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Metabolic Engineering
Published in Jean F. Challacombe, Metabolic Pathway Engineering, 2021
Bioprocess (or biochemical) engineering focuses on the design, construction, and application of processes involving biological organisms or organic molecules. The applications of bioprocess engineering include the mass production of biofuels, food, biopolymers, industrial enzymes, and pharmaceuticals, as well as the development of advanced biotechnology and water treatment processes [49–51]. To develop microbial systems that can effectively produce desired products involves the design and development of microbial cell factories and improved bioprocesses to facilitate the production of industrial compounds [49, 52, 53]. Optimizing a microbial cell might involve adding multiple traits to the genome and/or generating the right conditions to express a desired phenotype. Transcriptome profiling can be implemented in combination with fluxome analysis and this approach has been used to improve the production of microbial natural products for pharmaceutical use [54]. Transcriptome analysis can be used to identify transcriptional regulators, which can be manipulated to optimize the production of bulk chemicals such as succinate [55]. The application of transcriptome analysis in bioprocess engineering provides an increased understanding of cellular regulation on a global level, which can be exploited to understand (and then optimize) the responses of cells to their environment or to genetic perturbations [52].
Hybrid Modeling of Biochemical Processes
Published in Jarka Glassey, Moritz von Stosch, Hybrid Modeling in Process Industries, 2018
Vytautas Galvanauskas, Rimvydas Simutis, Andreas Lübbert
The general aim of bioprocess engineering is to develop and optimize biochemical production processes. Process design, optimization, and control require extensive knowledge of the process. The classical way of representing process knowledge in science and engineering is to use fundamental mathematical models (i.e., based on first principles). These models require a thorough understanding of mechanisms governing the process dynamics. In biochemical processes, however, many essential phenomena are not yet understood in sufficient detail necessary to develop physically based models. Hence, to establish models applicable in practice, additional resources must be exploited (von Stosch et al. 2014a, 2016). Everyday experience shows that a significant amount of quantitative knowledge about the biochemical processes is available that cannot yet be effectively represented in a form of first-principle mathematical models. Thus, it is necessary to look for possibilities to incorporate this knowledge into alternative kinds of numerically evaluable process models by transforming qualitative knowledge to quantitative knowledge. Also, the data from already-running biochemical processes covers a wealth of hidden information about the industrial process. Engineers have been recognizing this and condensing the information within the data in the form of so-called engineering correlations. Since this is extremely time-consuming, most data records have not been sufficiently exploited so far. Experience has shown that neither mathematical process models nor heuristic descriptions alone are sufficient to describe real production processes accurately enough so that an efficient automatic control system for a production-scale bioreactor can be based only on this description. In order to overcome this shortcoming, all available knowledge should be utilized. In particular, the information hidden in the extended measurement data records from the process under consideration must be exploited. Hence, procedures are needed to simultaneously capitalize on the available mathematical modeling knowledge, the information hidden in process data records, and the qualitative knowledge gained by process engineers through their experience. This can be achieved by means of the hybrid modeling technique. The hybrid modeling approach is intended for the simultaneous use of different sources of knowledge. It is expected that the utilization of more-relevant knowledge generally leads to improved accuracy of the process model.
Optimal control of bioprocess systems using hybrid numerical optimization algorithms
Published in Optimization, 2018
Xiang Wu, Kanjian Zhang, Ming Cheng
Systems engineering methods are increasingly applied in the bioprocess industries, such as food, biotechnological, environmental and pharmaceutical [1,2]. In order to raise the productivity, profitability and/or efficiency of bioprocesses, many contributions have been devoted to their improvement using computer aided bioprocess engineering approaches. Thus, mathematical modeling of bioprocess systems, dynamic optimization and system control have been becoming basic tools for optimization design and operate production facilities in the bioprocess industry sector [3–10]. However, because of the nonlinear characteristic of the bioprocesses and the existence of equality and/or inequality continuous-time constraints, it is very difficult to choose the optimal operating strategies. One of the effective approaches to solve such problems is the model-based optimal control numerical algorithm, which has been receiving many attentions [11,12].