Konstantinov K B
Bayer Corp, Berkeley, California 94710-1986, USA.
Biotechnol Bioeng. 1996 Oct 20;52(2):271-89. doi: 10.1002/bit.260520203.
Advances in bioprocess engineering depends ultimately on the level of understanding and control of the physiological state of the cell population. Process efficiency is strongly influenced by changes in the cellular state which should be monitored, interpreted, and, if possible, properly manipulated. In most control systems this function is not explicitly considered, which hampers process development and optimization. Conventional control logic is based on direct mapping of the growth environment into process efficiency, thereby bypassing the cell state as an intermediate control objective. Today, this limitation is well realized, and explicit monitoring and control of cellular physiology are considered to be among the most challenging tasks of modern bioprocess engineering. We present here a generic methodology for the design of systems capable of performing these advanced monitoring and control functions.The term "physiological state" is quantified by a vector composed of several process variables that convey significant information about cellular state. These variables can be selected among different classes, including specific metabolic rates, metabolic rate ratios, degees of limitation, and others. The real-time monitoring of many of these is possible using commercial sensors. The definition and calculation of representative sets of physiological state variables is demonstrated with examples from several fermentor cultures: recombinant Escherichia coli for phenylalanine production, bioluminescent E. coli (harboring lux genes driven by a heat shock protein promoter) for detection of environmental pollutants, plant cell culture of Perilla frutescensfor anthocyanin production, and perfusion cultures of recombinant mammalian cells (NS0 and CHO) for therapeutic protein production.If the physiological state vector is on-line calculated, the fermentation process can be described by its trajectory in a space defined by the vector components. Then, the goal of the control system is to maintain the physiological state of the cell as close as possible to the trajectory, providing maximum efficiency. A control structure meant to perform this function is proposed, along with the mechanism for its design. In contrast to conventional systems which work in a closed loop in respect to the cell environment, this scheme operates in a closed loop in respect to the cell state. The discussed control concept has been successfully applied to the recombinant phenylalanine production, resulting in physiologically consistent operation, total computer control, and high process efficiency. Initial results from the application of the method to perfusion mammalian cell cultures are also presented.
生物过程工程的进展最终取决于对细胞群体生理状态的理解和控制水平。细胞状态的变化会对过程效率产生强烈影响,因此需要对其进行监测、解读,并在可能的情况下进行适当调控。在大多数控制系统中,并未明确考虑这一功能,这阻碍了过程的开发和优化。传统的控制逻辑基于将生长环境直接映射为过程效率,从而绕过细胞状态这一中间控制目标。如今,人们已充分认识到这一局限性,对细胞生理学进行明确的监测和控制被视为现代生物过程工程中最具挑战性的任务之一。在此,我们提出一种通用方法,用于设计能够执行这些先进监测和控制功能的系统。
“生理状态”这一术语由一个向量量化,该向量由几个能传达细胞状态重要信息的过程变量组成。这些变量可从不同类别中选取,包括特定代谢速率、代谢速率比、限制程度等。利用商业传感器可以对其中许多变量进行实时监测。通过几种发酵罐培养的实例展示了生理状态变量代表性集的定义和计算:用于生产苯丙氨酸的重组大肠杆菌、用于检测环境污染物的发光大肠杆菌(携带由热休克蛋白启动子驱动的lux基因)、用于生产花青素的紫苏植物细胞培养以及用于生产治疗性蛋白质的重组哺乳动物细胞(NS0和CHO)的灌注培养。
如果在线计算生理状态向量,发酵过程可以用其在由向量分量定义的空间中的轨迹来描述。那么,控制系统的目标就是将细胞的生理状态尽可能维持在该轨迹附近,以提供最大效率。本文提出了一种旨在执行此功能的控制结构及其设计机制。与传统系统在细胞环境方面进行闭环操作不同,该方案在细胞状态方面进行闭环操作。所讨论的控制概念已成功应用于重组苯丙氨酸生产,实现了生理上的一致操作、完全计算机控制以及高过程效率。还展示了将该方法应用于灌注哺乳动物细胞培养的初步结果。