工业生物过程：生物技术产业化的关键——访北京化工大学副校长谭天伟教授

——请您简单介绍一下我国工业生物过程研究现状和趋势。 谭：工业生物过程是以可再生生物质资源为原料基础生产化学品、材料与能源的新型工业模式。工业生物过程工程包括过程单元、过程装备和过程优化三个主要方面。中国是一个工业生物技术大国,发酵体积1000万立方米．位居世界第一。     

工业生物技术的过程科学研究概况及发展趋势

简述了工业生物技术领域在过程科学方面的研究现状及发展趋势,从"细胞群体效应及过程放大原理"、"工业生物过程物质和能量传递与生物转化规律"、"工业生物过程优化新方法"等3个方面介绍了我国工业生物技术过程科学的重点进展和在国际上的地位,最后从"生物原料高效转化"、"生物转化过程物质和能量协调和匹配"、"生物过程强化"、"生物过程系统集成"等方面提出了工业生物技术在过程科学研究方面未来的战略方向。     

固态发酵技术在资源环境中的应用

发酵工程是生物技术的瓶颈,固态发酵作为发酵工程一个重要的部分,在资源环境应用研究方面取得了重要进展,主要表现在生物燃料,生物农药,生物转化,生物解毒及生物修复等领域,解决了能源危机,治理环境污染等问题,综述了近几年固态发酵技术在资源环境领域应用中一些重要的发展。     

工业物过程:生物技术产业化的关键——访北京化工大学副校长谭天伟教授

--请您简单介绍一下我国工业生物过程研究现状和趋势. 谭:工业生物过程是以可再生生物质资源为原料基础生产化学品、材料与能源的新型工业模式.工业生物过程工程包括过程单元、过程装备和过程优化三个主要方面.中国是一个工业生物技术大国,发酵体积1000万立方米,位居世界第一.     

影响固态发酵装料系数的因素及其强化措施

固态发酵是一项清洁、节能的生物发酵技术,随着能源问题的突出,固态发酵技术愈发显出其在生物技术领域重要的地位。然而固态发酵反应器的装料系数过低已经成为固态发酵过程工业发展的限制性瓶颈。从传递的角度对固态发酵过程中的温度梯度等问题进行剖析,提出了以粮仓效应为原理的新型工艺手段,为固态发酵的规模放大提供指导。     

固态发酵在中药炮制中的研究进展

固态发酵技术是中药炮制的重要方法。现代固态发酵技术是在继承传统发酵技术的基础上,结合发酵工程等现代生物技术而发展起来的。对传统中药发酵技术概况、现代中药固态发酵技术的研究进展及固态发酵在中药炮制中的作用进行了综述,并对中药发酵炮制的现代化发展进行了展望。     

加快生物过程关键技术及装备开发 支撑我国生物产业发展

<正>生物过程工业(bioprocess industry)包括传统的发酵工业和现代生物技术工业,是国民经济可持续发展的支柱之一。生物过程关键技术和装备是发展我国生物产业的重要支撑。国务院颁布的《国家中长期科学和技术发展规划纲要(2006~2020年)》明确将新一代工业生物技术和过程工业的绿色化、自动     

后基因组时代的工业生物技术

简述了工业生物技术的发展背景和意义,分析了基因组学和功能基因组学发展对工业生物技术的推动作用,重点介绍了本期专刊发表的代谢工程、发酵工程以及工业酶与生物催化领域的17篇论文。     

固态发酵工程与生物饲料添加剂的生产

发酵工程是生物技术的关键技术,固态发酵作为发酵工程的重要部分,由于能源危机与环境问题的日益严重,越来越受到人们的重视,并在资源环境应用研究方面取得了重要进展。着重综述了固态发酵技术在生物饲料添加剂生产中的应用,介绍了固态发酵工程技术的新进展。     

大数据时代的生物过程研究

正在讨论实现生物技术产业化时,认为由于从基因、细胞到生物反应器操作的生物过程高度复杂性,必须改变传统的数据处理思维方式。建议采用大数据分析方法和工业4.0,打破生命科学上游研究到生物制造下游研究的多学科技术壁垒,其大数据分析的4V特征和3个观念转变是生物过程研究的基本出发点。总结了笔者研究组近几十年的研究成果,分析了发酵过程多尺度理论方法与大数据分析理念,由此探索形成新的概念、理论、方法与装备技术。其中最重要的技术进展标志是生物反应器由原来的参数自动控制演变为实现过程优化与放大决策的智能控制,以及"数据超载"情况下的"信息→相关→因果→知识"的研究过程。     

Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.     

工业生物过程智能控制原理和方法进展

工业生物过程是一个复杂的系统过程,对活体细胞代谢过程的认识是实现高效工业生物制造的基础。文中首先综述了工业发酵过程多尺度优化控制原理和实践,包括多尺度理论与装备、细胞宏观代谢在线检测传感技术以及生理代谢参数相关分析。在此基础上,对工业生物过程智能控制——感知细胞内生理代谢特性新型传感技术、大数据库建立和数据深度计算以及生物过程智能决策进行了综述和展望。     

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.     

Bioprocess monitoring by marker gene analysis

The optimization and the scale up of industrial fermentation processes require an efficient and possibly comprehensive analysis of the physiology of the production system throughout the process development. Furthermore, to ensure a good quality control of established bioprocesses, on-line analysis techniques for the determination of marker gene expression are of interest to monitor the productivity and the safety of bioprocesses. A prerequisite for such analyses is the knowledge of genes, the expression of which is critical either for the productivity or for the performance of the bioprocess. This work reviews marker genes that are specific indicators for stress- and nutrient-limitation conditions or for the physiological status of the bacterial production hosts Bacillus subtilis, Bacillus licheniformis and Escherichia coli. The suitability of existing gene expression analysis techniques for bioprocess monitoring is discussed. Analytical approaches that enable a robust and sensitive determination of selected marker mRNAs or proteins are presented.     

Metabolic engineering of bacteria

Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering the main objective is the increase of a target metabolite production through genetic engineering. Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the production of a certain substance. In the last years, the development of recombinant DNA technology and other related technologies has provided new tools for approaching yields improvement by means of genetic manipulation of biosynthetic pathway. Industrial microorganisms like Escherichia coli, Actinomycetes, etc. have been developed as biocatalysts to provide new or to optimize existing processes for the biotechnological production of chemicals from renewable plant biomass. The factors like oxygenation, temperature and pH have been traditionally controlled and optimized in industrial fermentation in order to enhance metabolite production. Metabolic engineering of bacteria shows a great scope in industrial application as well as such technique may also have good potential to solve certain metabolic disease and environmental problems in near future.     

微生物过氧化氢酶的发酵生产及其在纺织工业的应用

微生物过氧化氢酶是一种重要的工业酶制剂,可以催化分解过氧化氢生成水和氧气。这一酶制剂在食品、纺织、医药等领域表现出广泛的应用潜力。生物工程和基因工程技术的进步推动了微生物过氧化氢酶的发酵生产。以下综述了微生物过氧化氢酶发酵生产的进展及其在纺织工业中的应用,同时讨论了微生物过氧化氢酶的发酵生产和纺织工业应用的未来趋势。     

Expert systems in the control of animal cell culture processes: Potentials,functions, and perspectives

In recent years, the development of advanced systems for bioprocess monitoring and control has become an area of intensive research. Along with traditional techniques, there are several new approaches which are increasingly being applied to bioprocess operations. Among these, of special note is expert system technology, which provides possibilities for the design of efficient bioprocess control systems with new functional capabilities. This technology has been successfully applied to variety of microbial processes at laboratory and industrial scale. The present paper analyzes the possibility for application of expert systems to animal cell cultures processes whose high complexity is well suited to expert control. The discussion focuses on the organization and the functionality of the intelligent control systems, and covers some practical aspects of their design.     

Artificial neural network based experimental design procedure for enhancing fermentation development

Conventional experimental design techniques are available to assist in the optimization of fermentation processes, but due to the nonlinearities in the bioprocess, they are limited in their effectiveness. This problem is further complicated with recombinant systems as a result of the additional complexities of the process. This article describes a general strategy using artificial neural networks as an alternative approach to fermentation process development laboratory are presented for the neural network based procedures. (c) 1994 John Wiley & Sons, Inc.     

Metabolic engineering under uncertainty. I: framework development

Standard bioprocess conditions have been widely applied for the microbial conversion of raw material to essential industrial products. Successful metabolic engineering (ME) strategies require a comprehensive framework to manage the complexity embedded in cellular metabolism, to explore the impacts of bioprocess conditions on the cellular responses, and to deal with the uncertainty of the physiochemical parameters. We have recently developed a computational and statistical framework that is based on Metabolic Control Analysis and uses a Monte Carlo method to simulate the uncertainty in the values of the system parameters [Wang, L., Birol, I., Hatzimanikatis, V., 2004. Metabolic control analysis under uncertainty: framework development and case studies. Biophys. J. 87(6), 3750-3763]. In this work, we generalize this framework to incorporate the central cellular processes, such as cell growth, and different bioprocess conditions, such as different types of bioreactors. The framework provides the mathematical basis for the quantification of the interactions between intracellular metabolism and extracellular conditions, and it is readily applicable to the identification of optimal ME targets for the improvement of industrial processes [Wang, L., Hatzimanikatis, V., 2005. Metabolic engineering under uncertainty. II: analysis of yeast metabolism. Submitted].