Enhanced understanding of protein glycosylation in CHO cells through computational tools and experimentation

Chinese hamster ovary (CHO) cells are the workhorse of the multibillion-dollar biopharmaceuticals industry. They have been extensively harnessed for recombinant protein synthesis, as they exhibit high titres and human-like post translational modifications (PTM), such as protein N-linked glycosylation. More specifically, N-linked glycosylation is a crucial PTM that includes the addition of an oligosaccharide in the backbone of the protein and strongly affects therapeutic efficacy and immunogenicity. In addition, the Quality by Design (QbD) paradigm that is broadly applied in academic research, necessitates a comprehensive understanding of the underlying biological relationships between the process parameters and the product quality attributes. To that end, computational tools have been vastly employed to elucidate cellular functions and predict the effect of process parameters on cell growth, product synthesis and quality. This thesis reports several advancements in the use of mathematical models for describing and optimizing bioprocesses. Firstly, a kinetic mathematical model describing CHO cell growth, metabolism, antibody synthesis and N-linked glycosylation was proposed, in order to capture the effect of galactose and uridine supplementation on cell growth and monoclonal antibody (mAb) glycosylation. Subsequently, the model was utilized to optimize galactosylation, a desired quality attribute of therapeutic mAbs. Following the QbD paradigm for ensuring product titre and quality, the kinetic model was subsequently used to identify an in silico Design Space (DS) that was also experimentally verified. An elaborate parameter estimation methodology was also developed in order to adapt the existing model to data from a newly introduced CHO cell line, without altering model structure. In an effort to reduce the burden of parameter estimation, the N-linked glycosylation submodel was replaced with an artificial neural network that was used as a standalone machine learning algorithm to predict the effect of feeding alterations and genetic engineering on the glycan distribution of several therapeutic proteins. In addition, a hybrid model configuration (HyGlycoM) incorporating the ANN-glycosylation model was also formulated to link extracellular process conditions to glycan distribution. The latter was found to outperform its fully kinetic equivalent when compared to experimental data. Finally, a comprehensive investigation of mAb galactosylation bottlenecks was carried out. Five fed-batch experiments with different concentrations of galactose and uridine supplemented throughout the culturing period, were carried out and were found to present similar mAb galactosylation. In order to identify the bottlenecks that limit galactosylation, further experimental analysis, including the investigation of glycans microheterogeneity of CHO host cell proteins (HCPs), was conducted. The experimental results were used to parameterize a novel and significant extension of the kinetic glycosylation model that simultaneously describes the N-linked glycosylation of both HCPs and the mAb product. Flux balance analysis was also used to analyse carbon and nitrogen metabolism using the experimental amino acid concentration profiles. In addition to the expression levels of the beta-1,4-galactosyltransferase enzyme, constraints imposed by the transport of the galactosylation sugar donor in the Golgi compartments and the consumption of resources towards HCPs glycosylation, were found to considerably influence mAb galactosylation.Open Acces

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

Unsteady radial transport in a transonic compressor stage

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1989.Includes bibliographical references (v.2, leaves 212-219).by Petros Anestis Kotidis.Ph.D

Prevention of Anastomotic Leak in Minimally Invasive Esophagectomy: The Role of Anastomotic Technique and Adjuvant Surgical Strategies

Ivor-Lewis esophagectomy is followed by a considerable anastomotic leakage rate, which is a potentially fatal complication, followed by increased morbidity and mortality. The emergence of minimally invasive surgery led to a wide variety of anastomotic techniques, three of which are mainly preferred. Hand-sewn anastomoses can be performed in an end-to-end or end-to-side manner, while stapled end-to-end or end-to-side anastomoses are conducted either as circular-stapled anastomoses using a transorally inserted anvil (Orvil™) or as hand-sewn purse-string stapled anastomoses. In addition, side-to-side esophagogastrostomy with a linear stapler is presented as a promising technique. Hybrid techniques are also reported. No consensus has been achieved upon optimal technique and the decision relies on surgeon preference and skills, cost, and length of the available conduit. Furthermore, numerous techniques have been proposed to prevent anastomotic leakage (AL), including appropriate submucosa apposition, omentoplasty of the anastomosis, wide gastric and duodenal mobilization, sufficient esophageal hiatus enlargement, gentle conduit manipulation, reinforcement of staple line, intraoperative fluorescence angiography, as well as preoperative ligation of the left gastric artery. This chapter aims to provide a critical appraisal of the various anastomotic techniques and the tips and tricks described for reducing the anastomotic leak rate during minimally invasive Ivor-Lewis esophagectomy

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

Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data

Cardiovascular drug therapy for human newborn: review of pharmacodynamic data

BACKGROUND: Circulatory failure in preterm and term newborn infants is commonly treated with inotropes or vasoactive medications. In this structured literature review, the available data on pharmacodynamic effects of the inotropes adrenaline, dobutamine, dopamine, levosimendan, milrinone, noradrenaline, and the vasoactive drugs vasopressin and hydrocortisone are presented. METHODS: Structured searches were conducted to identify relevant articles according to pre-defined inclusion criteria which were human clinical trials published after 2000. RESULTS: Out of 101 identified eligible studies only 22 studies met the criteria for evidence based practice guidelines level I to IV. The most prevalent pharmacodynamic effects were increase in blood pressure and/or heart rate, which were also the most frequently studied circulatory parameters. CONCLUSION: This review demonstrates the need for further systematic studies on all reviewed drugs with incorporation of novel non-invasive biomarkers in this vulnerable patient group, for more timely and appropriate treatment for clinical efficacy

Data Reduction Techniques for Sensor Networks

We are inevitably moving into a realm where small and inexpensive wireless devices would be seamlessly embedded in the physical world and form a wireless sensor network in order to perform complex monitoring and computational tasks. Such networks pose new challenges in data processing and dissemination due to the conflict between (i) the abundance of information that can be collected and processed in a distributed fashion among thousands of nodes and (ii) the limited resources (bandwidth, energy) that such devices possess. In this paper we propose a new data reduction technique that exploits the correlation and redundancy among multiple measurements on the same sensor and achieves high degree of data reduction while managing to capture even the smallest details of the recorded measurements. The key to our technique is the base signal, a series of values extracted from the real measurements, used for encoding piece-wise linear correlations among the collected data values. We provide efficient algorithms for extracting the base signal features from the data and for encoding the measurements using these features. Our experiments demonstrate that our method by far outperforms standard approximation techniques like Wavelets, Histograms and the Discrete Cosine Transform, on a variety of error metrics and for real datasets from different domains. (UMIACS-TR-2003-80

Another Outlier Bites the Dust: Computing Meaningful Aggregates in Sensor Networks

Abstract — Recent work has demonstrated that readings pro-vided by commodity sensor nodes are often of poor quality. In order to provide a valuable sensory infrastructure for monitoring applications, we first need to devise techniques that can withstand “dirty ” and unreliable data during query processing. In this paper we present a novel aggregation framework that detects suspicious measurements by outlier nodes and refrains from incorporating such measurements in the computed aggregate values. We consider different definitions of an outlier node, based on the notion of a user-specified minimum support, and discuss techniques for properly routing messages in the network in order to reduce the bandwidth consumption and the energy drain during the query evaluation. In our experiments using real and synthetic traces we demonstrate that: (i) a straightfor-ward evaluation of a user aggregate query leads to practically meaningless results due to the existence of outliers; (ii) our techniques can detect and eliminate spurious readings without any application specific knowledge of what constitutes normal behavior; (iii) the identification of outliers, when performed inside the network, significantly reduces bandwidth and energy drain compared to alternative methods that centrally collect and analyze all sensory data; and (iv) we can significantly reduce the cost of the aggregation process by utilizing simple statistics on outlier nodes and reorganizing accordingly the collection tree. I