Data-driven inference of bioprocess models: A low-rank matrix approximation approach

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Guilherme A. Pimentel, Laurent Dewasme, Alain Vande Wouwer
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引用次数: 0

Abstract

Following the recent advent of Process Analytical Technologies, dataset production has undergone significant leverage. In this new abundance of data, isolating meaningful, informative content is critical for process dynamic modeling. This paper proposes a data-driven algorithm based on low-rank matrix approximation, the so-called successive projection algorithm, to retrieve a minimal set of macroscopic reactions, the corresponding stoichiometry, and a consistent kinetic model structure from the measurements of the trajectories of the species concentrations during cultures in a bioreactor. The proposed method is successfully validated in simulation, considering a case study related to monoclonal antibody (MAb) production with hybridoma cell cultures.

数据驱动的生物过程模型推断:低阶矩阵近似方法
随着近来过程分析技术的出现,数据集的生产也发生了巨大的变化。在新的大量数据中,分离出有意义的信息内容对于过程动态建模至关重要。本文提出了一种基于低秩矩阵近似的数据驱动算法,即所谓的连续投影算法,以从生物反应器培养过程中物种浓度的测量轨迹中检索出一组最小的宏观反应、相应的化学计量学和一致的动力学模型结构。考虑到与杂交瘤细胞培养生产单克隆抗体(MAb)有关的案例研究,所提出的方法在模拟中得到了成功验证。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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