地衣芽孢杆菌发酵生产b-甘露聚糖酶的代谢通量分析

基于代谢通量分析理论研究了地衣芽孢杆菌生物合成b-甘露聚糖酶的胞内代谢活动. 首先构建了地衣芽孢杆菌产b-甘露聚糖酶的代谢网络,并依据代谢物质量平衡原则建立了反应网络的代谢流模型;进一步采用线性规划的优化方法分别以b-甘露聚糖酶合成反应通量最大和菌体生长速率最大为目标函数对模型进行求解,由此计算得到b-甘露聚糖的最大理论转化率为57.87%. 最后对代谢网络中的5个关键节点进行了比较分析,得到了2个类似非刚性的代谢节点(5-磷酸核酮糖节点和草酰乙酸节点),为利用基因工程方法提高b-甘露聚糖酶的生产能力提供了理论依据.     

Grey and black-box modelling based on neural networks and artificial immune systems applied to solid dissolution by rotating disc method

The dissolution rates of urea, sodium bicarbonate, and sodium carbonate in water and aqueous solutions were determined using the rotating disc technique. The experiments showed that the dissolution rate increases with increasing disc surface area, temperature, and rotating speed, while it decreases with the solute concentration increase in the dissolution medium. The comparison between experimental values for the dissolution rate and those calculated from Levich equation evidenced a satisfactory agreement in the case of the urea dissolution and poor compliance for the sodium bicarbonate and sodium carbonate dissolution. This poor results and the lack of a good model for making predictions in different situations determined the generation of empirical and semiempirical models (black and grey box approaches) which include neural networks developed with Clonal Selection algorithm (belonging to the Artificial Immune System class) and combination between neural network and phenomenological model. Satisfactory results were obtained with neural networks (black box models) and hybrid models (grey box models).     

Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

An artificial neural network (ANN) and a genetic algorithm (GA) are employed to model and optimize cell parameters to improve the performance of singular, intermediate‐temperature, solid oxide fuel cells (IT‐SOFCs). The ANN model uses a feed‐forward neural network with an error back‐propagation algorithm. The ANN is trained using experimental data as a black‐box without using physical models. The developed model is able to predict the performance of the SOFC. An optimization algorithm is utilized to select the optimal SOFC parameters. The optimal values of four cell parameters (anode support thickness, anode support porosity, electrolyte thickness, and functional layer cathode thickness) are determined by using the GA under different conditions. The results show that these optimum cell parameters deliver the highest maximum power density under different constraints on the anode support thickness, porosity, and electrolyte thickness.     

Colour‐appearance modeling using feedforward networks with Bayesian regularization method. Part II: Reverse model

In Part I of this article, the development of a multilayer perceptrons feedforward artificial neural network model to predict colour appearance from colorimetric values was reported. Bayesian regularization was employed for the training of the network. In this part of the article, the reverse model, that is, the perdition of colorimetric values from the colour appearance attributes is reported using the same neural network design methodology developed in Part I. This study should contribute to the building of an artificial neural network–based colour appearance prediction, both forward and reverse, using the most comprehensive LUTCHI colour appearance data sets for training and testing. Good prediction accuracy and generalization ability were obtained using the neural networks built in the study. Because the neural network approach is of a black‐box type, colour appearance prediction using this method should be easier to apply in practice. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 116–121, 2002; DOI 10.1002/col.10030     

基于神经网络和过程机理的热流体系统仿真

构建了一种基于复合神经网络和过程机理特性的热流体系统仿真模型 .该模型在形式上为一种复合人工神经网络模型 ,保证了模型具有十分理想的仿真速度 ;在网络模型设计上较好地考虑了系统输入与输出间的物理基础 ,网络模型在一定程度上由常规的黑箱模型转化为“灰箱模型” ,网络的训练除了具有常规的输入、输出间的纯数值映射关系学习功能之外较好地体现了对象输入与输出间的物理机理学习 ,保证了网络模型具有良好的联想能力、外推能力和时间递推能力 .     

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.     

HYBRID NEURAL APPROACHES FOR MODELLING DRYING PROCESSES FOR PARTICULATE SOLIDS

ABSTRACT

This work presents methods for synthesizing drying process models for particulate solids that combine prior knowledge with artificial neural networks. The inclusion of prior knowledge is investigated by developing two applications with the data from two indirect rotary steam dryers. The first application consisted in the modelling of the drying process of soya meal in a batch indirect rotary dryer, The external and internal mass transfer resistances were associated in the hidden layer of the network to linear and sigmoidal nodes, respectively. The second application consisted in the modelling of the drying process of soya meal in a continuos indirect rotary dryer. The model was constructed using the Semi-parametric Design Approach. The model predicts the evolution of solid moisture content and temperature as a function of the solid position in the dryer. The results show that the hybrid model performs better than the pure “ black box” neural network and default models. They also shows that prior knowledge enhances the extrapolation capabilities of a neural network model,     

青霉素发酵过程中的混合建模

由于微生物发酵过程机理的复杂性和高度非线性,建立发酵过程的精确模型具有一定难度。传统的动力学模型预测虽然会与实际输出有一定偏差,但它在某种程度上反映了过程机理;神经网络建模方法属于"黑箱"方法,建模过程中没有用到任何先验知识,有一定的预测效果;因此单一的建模方法往往会不具备其他建模方法的优势。本文以传统的发酵动力学模型为基础,结合RBF神经网络进行混合建模的"灰箱"建模方法是一种比较好的建模思路,可以取得较满意的软测量效果。     

BP神经网络在膜分离处理废水中的应用

为能更好地预测工艺条件对膜分离过程的影响,运用BP神经网络技术建立输入变量为压差、流速、浓度和温度,输出变量为膜通量的预测模型。通过大量实验数据训练预测模型,得到的网络模型整体误差平方和仅为0.014 5;计算值与模拟值相比,10组不同条件的膜通量平均预测误差仅为1.1,证实了所建立的BP神经网络膜通量预测模型与实验值吻合程度较好,有较好地预测能力。在此基础上进一步考察了工艺参数对膜分离过程的影响。     

Dynamic Modeling and Metabolic Flux Analysis for Optimized Production of Rhamnolipids

A comprehensive metabolic network based on the fundamental pathways representing the central metabolism of rhamnolipid by Pseudomonas aeruginosa is proposed and a dynamic model compatible with the underlying metabolic network is developed involving the macro-reactions derived from the elementary flux modes of the reaction network. The experimentally validated mathematical model is then coupled with a global optimization technique called differential evolution (DE) to optimize the medium composition as well as the extracellular and intracellular fluxes of the metabolic network. The analysis of the results shows the usefulness of the integrated approach involving the development of a dynamic model based on the metabolic network structure and model-based optimization of the medium composition and metabolic fluxes by an efficient evolutionary optimization technique to enhance the productivity of rhamnolipid.     

白炭黑填充PDMS/PVDF复合膜的纳滤分离性能及传质特性

将纳米级白炭黑填充于PDMS制备了白炭黑填充PDMS/PVDF复合膜,采用红外(FT-IR)、热失重(TGA)和接触角(CA)等方法对填充复合膜进行了分析和表征,并采用纳滤的方法系统研究了复合膜对大豆油/己烷混合油的分离性能。结果表明,白炭黑填充能有效促进PDMS的交联,提高PDMS的疏水性、热失重温度以及对溶剂的稳定性;白炭黑填充量增加使复合膜渗透通量降低,但截留率从96%提高到98%;随溶液浓度增加,渗透通量和截留率同时降低;随温度的升高,渗透通量上升,截留率降低。大豆油和己烷在膜中的传质特性可用不完全的溶解-扩散模型描述,溶液渗透压实验值与计算值符合较好。     

A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks

Constraint-based models of metabolism seldom incorporate tight bounds, or capacity constraints, on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. OCCI is expected to be a useful tool for integrating high-throughput fitness measurements with constraint-based models for elucidating metabolic network capacities.     

Performance study on ultrafiltration of Kraft black liquor and membrane characterization using Spiegler-Kedem model

Ultrafiltration of Kraft black liquor was carried out by using an asymmetric membrane in a stirred batch cell, modified to work on a continuous mode. Spiegler-Kedem (SK) model from irreversible thermodynamics was used for the estimation of different membrane-solute parameters, like solute permeability (P_m) and reflection coefficient (Σ). The P_m andΣ so calculated from the above model were used to study the variation of these parameters with other process variables, like bulk concentration, pressure difference and stirrer speed. Finally, a simulation model was developed with the objective to predict permeate flux and rejection, which coupled the film theory, osmotic pressure model and SK model. The simulation results obtained from this study were validated with the experimental data using cellulose acetate membrane of 5,000 Da MWCO. Reasonably good agreements between the predicted and experimental values were observed and the average absolute deviation (AAD) for the prediction of flux and rejection using SK model was found to be 6.3%.     

Extendable Machine Learning Model for the Stability of Single Atom Alloys

In this work, we aim to update the understanding of how impurity or promoter metals segregate on metal surfaces, particularly in the application of single-atom alloys (SAA) for catalysis. Using density functional theory, we calculated the relative stability of the idealized SAA relative to subsurface, dimer, and adatom configurations to determine the tendency of the promoter atom to diffuse into the bulk, form surface clusters, or avoid alloying with the host, respectively. We selected 26 d-block metals augmented with Al and Pb to create a 28?×?28 database that indicates a total of 250 combinations for which the SAA configuration is most stable, and an additional 358 systems for which the SAA geometry is within 0.5 eV of the most stable configuration. We classified the data using decision tree, support vector machine, and neural network machine learning algorithms with tabulated atomic properties as the input vector. These black box approaches are unable to extrapolate to other possible geometries, which was circumvented by redefining the stability problem as a regression. We propose a physical bond counting model to formulate intuitive criteria for the formation of stable SAAs. The accuracy is then improved by using the bonding configuration and tabulated atomic properties with a kernel ridge regression (KRR) algorithm. The hybrid KRR model correctly identifies 190 SAAs with 85 false positives. Importantly, its physical basis allows the hybrid model to extend to similar geometries not included in the training data, thereby expanding the domain where the model is useful.

     

利用基元模式分析酿酒酵母的葡萄糖厌氧发酵过程以提高乙醇产量

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.     

结合元素组成数据进行细胞生长过程代谢通量分析

将细胞内的生物大分子合成网络简化为对某些中心代谢系统前体的需求速率 ,由此得到一枯草杆菌生长代谢网络 ,并以其为对象结合文献数据对细胞生长过程的通量分析方法进行了研究 .提出了一种利用碳元素平衡和一般化还原度平衡将细胞的元素组成数据与其分子组成数据相结合计算胞内通量分布的最小二乘解的方法 ,可以利用更多的测量数据 ,并保证各测得量间的一致性 ,从而使计算结果更为可靠 .     

Metabolic network modelling: Including stochastic effects

We propose to model the dynamics of metabolic networks from a systems biology point of view by four dynamical structure elements: potential function, translocation matrix, degradation matrix, and stochastic force. These four elements are balanced to determine the network dynamics, which gives arise to a special stochastic differential equation supplemented by a relationship between the stochastic force and the degradation matrix. Important network behaviors can be obtained from the potential function without explicitly solving for the time-dependent solution. The existence of such a potential function suggests a global optimization principle. The existence stochastic force corresponds naturally to the hierarchical structure in metabolic networks. We provide theoretical evidences to justify our proposal by discussing its connections to others large-scale biochemical systems approaches, such as the network thermodynamics theory, biochemical systems theory, metabolic control analysis, and flux balance analysis. Experimental data displaying stochasticity which carries important biological information are also pointed out.     

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。     

用MATLAB神经网络预测气力输送过程物料破碎率

物料在气力输送过程中的破碎率大小,是评判气力输送系统及输送工艺优劣的重要参数之一.利用MATLAB语言,将炭黑气力输送系统各位置的压力数据输入BP神经网络,用训练好的网络模型进行仿真,对炭黑的破碎率进行了预测.与实验结果对比,采用BP神经网络对炭黑破碎率的预测具有比较高的精确度,可对气力输送系统的优化设计以及输送工艺参数的优化选取提供指导.