Relativistic Binaries in Globular Clusters

Galactic globular clusters are old, dense star systems typically containing 10\super{4}--10\super{7} stars. As an old population of stars, globular clusters contain many collapsed and degenerate objects. As a dense population of stars, globular clusters are the scene of many interesting close dynamical interactions between stars. These dynamical interactions can alter the evolution of individual stars and can produce tight binary systems containing one or two compact objects. In this review, we discuss theoretical models of globular cluster evolution and binary evolution, techniques for simulating this evolution that leads to relativistic binaries, and current and possible future observational evidence for this population. Our discussion of globular cluster evolution will focus on the processes that boost the production of hard binary systems and the subsequent interaction of these binaries that can alter the properties of both bodies and can lead to exotic objects. Direct {\it N}-body integrations and Fokker--Planck simulations of the evolution of globular clusters that incorporate tidal interactions and lead to predictions of relativistic binary populations are also discussed. We discuss the current observational evidence for cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries as well as possible future detection of relativistic binaries with gravitational radiation.Comment: 88 pages, 13 figures. Submitted update of Living Reviews articl

Applying mechanistic models in bioprocess development.

The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Kappeli (Biotechnol Bioeng 28: 927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well

Using simple models to describe the kinetics of growth, glucose consumption, and monoclonal antibody formation in naive and infliximab producer CHO cells

Despite their practical and commercial relevance, there are few reports on the kinetics of growth and production of Chinese hamster ovary (CHO) cells—the most frequently used host for the industrial production of therapeutic proteins. We characterize the kinetics of cell growth, substrate consumption, and product formation in naive and monoclonal antibody (mAb) producing recombinant CHO cells. Culture experiments were performed in 125 mL shake flasks on commercial culture medium (CD Opti CHO™ Invitrogen, Carlsbad, CA, USA) diluted to different glucose concentrations (1.2–4.8 g/L). The time evolution of cell, glucose, lactic acid concentration and monoclonal antibody concentrations was monitored on a daily basis for mAb-producing cultures and their naive counterparts. The time series were differentiated to calculate the corresponding kinetic rates (rx = d[X]/dt; rs = d[S]/dt; rp = d[mAb]/dt). Results showed that these cell lines could be modeled by Monod-like kinetics if a threshold substrate concentration value of [S]t = 0.58 g/L (for recombinant cells) and [S]t = 0.96 g/L (for naïve cells), below which growth is not observed, was considered. A set of values for μmax, and Ks was determined for naive and recombinant cell cultures cultured at 33 and 37 °C. The yield coefficient (Yx/s) was observed to be a function of substrate concentration, with values in the range of 0.27–1.08 × 107 cell/mL and 0.72–2.79 × 106 cells/mL for naive and recombinant cultures, respectively. The kinetics of mAb production can be described by a Luedeking–Piret model (d[mAb]/dt = αd[X]/dt + β[X]) with values of α = 7.65 × 10−7 µg/cell and β = 7.68 × 10−8 µg/cell/h for cultures conducted in batch-agitated flasks and batch and instrumented bioreactors operated in batch and fed-batch mode