Academic Journal
Overcoming the limits of traditional rate calculations from sparse concentration data: a probabilistic framework for bioprocess modeling.
| Title: | Overcoming the limits of traditional rate calculations from sparse concentration data: a probabilistic framework for bioprocess modeling. |
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| Authors: | Richelle A; Sartorius Corporate Research, Brussels, Belgium. anne.richelle@sartorius.com., Andersson D; Sartorius Corporate Research, Umeå, Sweden., Vernersson A; Sartorius Corporate Research, Umeå, Sweden., Cloarec O; Sartorius Corporate Research, Aubagne, France., Trygg J; Sartorius Corporate Research, Umeå, Sweden. |
| Source: | Bioprocess and biosystems engineering [Bioprocess Biosyst Eng] 2026 Aug; Vol. 49 (8), pp. 2147-2161. Date of Electronic Publication: 2026 Jul 13. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101088505 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1615-7605 (Electronic) Linking ISSN: 16157591 NLM ISO Abbreviation: Bioprocess Biosyst Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Berlin, Germany : Springer-Verlag, 2001- |
| MeSH Terms: | Bioreactors* , Models, Biological* , Computer Simulation* , Models, Statistical*, Bayes Theorem ; Algorithms |
| Abstract: | Accurate estimation of growth and metabolic rates is essential for understanding and optimizing bioprocesses, yet traditional methods often fail when faced with sparse or noisy concentration data. We present MetRaC, a probabilistic framework based on Bayesian inference and Nested Sampling that addresses these challenges by integrating biological knowledge directly into the model structure. The approach transforms raw concentration measurements into pseudo-concentrations that account for distortions caused by bioreactor volume changes (e.g., feed additions, sample withdrawals), and models metabolic rates as linear combinations of basis functions to yield continuous rate profiles from discrete data. Using in-silico simulations, we evaluated the framework under a range of experimental conditions and compared its performance with a conventional rate calculation method. We further analyzed the influence of key experimental design parameters - sampling frequency, sample volume, and measurement noise - on both rate estimation accuracy and concentration reconstruction quality. Results demonstrate that the proposed framework delivers accurate, robust metabolic rate estimates even under severe data sparsity and noise, offering a powerful tool for improving bioprocess characterization and optimization. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Conflict of interest: All authors are employees of Sartorius. |
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| Contributed Indexing: | Keywords: Bayesian inference; Bioprocess; Metabolic rate; Nested sampling |
| Entry Date(s): | Date Created: 20260713 Date Completed: 20260730 Latest Revision: 20260802 |
| Update Code: | 20260802 |
| PubMed Central ID: | PMC13424222 |
| DOI: | 10.1007/s00449-026-03382-y |
| PMID: | 42440131 |
| Database: | MEDLINE |
| ISSN: | 1615-7605 |
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| DOI: | 10.1007/s00449-026-03382-y |