Bridging Offline Functional Model Carrying Aging-Specific Growth Rate Information and Recombinant Protein Expression: Entropic Extension of Akaike Information Criterion

Abstract

This study presents a mathematical model of recombinant protein expression, including its development, selection, and fitting results based on seventy fed-batch cultivation experiments from two independent biopharmaceutical sites. To resolve the overfitting feature of the Akaike information criterion, we proposed an entropic extension, which behaves asymptotically like the classical criteria. Estimation of recombinant protein concentration was performed with pseudo-global optimization processes while processing offline recombinant protein concentration samples. We show that functional models including the average age of the cells and the specific growth at induction or the start of product biosynthesis are the best descriptors for datasets. We also proposed introducing a tuning coefficient that would force the modified Akaike information criterion to avoid overfitting when the designer requires fewer model parameters. We expect that a lower number of coefficients would allow the efficient maximization of target microbial products in the upstream section of contract development and manufacturing organization services in the future. Experimental model fitting was accomplished simultaneously for 46 experiments at the first site and 24 fed-batch experiments at the second site. Both locations contained 196 and 131 protein samples, thus giving a total of 327 target product concentration samples derived from the bioreactor medium.

Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate

“… where x is the biomass concentration (g kg −1 ), t is the time (h), OUR is the oxygen uptake rate (g kg −1 h −1 ), and (g g −1 ) and β (g g −1 h −1 ) are the stoichiometry parameters that determine the growth and maintenance properties of biomass. The SGR estimation procedure suggested in this study originates from biomass concentration estimates that were based on a bioreactor exhaust gas analysis [41] , [28] . The chosen method exhibited stability in estimating biomass concentrations at various cultivation conditions.…”

An Approach for the Estimation of Concentrations of Soluble Compounds in E. coli Bioprocesses

“…Such an assumption suggests that these parameters are influenced not only by the immediate state of the cells but also the average age of the individual cells which are present. The latent average age points to the active biomass concentration within the population [37,38]:…”

Non-Invasive Estimation of Acetates Using Off-Gas Information for Fed-Batch E. coli Bioprocess

“…Previous studies showed that specific growth rate µ is one of the best descriptors for estimating bioprocess parameters [10]. This parameter and oxygen uptake rate (OUR) carry much information about the growth and life of the cell [11,12].…”

“…Substrate consumption and broth weight give model information about biomass growth, and broth weight also gives information about the dilution effect of substrate feeding. Additionally, broth and consumed substrate weight with supplementary aging-related information are separate inputs introducing the cumulative regularization [10]. The selected inputs were (Table 1):…”