A model‐driven approach towards rational microbial bioprocess optimization

Due to sustainability concerns, bio‐based production capitalizing on microbes as cell factories is in demand to synthesize valuable products. Nevertheless, the nonhomogenous variations of the extracellular environment in bioprocesses often challenge the biomass growth and the bioproduction yield. To enable a more rational bioprocess optimization, we have established a model‐driven approach that systematically integrates experiments with modeling, executed from flask to bioreactor scale, and using ferulic acid to vanillin bioconversion as a case study. The impacts of mass transfer and aeration on the biomass growth and bioproduction performances were examined using minimal small‐scale experiments. An integrated model coupling the cell factory kinetics with the three‐dimensional computational hydrodynamics of bioreactor was developed to better capture the spatiotemporal distributions of bioproduction. Full‐factorial predictions were then performed to identify the desired operating conditions. A bioconversion yield of 94% was achieved, which is one of the highest for recombinant Escherichia coli using ferulic acid as the precursor.     

Optimization and scale‐up of oligonucleotide synthesis in packed bed reactors using computational fluid dynamics modeling

A computational fluid dynamics (CFD) model for the analysis of oligonucleotide synthesis in packed bed reactors was developed and used to optimize the scale up of the process. The model includes reaction kinetics data obtained under well defined conditions comparable to the situation in the packed bed. The model was validated in terms of flow conditions and reaction kinetics by comparison with experimental data. Experimental validation and the following model parameter studies by simulation were performed on the basis of a column with 0.3 g oligonucleotide capacity. The scale‐up studies based on CFD modelling were calculated on a 440 g scale (oligonucleotide capacity). © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1048–1056, 2014     

A mathematical model of blood,cerebrospinal fluid and brain dynamics

Using first principles of fluid and solid mechanics a comprehensive model of human intracranial dynamics is proposed. Blood, cerebrospinal fluid (CSF) and brain parenchyma as well as the spinal canal are included. The compartmental model predicts intracranial pressure gradients, blood and CSF flows and displacements in normal and pathological conditions like communicating hydrocephalus. The system of differential equations of first principles conservation balances is discretized and solved numerically. Fluid–solid interactions of the brain parenchyma with cerebral blood and CSF are calculated. The model provides the transitions from normal dynamics to the diseased state during the onset of communicating hydrocephalus. Predicted results were compared with physiological data from Cine phase-contrast magnetic resonance imaging to verify the dynamic model. Bolus injections into the CSF are simulated in the model and found to agree with clinical measurements.

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Scaling up population dynamics: integrating theory and data

How to scale up from local-scale interactions to regional-scale dynamics is a critical issue in field ecology. We show how to implement a systematic approach to the problem of scaling up, using scale transition theory. Scale transition theory shows that dynamics on larger spatial scales differ from predictions based on the local dynamics alone because of an interaction between local-scale nonlinear dynamics and spatial variation in density or the environment. Based on this theory, a systematic approach to scaling up has four steps: (1) derive a model to translate the effects of local dynamics to the regional scale, and to identify key interactions between nonlinearity and spatial variation, (2) measure local-scale model parameters to determine nonlinearities at local scales, (3) measure spatial variation, and (4) combine nonlinearity and variation measures to obtain the scale transition. We illustrate the approach, with an example from benthic stream ecology of caddisflies living in riffles. By sampling from a simulated system, we show how collecting the appropriate data at local (riffle) scales to measure nonlinearities, combined with measures of spatial variation, leads to the correct inference for dynamics at the larger scale of the stream. The approach provides a way to investigate the mechanisms and consequences of changes in population dynamics with spatial scale using a relatively small amount of field data.     

Shoulder joint kinetics and dynamics during underwater forward arm elevation

Aquatic exercises are widely implemented into rehabilitation programs. However, both evaluating their mechanical demands on the musculoskeletal system and designing protocols to provide progressive loading are difficult tasks. This study reports for the first time shoulder joint kinetics and dynamics during underwater forward arm elevation performed at speeds ranging from 22.5 to 90°/s. Net joint moments projected onto anatomical axes of rotation, joint power, and joint work were calculated in 18 participants through a novel approach coupling numerical fluid flow simulations and inverse dynamics. Joint dynamics was revealed from the 3D angle between the joint moment and angular velocity vectors, identifying three main functions—propulsion, stabilization, and resistance. Speeds <30°/s necessitated little to no power at all, whereas peaks about 0.20 W⋅kg⁻¹ were seen at 90°/s. As speed increased, peak moments were up to 61 × higher at 90 than at 22.5°/s, (1.82 ± 0.12%BW⋅AL vs 0.03 ± 0.01%BW⋅AL, P < 0.038). This was done at the expense of a substantial decrease in the joint moment contribution to joint stability though, which goes against the intuition that greater stabilization is required to protect the shoulder from increasing loads. Slow arm elevations (<30°/s) are advantageous for joint mobility gain at low mechanical solicitation, whereas the intensity at 90°/s is high enough to stimulate muscular endurance improvements. Simple predictive equations of shoulder mechanical loading are provided. They allow for easy design of progressive protocols, either for the postoperative shoulder or the conditioning of athlete targeting very specific intensity regions.     

Application of multivariate analysis and mass transfer principles for refinement of a 3‐L bioreactor scale‐down model—when shake flasks mimic 15,000‐L bioreactors better

Characterization of manufacturing processes is key to understanding the effects of process parameters on process performance and product quality. These studies are generally conducted using small‐scale model systems. Because of the importance of the results derived from these studies, the small‐scale model should be predictive of large scale. Typically, small‐scale bioreactors, which are considered superior to shake flasks in simulating large‐scale bioreactors, are used as the scale‐down models for characterizing mammalian cell culture processes. In this article, we describe a case study where a cell culture unit operation in bioreactors using one‐sided pH control and their satellites (small‐scale runs conducted using the same post‐inoculation cultures and nutrient feeds) in 3‐L bioreactors and shake flasks indicated that shake flasks mimicked the large‐scale performance better than 3‐L bioreactors. We detail here how multivariate analysis was used to make the pertinent assessment and to generate the hypothesis for refining the existing 3‐L scale‐down model. Relevant statistical techniques such as principal component analysis, partial least square, orthogonal partial least square, and discriminant analysis were used to identify the outliers and to determine the discriminatory variables responsible for performance differences at different scales. The resulting analysis, in combination with mass transfer principles, led to the hypothesis that observed similarities between 15,000‐L and shake flask runs, and differences between 15,000‐L and 3‐L runs, were due to pCO₂ and pH values. This hypothesis was confirmed by changing the aeration strategy at 3‐L scale. By reducing the initial sparge rate in 3‐L bioreactor, process performance and product quality data moved closer to that of large scale. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1370–1380, 2015     

Actinomycetes scale‐up for the production of the antibacterial,nocathiacin

An Amycolatopsis fastidiosa culture, which produces the nocathiacin class of antibacterial compounds, was scaled up to the 15,000 L working volume. Lower volume pilot fermentations (600, 900, and 1,500 L scale) were conducted to determine process feasibility at the 15,000 L scale. The effects of inoculum volume, impeller tip speed, volumetric gas flow rate, superficial gas velocity, backpressure, and sterilization heat stress were examined to determine optimal scale‐up operating conditions. Inoculum volume (6 vs. 2 vol %) and medium sterilization (R_o of 68 vs. 92 min^?1) had no effect on productivity or titer, and higher impeller tip speeds (2.1 vs. 2.9 m/s) had a slight effect (20% decrease). In contrast, higher backpressure, incorporating increased head pressure at the 15,000 L scale (1.2 vs. 0.7 kg/cm²) and low gas flow rates (0.25 vs. 0.8 vvm), appeared to be problematic (40–50% decrease). High off‐gas CO₂ levels were likely reasons for observed lower productivity. Consequently, air flow rate for this 25‐fold scale‐up (600–15,000 L) was controlled to match off‐gas CO₂ profiles of acceptable smaller scale batches to maintain levels below 0.5%. The 15,000 L‐scale fermentation achieved an expected nocathiacin I titer of 310 mg/L after 7 days. Other on‐line data (i.e., pH, oxygen uptake rate, and CO₂ evolution rate) and off‐line data (i.e., analog production, glucose utilization, ammonium production, and dry cell weight) at the 15,000 L scale also tracked similarly to the smaller scale, demonstrating successful fermentation scale‐up. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Zonal rate model for axial and radial flow membrane chromatography,part II: Model‐based scale‐up

Membrane chromatography (MC) systems are finding increasing use in downstream processing trains for therapeutic proteins due to the unique mass‐transfer characteristics they provide. As a result, there is increased need for model‐based methods to scale‐up MC units using data collected on a scaled‐down unit. Here, a strategy is presented for MC unit scale‐up using the zonal rate model (ZRM). The ZRM partitions an MC unit into virtual flow zones to account for deviations from ideal plug‐flow behavior. To permit scale‐up, it is first configured for the specific device geometry and flow profiles within the scaled‐down unit so as to achieve decoupling of flow and binding related non‐idealities. The ZRM is then configured for the preparative‐scale unit, which typically utilizes markedly different flow manifolds and membrane architecture. Breakthrough is first analyzed in both units under non‐binding conditions using an inexpensive tracer to independently determine unit geometry related parameters of the ZRM. Binding related parameters are then determined from breakthrough data on the scaled‐down MC capsule to minimize sample requirements. Model‐based scale‐up may then be performed to predict band broadening and breakthrough curves on the preparative‐scale unit. Here, the approach is shown to be valid when the Pall XT140 and XT5 capsules serve as the preparative and scaled‐down units, respectively. In this case, scale‐up is facilitated by our finding that the distribution of linear velocities through the membrane in the XT140 capsule is independent of the feed flow rate and the type of protein transmitted. Introduction of this finding into the ZRM permits quantitative predictions of breakthrough over a range of industrially relevant operating conditions. Biotechnol. Bioeng. 2014;111: 1587–1594. © 2014 Wiley Periodicals, Inc.     

An industrial perspective on bioreactor scale-down: what we can learn from combined large-scale bioprocess and model fluid studies

For industrial bioreactor design, operation, control and optimization, the scale-down approach is often advocated to efficiently generate data on a small scale, and effectively apply suggested improvements to the industrial scale. In all cases it is important to ensure that the scale-down conditions are representative of the real large-scale bioprocess. Progress is hampered by limited detailed and local information from large-scale bioprocesses. Complementary to real fermentation studies, physical aspects of model fluids such as air-water in large bioreactors provide useful information with limited effort and cost. Still, in industrial practice, investments of time, capital and resources often prohibit systematic work, although, in the end, savings obtained in this way are trivial compared to the expenses that result from real process disturbances, batch failures, and non-flyers with loss of business opportunity. Here we try to highlight what can be learned from real large-scale bioprocess in combination with model fluid studies, and to provide suitable computation tools to overcome data restrictions. Focus is on a specific well-documented case for a 30-m(3) bioreactor. Areas for further research from an industrial perspective are also indicated.     

Validated numerical fluid simulation of a thin‐layer cascade photobioreactor in OpenFOAM

Recently, microalgae have been considered as a promising alternative for the production of biofuels from CO₂. For the efficient cultivation of these microalgae, several types of photobioreactors have been designed and Pilot scale photobioreactors have been used to assess the performance of these reactors. Therein the primarily investigated reactor type is the Raceway Pond. However, the less researched Thin‐Layer Cascade Photobioreactor (TLC) shows a high potential for efficient production processes. Unfortunately, for low‐value products like biofuels costs must be kept to a minimum for an economic operation. To facilitate this, 3D Computational Fluid Dynamic simulations can be employed to estimate performance of reactor variants e.g. with respect to power input and mixing. Since up to now little effort has been put into the modelling of TLC reactors, this report aims to present a simulation approach for these reactors types that allows simple adaptation to different geometric or operational boundary conditions. All models have been generated for a two‐phase mixture in OpenFOAM. To demonstrate its applicability, validation measurements with a physical unit have been performed and were compared to the simulation results. With errors in the order of 10 % a successful simulation of the reactor geometry could be proven.     

Assessment of human left ventricle flow using statistical shape modelling and computational fluid dynamics

Blood flow patterns in the human left ventricle (LV) have shown relation to cardiac health. However, most studies in the literature are limited to a few patients and results are hard to generalize. This study aims to provide a new framework to generate more generalized insights into LV blood flow as a function of changes in anatomy and wall motion. In this framework, we studied the four-dimensional blood flow in LV via computational fluid dynamics (CFD) in conjunction with a statistical shape model (SSM), built from segmented LV shapes of 150 subjects. We validated results in an in-vitro dynamic phantom via time-resolved optical particle image velocimetry (PIV) measurements. This combination of CFD and the SSM may be useful for systematically assessing blood flow patterns in the LV as a function of varying anatomy and has the potential to provide valuable data for diagnosis of LV functionality.     

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics

Aortic valve stenosis is associated with an elevated left ventricular pressure and transaortic pressure drop. Clinicians routinely use Doppler ultrasound to quantify aortic valve stenosis severity by estimating this pressure drop from blood velocity. However, this method approximates the peak pressure drop, and is unable to quantify the partial pressure recovery distal to the valve. As pressure drops are flow dependent, it remains difficult to assess the true significance of a stenosis for low-flow low-gradient patients. Recent advances in segmentation techniques enable patient-specific Computational Fluid Dynamics (CFD) simulations of flow through the aortic valve. In this work a simulation framework is presented and used to analyze data of 18 patients. The ventricle and valve are reconstructed from 4D Computed Tomography imaging data. Ventricular motion is extracted from the medical images and used to model ventricular contraction and corresponding blood flow through the valve. Simplifications of the framework are assessed by introducing two simplified CFD models: a truncated time-dependent and a steady-state model. Model simplifications are justified for cases where the simulated pressure drop is above 10 mmHg. Furthermore, we propose a valve resistance index to quantify stenosis severity from simulation results. This index is compared to established metrics for clinical decision making, i.e. blood velocity and valve area. It is found that velocity measurements alone do not adequately reflect stenosis severity. This work demonstrates that combining 4D imaging data and CFD has the potential to provide a physiologically relevant diagnostic metric to quantify aortic valve stenosis severity.     

On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamics

Boundary conditions (BCs) are an essential part in computational fluid dynamics (CFD) simulations of blood flow in large arteries. Although several studies have investigated the influence of BCs on predicted flow patterns and hemodynamic wall parameters in various arterial models, there is a lack of comprehensive assessment of outlet BCs for patient-specific analysis of aortic flow. In this study, five different sets of outlet BCs were tested and compared using a subject-specific model of a normal aorta. Phase-contrast magnetic resonance imaging (PC-MRI) was performed on the same subject and velocity profiles extracted from the in vivo measurements were used as the inlet boundary condition. Computational results obtained with different outlet BCs were assessed in terms of their agreement with the PC-MRI velocity data and key hemodynamic parameters, such as pressure and flow waveforms and wall shear stress related indices. Our results showed that the best overall performance was achieved by using a well-tuned three-element Windkessel model at all model outlets, which not only gave a good agreement with in vivo flow data, but also produced physiological pressure waveforms and values. On the other hand, opening outlet BCs with zero pressure at multiple outlets failed to reproduce any physiologically relevant flow and pressure features.     

Development and scale‐up of a commercial fed batch refolding process for an anti‐CD22 two chain immunotoxin

We describe the development and scale‐up of a novel two chain immunotoxin refolding process. This work provides a case study comparing a clinical manufacturing process and the commercial process developed to replace it. While the clinical process produced high quality material, it suffered from low yield and high yield variability. A systematic approach to process development and understanding led to a number of improvements that were implemented in the commercial process. These include a shorter inclusion body recovery process, limiting the formation of an undesired deamidated species and the implementation of fed batch dilution refolding for increased refold titers. The use of a combination of urea, arginine and DTT for capture column cleaning restored the binding capacity of the capture step column and resulted in consistent capture step yields compared to the clinical process. Scalability is shown with data from 250 L and 950 L scale refolding processes. Compared to the clinical process it replaces, the commercial process demonstrated a greater than fivefold improvement in volumetric productivity at the 950 L refolding scale. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1380–1389, 2014     

High throughput automated microbial bioreactor system used for clone selection and rapid scale‐down process optimization

High throughput automated fermentation systems have become a useful tool in early bioprocess development. In this study, we investigated a 24 x 15 mL single use microbioreactor system, ambr 15f, designed for microbial culture. We compared the fed‐batch growth and production capabilities of this system for two Escherichia coli strains, BL21 (DE3) and MC4100, and two industrially relevant molecules, hGH and scFv. In addition, different carbon sources were tested using bolus, linear or exponential feeding strategies, showing the capacity of the ambr 15f system to handle automated feeding. We used power per unit volume (P/V) as a scale criterion to compare the ambr 15f with 1 L stirred bioreactors which were previously scaled‐up to 20 L with a different biological system, thus showing a potential 1,300 fold scale comparability in terms of both growth and product yield. By exposing the cells grown in the ambr 15f system to a level of shear expected in an industrial centrifuge, we determined that the cells are as robust as those from a bench scale bioreactor. These results provide evidence that the ambr 15f system is an efficient high throughput microbial system that can be used for strain and molecule selection as well as rapid scale‐up. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:58–68, 2018     

Top‐down and bottom‐up control of periphyton by benthivorous fish and light supply in two streams

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