Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Mar 13;6(1):6.
doi: 10.1038/s41540-020-0127-y.

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

Anne Richelle  1 , Blandine David  2 , Didier Demaegd  2 , Marianne Dewerchin  2 , Romain Kinet  2 , Angelo Morreale  2 , Rui Portela  2 , Quentin Zune  2 , Moritz von Stosch  3
Affiliations
Review

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

Anne Richelle et al. NPJ Syst Biol Appl. .

Abstract

In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. From experimental data to bioprocess improvement.
Systematic workflow using data extracted from real-time monitoring to tailor genome-scale biological networks to core metabolic models that can be combined with artificial intelligence and machine learning tools for an effective implementation of control and optimization strategies.
See this image and copyright information in PMC

References

    1. Sadowski MI, Grant C, Fell TS. Harnessing QbD, programming languages, and automation for reproducible biology. Trends Biotechnol. 2016;34:214–227. doi: 10.1016/j.tibtech.2015.11.006. - DOI - PubMed
    1. Chen C, Le H, Goudar CT. Integration of systems biology in cell line and process development for biopharmaceutical manufacturing. Biochem. Eng. J. 2016;107:11–17. doi: 10.1016/j.bej.2015.11.013. - DOI
    1. Kubinyi, H. In Computer Applications in Pharmaceutical Research and Development (eds Wang, B. & Ekins, S.) 377–424 (John Wiley & Sons, 2006).
    1. Schmidt BJ, Papin JA, Musante CJ. Mechanistic systems modeling to guide drug discovery and development. Drug Discov. Today. 2013;18:116–127. doi: 10.1016/j.drudis.2012.09.003. - DOI - PMC - PubMed
    1. Choi KR, et al. Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering. Trends Biotechnol. 2019;37:817–837. doi: 10.1016/j.tibtech.2019.01.003. - DOI - PubMed

Publication types

Substances