Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression

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Using CFD simulations and statistical analysis to correlate oxygen mass transfer coefficient to both geometrical parameters and operating conditions in a stirred-tank bioreactor

Optimization of a bioreactor design can be an especially challenging process. For instance, testing different bioreactor vessel geometries and different impeller and sparger types, locations, and dimensions can lead to an exceedingly large number of configurations and necessary experiments. Computational fluid dynamics (CFD), therefore, has been widely used to model multiphase flow in stirred-tank bioreactors to minimize the number of optimization experiments. In this study, a multiphase CFD model with population balance equations are used to model gas–liquid mixing, as well as gas bubble distribution, in a 50 L single-use bioreactor vessel. The vessel is the larger chamber in an early prototype of a multichamber bioreactor for mammalian cell culture. The model results are validated with oxygen mass transfer coefficient (k_La) measurements within the prototype. The validated model is projected to predict the effect of using ring or pipe spargers of different sizes and the effect of varying the impeller diameter on k_La. The simulations show that ring spargers result in a superior k_La compared to pipe spargers, with an optimum sparger-to-impeller diameter ratio of 0.8. In addition, larger impellers are shown to improve k_La. A correlation of k_La is presented as a function of both the reactor geometry (i.e., sparger-to-impeller diameter ratio and impeller-to-vessel diameter ratio) and operating conditions (i.e., Reynolds number and gas flow rate). The resulting correlation can be used to predict k_La in a bioreactor and to optimize its design, geometry, and operating conditions.     

Scale-down model qualification of ambr® 250 high-throughput mini-bioreactor system for two commercial-scale mAb processes

Recent advances in high-throughput (HTP) automated mini-bioreactor systems have significantly improved development timelines for early-stage biologic programs. Automated platforms such as the ambr® 250 have demonstrated the ability, using appropriate scale-down approaches, to provide reliable estimates of process performance and product quality from bench to pilot scale, but data sets comparing to large-scale commercial processes (>10,000 L) are limited. As development moves toward late stages, specifically process characterization (PC), a qualified scale-down model (SDM) of the commercial process is a regulatory requirement as part of Biologics License Application (BLA)-enabling activities. This work demonstrates the qualification of the ambr® 250 as a representative SDM for two monoclonal antibody (mAb) commercial processes at scales >10,000 L. Representative process performance and product quality associated with each mAb were achieved using appropriate scale-down approaches, and special attention was paid to pCO₂ to ensure consistent performance and product quality. Principal component analysis (PCA) and univariate equivalence testing were utilized in the qualification of the SDM, along with a statistical evaluation of process performance and product-quality attributes for comparability. The ambr® 250 can predict these two commercial-scale processes (at center-point condition) for cell-culture performance and product quality. The time savings and resource advantages to performing PC studies in a small-scale HTP system improves the potential for the biopharmaceutical industry to get products to patients more quickly.     

Ecological niche modelling of Cantharellus species in Benin,and revision of their conservation status

Of the eight Cantharellus species known from Benin, seven have been encountered under similar macroecological conditions. The present work attempts to generate a more complete distribution of these seven species. Forty-eight occurrences of the target species and four explanatory variables including three bioclimatic variables and a land cover variable were used to build an ensemble model from five modelling approaches under the Biomod2 package of R software. Results showed a distribution restricted to the Bassila and Atacora mountain range phytogeographic districts with excellent statistical performance (TSS = 0.98, AUC = 0.99). This distribution is governed mainly by high soil moisture and high potential evapotranspiration, thus defining only gallery forests as the most suitable habitat for chanterelles in Sudano-guinean and Soudanese ecozones of Benin. Based on IUCN criterion B1 and sub-criteria B1a and B1c(i), these seven species were categorized under the Endangered (EN) threat category according to our results.     

Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.     

Cargo properties play a critical role in myosin Va-driven cargo transport along actin filaments

High-resolution experiments revealed that a single myosin-Va motor can transport micron-sized cargo on actin filaments in a stepwise manner. However, intracellular cargo transport is mediated through the dense actin meshwork by a team of myosin Va motors. The mechanism of how motors interact mechanically to bring about efficient cargo transport is still poorly understood. This study describes a stochastic model where a quantitative understanding of the collective behaviors of myosin Va motors is developed based on cargo stiffness. To understand how cargo properties affect the overall cargo transport, we have designed a model in which two myosin Va motors were coupled by wormlike chain tethers with persistence length ranging from 10 to 80 nm and contour length from 100 to 200 nm, and predicted distributions of velocity, run length, and tether force. Our analysis showed that these parameters are sensitive to both the contour and persistence length of cargo. While the velocity of two couple motors is decreased compared to a single motor (from 531 ± 251 nm/s to as low as 318 ± 287 nm/s), the run length (716 ± 563 nm for a single motor) decreased for short, rigid tethers (to as low as 377 ± 187 μm) and increased for long, flexible tethers (to as high as 1.74 ± 1.50 μm). The sensitivity of processive properties to tether rigidity (persistence length) was greatest for short tethers, which caused the motors to exhibit close, yet anti-cooperative coordination. Motors coupled by longer tethers stepped more independently regardless of tether rigidity. Therefore, the properties of the cargo or linkage must play an essential role in motor-motor communication and cargo transport.     

Genome-scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.     

A general kinetic model for biological nutrient removal activated sludge systems: model development

In this article, a kinetic model is developed and presented for biological nutrient removal (BNR) activated sludge (BNRAS) systems in general, but for external nitrification (EN) BNRAS (ENBNRAS) systems in particular. The model is based on the UCTPHO model, but includes some significant modifications, such as anoxic P uptake and associated denitrification by phosphorus accumulating organisms (PAOs). Some key features of the model are described and discussed before the model is presented. Model evaluation will be addressed in another article (Hu et al., 2007).     

Effect of ferrous ion on the biological activity in a UASB reactor: mathematical modeling and verification

The effect of ferrous ion on the biological activity in a upflow anaerobic sludge blanket (UASB) reactor was studied. A mathematical model was developed and validated in order to simulate the dynamic behavior of a UASB reactor. This model took into consideration of all the biological and physicochemical reactions. The model was able to simulate the accumulation of iron in the sludge bed and its effect on the biological activity of the anaerobic sludge. A significant increase in the maximum uptake rate of methanogens and acidogens was revealed when iron was supplemented to the UASB reactor. The addition of ferrous iron induced a stable and excellent COD conversion rate. The model can be a useful tool for the prediction of process performance in the future and can be used to assist in the operation of biogas plants. The ferrous ion addition proved to enhance the biological activity of UASB sludge. Thus, the model can be used for the design of treatment plants that will take advantage of the benefits of iron addition.     

The importance of clutch characteristics and learning for antiparasite adaptations in hosts of avian brood parasites

There is considerable variation in rejection rates of parasitic eggs among hosts of avian brood parasites. In this article, we develop a model that can be used to predict host egg rejection behavior in brood parasite-host systems in general, by considering both intra- and interclutch variation in host egg appearance; clutch characteristics that may be important in calculating the fitness of individuals adopting rejecter or acceptor strategies. In addition, we consider the importance of learning the appearance of own eggs during the first breeding attempt and host probability of survival between breeding seasons on evolution of rejection behavior. Based on this model we can predict at which level of parasitism fitness of rejecter individuals is higher than that of acceptor individuals and vice versa. The model analyses show that variation in egg appearance can be a key factor for the evolution of host defense against parasitism. In more detail, analyses show that we should expect to find a prolonged learning period only in hosts that have a high intraclutch variation in egg appearance, because such hosts may potentially experience high costs in terms of recognition errors. Furthermore, learning is in general more adaptive in parasite-host systems in which hosts do have some reproductive success even when parasitized, and when parasitism rates are moderate. By including variables that have not been considered in previous models, our model represents a useful tool in investigations of host rejection behavior in various host-parasite systems.