High-throughput systems and processes have typically been targeted for process development and optimization in the bioprocessing industry. For process characterization, bench scale bioreactors have been the system of choice. Due to the need for performing different process conditions for multiple process parameters, the process characterization studies typically span several months and are considered time and resource intensive. In this study, we have shown the application of a high-throughput mini-bioreactor system viz. the Advanced Microscale Bioreactor (ambr15(TM) ), to perform process characterization in less than a month and develop an input control strategy. As a pre-requisite to process characterization, a scale-down model was first developed in the ambr system (15 mL) using statistical multivariate analysis techniques that showed comparability with both manufacturing scale (15,000 L) and bench scale (5 L). Volumetric sparge rates were matched between ambr and manufacturing scale, and the ambr process matched the pCO2 profiles as well as several other process and product quality parameters. The scale-down model was used to perform the process characterization DoE study and product quality results were generated. Upon comparison with DoE data from the bench scale bioreactors, similar effects of process parameters on process yield and product quality were identified between the two systems. We used the ambr data for setting action limits for the critical controlled parameters (CCPs), which were comparable to those from bench scale bioreactor data. In other words, the current work shows that the ambr15(TM) system is capable of replacing the bench scale bioreactor system for routine process development and process characterization.

Biofilms are highly organized structures coordinately formed by multiple species of bacteria. Quorum sensing (QS) is one cell-cell communication mechanism that is used by bacteria during biofilm formation. Biofilm formation is widely acknowledged to occur through a sequence of spatially and temporally regulated colonization events. While several mathematical models exist for describing biofilm development, these have been developed for open systems and are not applicable to closed systems where biofilm development and hydrodynamics are interlinked. Here, we report the development of a mathematical model describing QS and biofilm formation in a closed system such as a microfluidic channel. The model takes into account the effect of the external environment viz the mass and momentum transport in the microfluidic channel on QS and biofilm development. Model predictions of biofilm thickness were verified experimentally by developing Pseudomonas aeruginosa PA14 biofilms in microfluidic chambers and reflect the interplay between the dynamics of biofilm community development, mass transport, and hydrodynamics. Our QS model is expected to guide the design of experiments in closed systems to address spatio-temporal aspects of QS in biofilm development and can lead to novel approaches for controlling biofilm formation through disruption of QS spatio-temporal dynamics.

Abstract-Extreme Learning Machine (ELM) is a promising learning scheme for nonlinear classification and regression problems and has shown its effectiveness in the machine learning literature. ELM represents a class of generalized single hidden layer feed-forward networks (SLFNs) whose hidden layer parameters are assigned randomly resulting in an extremely fast learning speed along with superior generalization performance. It is well known that the online sequential learning algorithm (OS-ELM) based on recursive least squares [1] might result in ill-conditioning of the Hessian matrix and hence instability in the parameter estimation. To address this issue, the stability theory of Lyapunov is utilized to develop an online learning algorithm for temporal data from dynamic systems and time series. The developed algorithm results in parameter estimation that is globally asymptotically stable. Simulations results of the developed algorithm compared against online sequential ELM (OS-ELM) and the offline batch learning ELM (O-ELM) show that the online Lyapunov ELM algorithm can perform online learning at reduced computation, comparable accuracy and with a guarantee on the boundedness of the estimated system.

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Mammalian cell cultures used for biopharmaceutical production undergo various dynamic biological changes over time, including the transition of cells from an exponential growth phase to a stationary phase during cell culture. To better understand the dynamic aspects of cell culture, a quantitative proteomics approach was used to identify dynamic trends in protein expression over the course of a Chinese hamster ovary (CHO) cell culture for the production of a recombinant monoclonal antibody and overexpressing the antiapoptotic gene Bcl-xl. Samples were analyzed using a method incorporating iTRAQ labeling, two-dimensional LC/MS, and linear regression calculations to identify significant dynamic trends in protein abundance. Using this approach, 59 proteins were identified with significant temporal changes in expression. Pathway analysis tools were used to identify a putative network of proteins associated with cell growth and apoptosis. Among the differentially expressed proteins were molecular chaperones and isomerases, such as GRP78 and PDI, and reported cell growth markers MCM2 and MCM5. In addition, two proteins with growth-regulating properties, transglutaminase-2 and clusterin, were identified. These proteins are associated with tumor proliferation and apoptosis and were observed to be expressed at relatively high levels during stationary phase, which was confirmed by western blotting. The proteomic methodology described here provides a dynamic view of protein expression throughout a CHO fed-batch cell culture, which may be useful for further elucidating the biological processes driving mammalian cell culture performance.

The metabolome offers real time detection of the adaptive, multi-parametric response of the organisms to environmental changes, pathophysiological stimuli or genetic modifications and thus rationalizes the optimization of cell cultures in bioprocessing. In bioprocessing the measurement of physiological intracellular metabolite levels is imperative for successful applications. However, a sampling method applicable to all cell types with little to no validation effort which simultaneously offers high recovery rates, high metabolite coverage and sufficient removal of extracellular contaminations is still missing. Here, quenching, centrifugation and fast filtration were compared and fast filtration in combination with a stabilizing washing solution was identified as the most promising sampling method. Different influencing factors such as filter type, vacuum pressure, washing solutions were comprehensively tested. The improved fast filtration method (MxP® FastQuench) followed by routine lipid/polar extraction delivers a broad metabolite coverage and recovery reflecting well physiological intracellular metabolite levels for different cell types, such as bacteria (Escherichia coli) as well as mammalian cells chinese hamster ovary (CHO) and mouse myeloma cells (NS0).The proposed MxP® FastQuench allows sampling, i.e. separation of cells from medium with washing and quenching, in less than 30 seconds and is robustly designed to be applicable to all cell types. The washing solution contains the carbon source respectively the 13C-labeled carbon source to avoid nutritional stress during sampling. This method is also compatible with automation which would further reduce sampling times and the variability of metabolite profiling data.

In this article, a stochastic gradient based online learning algorithm for Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion based on Lyapunov approach is used to prove both asymptotic stability of estimation error and stability in the estimated parameters suitable for identification of nonlinear dynamic systems. The developed algorithm not only guarantees stability, but also reduces the computational demand compared to the OS-ELM approach [1] based on recursive least squares. In order to demonstrate the effectiveness of the algorithm on a real-world scenario, an advanced combustion engine identification problem is considered. The algorithm is applied to two case studies: An online regression learning for system identification of a Homogeneous Charge Compression Ignition (HCCI) Engine and an online classification learning (with class imbalance) for identifying the dynamic operating envelope of the HCCI Engine. The results indicate that the accuracy of the proposed SG-ELM is comparable to that of the state-of-the-art but adds stability and a reduction in computational effort.

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