Osmolality effects on CHO cell growth, cell volume and antibody productivity and glycosylation

The addition of nutrients and accumulation of metabolites in a fed-batch culture of Chinese hamster ovary (CHO) cells leads to an increase in extracellular osmolality in late stage culture. Herein, we explore the effect of osmolality on CHO cell growth, specific monoclonal antibody (mAb) productivity and glycosylation achieved with the addition of NaCl or the supplementation of a commercial feed. Although both methods lead to an increase in specific antibody productivity, they have different effects on cell growth and antibody production. Osmolality modulation using NaCl up to 470 mOsm kg−1 had a consistently positive effect on specific antibody productivity and titre. The addition of the commercial feed achieved variable results: specific mAb productivity was increased, yet cell growth rate was significantly compromised at high osmolality values. As a result, Feed C addition to 410 mOsm kg−1 was the only condition that achieved a significantly higher mAb titre compared to the control. Additionally, Feed C supplementation resulted in a significant reduction in galactosylated antibody structures. Cell volume was found to be positively correlated to osmolality; however, osmolality alone could not account for observed changes in average cell diameter without considering cell cycle variations. These results help delineate the overall effect of osmolality on titre and highlight the potentially negative effect of overfeeding on cell growth

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

Optimized kinematics enable both aerial and aquatic propulsion from a single three-dimensional flapping wing

Flapping wings in nature demonstrate a large force envelope, with capabilities far beyond the traditional limits of static airfoil section coefficients. Puffins, murres, and other auks particularly showcase this effect, as they are able to generate both enough thrust to swim and enough lift to fly, using the same wing, purely by changing the wing motion trajectory. The wing trajectory is therefore an additional design criterion to be optimized along with traditional aircraft parameters and could open the door to dual aerial-aquatic robotic vehicles. In this paper we showcase one realization of a three-dimensional flapping-wing actuation system that reproduces the force coefficients necessary for dual aerial-aquatic flight. The wing apparatus oscillates by the root and employs an active upstream and downstream sweep degree of freedom. We analyze two types of motions in detail: aerial motions where the wing tip moves upstream during the power stroke of each flapping cycle and aquatic motions where the wing tip moves downstream during the power stroke. We design these aerial and aquatic flapping-wing trajectories using an experiment-coupled optimization routine, allowing control of the unsteady forces throughout each flapping cycle. Additionally, we elucidate the wakes of these complex wing trajectories using dye visualization, correlating the wake vortex structures with simultaneous experiment force measurements. After optimization, the wing trajectories generate the large force envelope necessary for propulsion in both fluid media and furthermore demonstrate improved control over the unsteady wake

Model-based optimization of antibody galactosylation in CHO cell culture

Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses

Effects of adiponectin on breast cancer cell growth and signaling

Obesity is a risk factor for postmenopausal breast cancer. Adiponectin/Acrp30 is lower in obese individuals and may be negatively regulating breast cancer growth. Here we determined that five breast cancer cell lines, MDA-MB-231, MDA-MB-361, MCF-7, T47D, and SK-BR-3, expressed one or both of the Acrp30 receptors. In addition, we found that the addition of Acrp30 to MCF-7, T47D, and SK-BR-3 cell lines inhibited growth. Oestrogen receptor (ER) positive MCF-7 and T47D cells were inhibited at lower Acrp30 concentrations than ER-negative SK-BR-3 cells. Growth inhibition may be related to apoptosis since PARP cleavage was increased by Acrp30 in the ER-positive cell lines. To investigate the role of ER in the response of breast cancer cells to Acrp30, we established the MDA-ERα7 cell line by insertion of ER-α into ER-α-negative MDA-MB-231 cells. This line readily formed tumours in athymic mice and was responsive to oestradiol in vivo. In vitro, MDA-ERα7 cells were growth inhibited by globular Acrp30 while the parental cells were not. This inhibition appeared to be due to blockage of JNK2 signalling. These results provide information on how obesity may influence breast cancer cell proliferation and establish a new model to examine interactions between ER and Acrp30

Harnessing the potential of artificial neural networks for predicting protein glycosylation

Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation

Strategic framework for parameterization of cell culture models

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development

Analysis of Ultrasonic Waves in Arbitrarily Oriented or Rotating Anisotropic Thin Materials

Much work has been performed modeling thin anisotropic materials for use in nondestructive testing with plane wave excitation and detection [1–5]. There has been interest in the development of dispersion curve inversion and group velocity inversion procedures [6–8]. The general approach in many of these techniques is to align the excitation and detection along a known crystal axis, and analyze the detected signal to determine elastic constants, bond quality, thickness, or any number of other parameters [9–12]. Unfortunately, most of these approaches will fail to perform adequately when the target’s crystal axis is rotating with respect to the excitation axis, or when the coordinate frame of the crystal axis is unknown.</p