Affiliation:
1. State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
Funder
National Key Research and Development Program of China
Shanghai Rising-Star Program
National Natural Science Foundation of China
Reference138 articles.
1. COVID-19 and the Bioeconomy;Gropp;Bioscience,2020
2. Sustainability and Life Cycle Assessment in Industrial Biotechnology: A Review of Current Approaches and Future Needs;Frohling;Adv. Biochem. Eng. Biotechnol.,2020
3. Venture Capital’s Role in Financing Innovation: What We Know and How Much We Still Need to Learn;Lerner;J. Econ. Perspect.,2020
4. Reinforcement Learning for Bioretrosynthesis;Koch;ACS Synth. Biol.,2020
5. Computational fluid dynamics for improved bioreactor design and 3D culture;Hutmacher;Trends Biotechnol.,2008
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