journal article·2025-07-02Open access

Standard Calibration and On-Line Estimation of Cell-Specific Growth and Protein Synthesis Rates in Chi.Bio Mini-Bioreactors

Harold José Díaz-Iza, Andrés Arboleda-García, Yadira Boada, Alejandro Vignoni, Jesús Picó

Applied Sciences· Volume 15· Issue 13

Abstract

Low-cost mini-bioreactor platforms are becoming increasingly important in synthetic biology and biotechnology for the characterization of genetic constructs and their initial scaling-up to large-scale cultures. Key process variables are the specific growth rate of cells and the synthesis rate of reporter proteins associated with transcriptional units of interest. These variables are of paramount importance for the characterization of gene synthetic circuits. In addition, their on-line estimation can be used for real-time monitoring of cells’ metabolic state and gene circuit dynamic performance, thus allowing for on-line decision taking. In this work, we first describe a procedure for the calibration of absorbance and fluorescence measurements, ensuring standardized and comparable units across different experimental setups and measurement devices. Then, we implement an observer-based software sensor that uses the calibrated on-line measurements to simultaneously estimate the specific growth and protein synthesis rates. We implemented the calibration procedure and software sensor in a Chi.Bio mini-bioreactor platform. The experimental results show very good performance and pave the way for the use of mini-bioreactor platforms in the real-time characterization of gene synthetic circuits under dynamically regulated time-varying complex scenarios.
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Research context

OpenAlex topics: Viral Infectious Diseases and Gene Expression in Insects · 3D Printing in Biomedical Research · Gene Regulatory Network Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Synthetic Biology and Biosystems Control Lab, Institut d’Automática e Informática Industrial, Universitat Politècnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain

Centro Universitario EDEM, Escuela de Empresarios, La Marina de València, Muelle de la Aduana s/n, 46024 Valencia, Spain

Funding

MCIN/AEI/10.13039/501100011033 · ”Contrato predoctoral para la formación de doctores, convocatoria 2021” · Universitat Politècnica de València · Acceso al Sistema Español de Ciencia e Innovación · Secretaría de Educación Superior, Ciencia, Tecnología e Innovación of Ecuador

References (46)

Show source-provided references
1. Microbial production of advanced biofuels10.1038/s41579-021-00577-w ↗
2. Liao, Q., Chang, J.S., Herrmann, C., and Xia, A. (2018). Bioreactors for Microbial Biomass and Energy Conversion, Springer.10.1007/978-981-10-7677-0 ↗
3. Palladino, F., Marcelino, P.R.F., Schlogl, A.E., José, Á.H.M., Rodrigues, R.d.C.L.B., Fabrino, D.L., Santos, I.J.B., and Rosa, C.A. (2024). Bioreactors: Applications and Innovations for a Sustainable and Healthy Future—A Critical Review. Appl. Sci., 14.10.3390/app14209346 ↗
4. Mandenius, C.F. (2016). Bioreactors: Design, Operation and Novel Applications, John Wiley & Sons.10.1002/9783527683369 ↗
5. Drobnjakovic, M., Hart, R., Kulvatunyou, B.S., Ivezic, N., and Srinivasan, V. (2023). Current challenges and recent advances on the path towards continuous biomanufacturing. Biotechnol. Prog., 39.10.1002/btpr.3378 ↗
6. Global stabilisation of continuous bioreactors: Tools for analysis and design of feeding laws10.1016/j.automatica.2017.12.041 ↗
7. Yamanè, T., and Shimizu, S. (1984). Fed-batch techniques in microbial processes. Bioprocess Parameter Control, Springer.10.1007/BFb0006382 ↗
8. Bhatt, A.K., Bhatia, R.K., and Bhalla, T.C. (2023). Chapter 21—Fundamentals of fermentation technology. Basic Biotechniques for Bioprocess and Bioentrepreneurship, Academic Press.
9. Nonlinear PI control of fed-batch processes for growth rate regulation10.1016/j.jprocont.2012.02.011 ↗
10. Kelly, J.R., Rubin, A.J., Davis, J.H., Ajo-Franklin, C.M., Cumbers, J., Czar, M.J., de Mora, K., Glieberman, A.L., Monie, D.D., and Endy, D. (2009). Measuring the activity of BioBrick promoters using an in vivo reference standard. J. Biol. Eng., 3.10.1186/1754-1611-3-4 ↗
11. Reproducibility of synthetic gene circuits in closely related bacterial strains
12. Burden-driven feedback control of gene expression10.1038/nmeth.4635 ↗
13. Noise propagation in synthetic gene circuits for metabolic control10.1021/sb400126a ↗
14. Deconvolving gene expression noise into physical dynamics of cell populations
15. Interdependence of cell growth and gene expression10.1126/science.1192588 ↗
16. Control theory meets synthetic biology10.1098/rsif.2016.0380 ↗
17. Hemmerich, J., Noack, S., Wiechert, W., and Oldiges, M. (2018). Microbioreactor Systems for Accelerated Bioprocess Development. Biotechnol. J., 13.10.1002/biot.201700141 ↗
18. Integration of pH control into Chi. Bio reactors and demonstration with small-scale enzymatic poly (ethylene terephthalate) hydrolysis10.1021/acs.biochem.4c00149 ↗
19. Beal, J., Telmer, C.A., Vignoni, A., Boada, Y., Baldwin, G.S., Hallett, L., Lee, T., Selvarajah, V., Billerbeck, S., and Brown, B. (2022). Multicolor plate reader fluorescence calibration. Synth. Biol., 7.10.1093/synbio/ysac010 ↗
20. Steel, H., Habgood, R., Kelly, C.L., and Papachristodoulou, A. (2020). In situ characterisation and manipulation of biological systems with Chi.Bio. PLoS Biol., 18.10.1371/journal.pbio.3000794 ↗
21. Delvigne, F., Goffin, P., and Toye, D. (2020). Mini-bioreactor platforms: A powerful tool for bioprocess development. Biochem. Eng. J., 160.
22. Mini-bioreactor systems for accelerated development of microbial production processes
23. Recent progress in portable fluorescence sensors10.1149/1945-7111/abd494 ↗
24. Telle, H.H., and Ureña, Á.G. (2018). Laser Spectroscopy and Laser Imaging: An Introduction, CRC Press.10.1201/9781315156989 ↗
25. Standardization in synthetic biology
26. Beal, J., Farny, N.G., Haddock-Angelli, T., Selvarajah, V., Baldwin, G.S., Buckley-Taylor, R., Gershater, M., Kiga, D., Marken, J., and Sanchania, V. (2020). Robust estimation of bacterial cell count from optical density. Commun. Biol., 3.10.1101/803239 ↗
27. Beal, J., Haddock-Angelli, T., Gershater, M., de Mora, K., and Lizarazo, M. (2016). Reproducibility of Fluorescent Expression from Engineered Biological Constructs in E. coli. PLoS ONE, 11.10.1371/journal.pone.0157255 ↗
28. Characterization of gene circuit parts based on multiobjective optimization by using standard calibrated measurements10.1002/cbic.201900272 ↗
29. Tiwari, K., Kananathan, S., Roberts, M.G., Meyer, J.P., Sharif Shohan, M.U., Xavier, A., Maire, M., Zyoud, A., Men, J., and Ng, S. (2021). Reproducibility in systems biology modelling. Mol. Syst. Biol., 17.10.15252/msb.20209982 ↗
30. Medley, J.K., Choi, K., König, M., Smith, L., Gu, S., and Sauro, H.M. (2018). Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology. PLoS Comput. Biol., 14.10.1371/journal.pcbi.1006220 ↗
31. Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments10.1038/s41467-022-34857-7 ↗
32. Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response10.1073/pnas.2115032119 ↗
33. On-line estimation in bioreactors: A review10.1515/REVCE.2000.16.4.311 ↗
34. Soft sensors development for on-line bioreactor state estimation10.1016/S0098-1354(00)00489-0 ↗
35. Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms10.1007/s00449-012-0752-y ↗
36. Torres, J., Reza, V., and Guerrero, J. (2025). An Extended Adaptive Generalized Second-Order Algorithm for Simultaneous Estimation of Reaction Rates and an Input Disturbance in Bioprocesses. Int. J. Robust Nonlinear Control.10.1002/rnc.8035 ↗
37. Mira, P., Yeh, P., and Hall, B.G. (2022). Estimating microbial population data from optical density. PLoS ONE, 17.10.1371/journal.pone.0276040 ↗
38. Valencia UPV 2018 IGEM Team (2025, June 21). Printeria Part Collection [Online]. Available online: http://2018.igem.org/Team:Valencia_UPV/Part_Collection.
39. IGEM Foundation (2025, June 15). Part Registry [Online]. Available online: https://parts.igem.org.
40. Engler, C., Kandzia, R., and Marillonnet, S. (2008). A one pot, one step, precision cloning method with high throughput capability. PLoS ONE, 3.10.1371/journal.pone.0003647 ↗
41. GoldenBraid 2.0: A comprehensive DNA assembly framework for plant synthetic biology10.1104/pp.113.217661 ↗
42. iGEM Registry (2025, June 21). Part:BBa_K2656105-sfGFP Expression Device (Strong). Available online: https://parts.igem.org/Part:BBa_K2656105.
43. Mini-bioreactor for parts characterization of a biological circuit: Absorbance and Fluorescence measurement calibration, and online growth rate estimation10.1016/j.ifacol.2024.10.017 ↗
44. González-Cebrián, A., Borràs-Ferrís, J., Boada, Y., Vignoni, A., Ferrer, A., and Picó, J. (2023). PLATERO: A calibration protocol for plate reader green fluorescence measurements. Front. Bioeng. Biotechnol., 11.10.3389/fbioe.2023.1104445 ↗
45. Morales-Brotons, D., Vogels, T., and Hendrikx, H. (2024). Exponential moving average of weights in deep learning: Dynamics and benefits. arXiv.
46. How to scale your EMA