Mechanism-based and data-driven modeling in cell-free synthetic biology

Author:

Yurchenko Angelina123ORCID,Özkul Gökçe123,van Riel Natal A. W.456,van Hest Jan C. M.78ORCID,de Greef Tom F. A.123910ORCID

Affiliation:

1. Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

2. Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

3. Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

4. Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

5. Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands

6. Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands

7. Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

8. Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

9. Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands

10. Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands

Abstract

This highlight discusses state-of-the-art mechanism-based modeling and machine-learning approaches in cell-free synthetic biology and their potential for designing and optimizing novel genetic circuits.

Funder

Research Executive Agency

Publisher

Royal Society of Chemistry (RSC)

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