New software fixes decades-old problem measuring cell growth in bioreactors
Researchers have developed a probabilistic tool that accurately calculates metabolic rates from sparse, noisy data—a chronic headache in biomanufacturing. The advance could help companies optimize production of biologics, vaccines, and fermented products by extracting better insights from existing lab measurements, potentially reducing development costs and time-to-market.
Originaltitel: Overcoming the limits of traditional rate calculations from sparse concentration data: a probabilistic framework for bioprocess modeling.
MetRaC, en probabilistisk modelleringsmetod från Sartorius, löser ett praktiskt problem för bioprocessoptimering: traditionella beräkningsmetoder för tillväxt- och metabola hastigheter misslyckas när data är glest fördelade eller bullrig. Metoden använder Bayesian-inferens och Nested Sampling för att omvandla råa koncentrationsmätningar till pseudo-koncentrationer som korrigerar för volymförändringar i bioreaktorn från tillsatser och provtagningar. Forskarna jämförde MetRaCs prestanda mot konventionella metoder med hjälp av simuleringar och analyserade hur samplingfrekvens, provvolym och mätbrus påverkar noggrannheten. Ramen levererar tillförlitliga metabola hastighetsestimater även under extrem datasparsitet. För FoU-chefer och processutvecklare blir detta relevant: bättre hastighetskalkyl från befintlig data minskar behovet av omfattande provtagningsprotokoll och möjliggör snabbare processkarakterisering. Särskilt värdefull för småskaliga och fed-batch-processer där datakvaliteten ofta är utmanande.
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.