008 Wiley Periodicals, Inc.     

Metabolic control analysis of an enzymatic biofuel cell

Metabolic control analysis (MCA) is an analytical technique that aims to quantify the distribution of control that enzymes exhibit over the steady‐state fluxes through a metabolic network. In an enzymatic biofuel cell, the flux of interest is the electrical current generated by the system. Regardless of transport limitations and other constraints, kinetic limitations can become potential bottlenecks in the operation of a biofuel cell. We have used an indirect approach to MCA to investigate a common osmium‐mediated glucose oxidase/laccase enzymatic biofuel cell. The results of the analysis show that the control of the electron flux strongly depends on the total mediator concentrations and the extent of polarization of the individual electrodes. The effect of varying oxygen concentrations is also examined, as oxygen is required for the cathode, but it participates in a non‐productive reaction at the anode. Under normal operating conditions the electrodes will be highly polarized and will both contain high mediator concentrations. This configuration will result in a dominant FCC at the anode, and the conditions that are needed for balanced flux control between the anode and cathode are explored. As increasingly complex bioelectrocatalytic systems and architectures are envisioned, MCA will be a valuable framework to facilitate their design and subsequent operation. Biotechnol. Bioeng. 2009;102: 1624–1635. © 2008 Wiley Periodicals, Inc.     

A rational design approach for amino acid supplementation in hepatocyte culture

Improvement of culture media for mammalian cells is conducted via empirical adjustments, sometimes aided by statistical design methodologies. Here, we demonstrate a proof of principle for the use of constraints‐based modeling to achieve enhanced performance of liver‐specific functions of cultured hepatocytes during plasma exposure by adjusting amino acid supplementation and hormone levels in the medium. Flux balance analysis (FBA) is used to determine an amino acid flux profile consistent with a desired output; this is used to design an amino acid supplementation. Under conditions of no supplementation, empirical supplementation, and designed supplementation, hepatocytes were exposed to plasma and their morphology, specific cell functions (urea, albumin production) and lipid metabolism were measured. Urea production under the designed amino acid supplementation was found to be increased compared with previously reported (empirical) amino acid supplementation. Not surprisingly, the urea production attained was less than the theoretical value, indicating the existence of pathways or constraints not present in the current model. Although not an explicit design objective, albumin production was also increased by designed amino acid supplementation, suggesting a functional linkage between these outputs. In conjunction with traditional approaches to improving culture conditions, the rational design approach described herein provides a novel means to tune the metabolic outputs of cultured hepatocytes. Biotechnol. Bioeng. 2009;103: 1176–1191. © 2009 Wiley Periodicals, Inc.     

Effect of wave exposure dynamics on gut content mass and growth of young‐of‐the‐year fishes in the littoral zone of lakes

Total length, body mass and gut content mass of young‐of‐the‐year (YOY) perch Perca fluviatilis, dace Leuciscus leuciscus and bleak Alburnus alburnus were recorded over the summer of 2006 at three littoral sites at Upper Lake Constance. In P. fluviatilis and L. leuciscus, gut content mass correlated positively with wave‐induced energy flux (E_F) of the respective site and sampling day, while no correlation of gut content mass with E_F was found in A. alburnus. It was assumed that benthivorous P. fluviatilis and L. leuciscus profited from suspended or uncovered benthic food items generated by wave action at sites and periods with high E_F. Alburnus alburnus, in contrast, feeding mainly on zooplankton in upper parts of the water column, could not profit from increased E_F. In P. fluviatilis, increased gut content mass during periods of high E_F resulted in higher growth rates. For L. leuciscus, no real growth rates in local fish populations could be determined, as individuals were less sedentary, and when increased growth occurred at sites during the periods of high E_F, migration of fish levelled out the resulting size differences within few days. The results of this study show that dynamic habitat variables affect site profitability in the littoral zone of lakes, especially in benthivorous fishes. Therefore, dynamic habitat variables should be considered in addition to fixed habitat properties in analyses of habitat choice of fishes in the littoral zone of lakes.     

数据处理方法不确定性对CO2通量 组分估算的影响

基于中国陆地生态系统通量观测研究网络(ChinaFLUX)4个站点(2个森林站和2个草地站)的涡度相关通量观测资料,分析了CO₂通量数据处理过程中异常值剔除参数设置、夜间摩擦风速(u^*)临界值(u^*_c)确定及数据插补模型选择对CO₂通量组分估算的影响.结果表明： 3种数据处理方法均对净生态系统碳交换量(NEE)年总量估算有显著影响,其中u^*_c确定是影响NEE估算的重要因子;异常值剔除、u^*_c确定及数据插补模型选择导致NEE年总量估算偏差分别为0.62～21.31 g C·m^-2·a^-1(0.84%～65.31%)、4.06～30.28 g C·m^-2·a^-1(3.76%～21.58%)和0.69～27.73 g C·m^-2·a^-1(0.23%～55.62%),草地生态系统NEE估算对数据处理方法参数设置更敏感;数据处理方法不确定性引起的总生态系统碳交换量和生态系统呼吸年总量估算相对偏差分别为3.88%～11.41%和6.45%～24.91%.     

东北东部5种温带森林的春季土壤呼吸

春季土壤解冻过程是中高纬度地区森林生态系统土壤呼吸(即土壤表面CO2通量,RS)年内变化的一个关键时期,但此期间RS的时间动态规律及其控制机理尚不清楚。以我国东北东部5种温带森林为研究对象,采用静态箱-气相色谱法测定春季土壤解冻时期RS动态及其相关的环境因子。结果表明:在土壤解冻过程中,RS受林型、解冻时期及其交互作用的显著影响。红松(Pinus koraiensis)林、落叶松(Larix gmelinii)林、硬阔叶林、杨桦(Populus davidiana-Betulaplaty phylla)林和蒙古栎(Quercus mongolica)林的RS变化范围依次为:10.0196.0mg·m-·2h-1,5.8217.1mg·m-·2h-1,9.7382.1mg·m-·2h-1,15.8-269.0mg·m-·2h-1和35.9262.5mg·m-·2h-1。RS的平均值随着解冻的进程而增大,其变化趋势大致与土壤温度的变化相吻合。土壤温度极显著地影响RS(R2=0.46-0.77),而土壤含水量对RS的影响则因林型和土壤深度而异。5种林型的土壤呼吸温度系数(Q10)依次为:落叶松林10.9,硬阔叶林7.1,红松林6.5,杨桦林4.3和蒙古栎林2.3。进一步的研究应该集中研究春季自然解冻过程中土壤呼吸的控制机制,尤其是土壤呼吸与土壤微生物种群动态及其活性之间的关系。     

拔节期追氮对不同季节糯玉米淀粉糊化特性的影响

明确基因型、生长环境和栽培措施对糯玉米淀粉糊化特性的影响可以调控淀粉品质。以垦粘1号、苏玉糯1号和苏玉糯5号为材料,研究了拔节期追氮量(0、150kghm-2和300kghm-2)对春季和秋季淀粉糊化特性的影响。结果表明:生长季节对淀粉糊化特性的影响大于基因型和拔节期追氮处理。秋季生长条件下的糯玉米淀粉具有较高的峰值黏度、谷值黏度、崩解值和终值黏度;拔节期适量追氮处理下糯玉米淀粉糊化特性较不追氮差异较小,但过量追氮峰值黏度和崩解值下降,糊化温度升高。相关分析表明,峰值黏度和谷值黏度、崩解值和终值黏度呈显著或极显著正相关,且峰值黏度、崩解值和峰值时间以及糊化温度呈显著或极显著负相关。以垦粘1号为材料,春季条件下生长并拔节期追施氮300kghm-2,秋季条件下生长并拔节期追施氮150kghm-2,淀粉的糊化特性较优,即具有较高的峰值黏度和崩解值。     

From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Due to reductions in both time and cost, group testing is a popular alternative to individual-level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual-level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual-level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa.