Estimating Growth Rate from Sparse and Noisy Data: A Bayesian Approach

Accurate estimation of growth rates from sparse and noisy concentration data is a significant challenge in bioprocess modeling. This paper presents a method that utilizes a modular non-linear interpolation framework combined with Bayesian parameter inference to address this issue. By incorporating prior knowledge of cell culture dynamics with differentiable basis functions, our approach generates credibility intervals for growth rates, enhancing the reliability of predictions. We validated the performance of our method using growth rate data generated in silico and demonstrated its application on two in vitro datasets, showcasing its robustness across various measurement conditions and practical applicability. Results indicate improvements in the reliability and credibility of predictions compared to traditional methods, making this framework a valuable resource for accurate growth rate estimations.