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Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1‐antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by‐products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α‐ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.

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In the search for alternatives to chlorine-containing gases, tetrafluoroethane, CF3CH2F (R134a), a widely used refrigerant gas, has been recognized as a promising substitute for dichlorodifluoromethane, CCl2F2 (R12). When R12 is replaced by R134a, the global warming potential drops from 8100 to 1430, the ozone depletion potential changes from 1 to 0, and the atmospheric lifetime decreases from 100 to 14 years. Electron interactions in the gas phase play a fundamental role in the atmospheric sciences. Here, we present a detailed study on electron-driven fragmentation pathways of CF3CH2F, in which we have investigated processes induced by both electron ionization and electron attachment. The measurements allow us to report the ion efficiency curves for ion formation in the energy range of 0 up to 25 eV. For positive ion formation, R134a dissociates into a wide assortment of ions, in which CF3 + is observed as the most abundant out of seven ions with a relative intensity above 2%. The results are supported by quantum chemical calculations based on bound state techniques, electron-impact ionization models, and electron-molecule scattering simulations, showing a good agreement. Moreover, the experimental first ionization potential was found at 13.10 ± 0.17 eV and the second at around 14.25 eV. For negative ion formation, C2F3 – was detected as the only anion formed, above 8.3 eV. This study demonstrates the role of electrons in the dissociation of R134a, which is relevant for an improvement of the refrigeration processes as well as in atmospheric chemistry and plasma sciences.

Summary Here we present sbml2hyb, an easy-to-use standalone Python tool that facilitates the conversion of existing mechanistic models of biological systems in Systems Biology Markup Language (SBML) into hybrid semiparametric models that combine mechanistic functions with machine learning (ML). The so-formed hybrid models can be trained and stored back in databases in SBML format. The tool supports a user-friendly export interface with an internal format validator. Two case studies illustrate the use of the sbml2hyb tool. Additionally, we describe HMOD, a new model format designed to support and facilitate hybrid models building. It aggregates the mechanistic model information with the ML information and follows as close as possible the SBML rules. We expect the sbml2hyb tool and HMOD to greatly facilitate the widespread usage of hybrid modeling techniques for biological systems analysis. Availability and implementation The Python interface, source code and the example models used for the case studies are accessible at: https://github.com/r-costa/sbml2hyb. Supplementary information Supplementary data are available at Bioinformatics online.