Hydrodynamics considerations in cells systems from ocean flow to perfusion cultivation process

Caijuan Zhan

Doctoral Thesis in Biotechnology Stockholm, Sweden 2019

©Caijuan Zhan School of Engineering Sciences in Chemistry, Biotechnology and Health Royal Institute of Technology AlbaNova University Center SE-106 91 Stockholm Sweden Printed by Universitetsservice US-AB TRITA-CBH-FOU-2019:35 ISBN 978-91-7873-233-3

Abstract

Microorganisms and animal cells are grown surrounded by fluid, which is providing them with nutrients and removing their waste products. In nature and industry processes, cells/microbes can be subject to aggressive environments, such as turbulent flow or shear flow. Hydrodynamics force generated in these flows can affect the distribution of cells/microbes and even lead to cell damage. Understanding the mechanism and exploring the effect of hydrodynamic force in these environments could make the prediction of cells’ hydrodynamic response more systematic. In pharmaceutical industry, perfusion process is recognized as an attractive option for biologics production due to its high productivity. However, there are still some challenges and limitations for further process improvement due to lack of information of cell response to hydrodynamic force and nutrients. In both cases, hydrodynamics plays an important role and similar tool can be used to achieve a deeper understanding of these processes. This thesis is mainly aiming to elucidate the influence of hydrodynamic forces on microorganisms or cells in nature and during bioprocesses. In particular, shear stress in a natural environment and in a bioreactor operated in perfusion mode is studied.

This work mainly investigates hydrodynamics in nature and bioprocess including three flow cases. The first study investigates the effect of turbulence on marine life by performing direct numerical simulations (DNS) of motile micro-organisms in isotropic homogeneous turbulence. The clustering level of micro-organisms with one preferential swimming direction (e.g. gyrotaxis) is examined. The second study uses Computation Fluid Dynamics (CFD) to simulate the fluid flow inside a Wave bioreactor bag. The phenomenon of mixing, oxygen transfer rate and shear stress in nine different operating conditions of rocking speeds and angles are discussed. In the third study, the cellular response to shear force including growth and metabolism in a cell retention device such as hollow fiber filters during a perfusion process is analyzed. Theoretical calculations and experiment validation is performed to compare two filtration modes, tangential flow filtration (TFF) or alternating tangential flow filtration (ATF). Further optimizations regarding mixing and feeding are performed in a screening scale of in a perfusion system of stirred tank bioreactor with cell separation device.

The main findings can be summarized as that spherical gyrotaxis swimmers show significant clustering, whereas prolate swimmers remain more uniformly distributed due to their large sensitivity to the local shear. These results could explain how pure hydrodynamic effects can alter the ecology of micro-organisms for instance by varying shape and their preferential orientation (paper I). The simulations of Wave bioreactors show that the mixing and shear stress increase with the rocking angle but that increasing rocking speeds are not systematically associated with increasing mixing and shear stress. A resonance phenomenon is responsible for the fact that the lowest studied rocking speed generates the highest fluid velocity, mixing and shear stress (paper II). Theoretical velocity profile-based calculations suggested a lower shear stress for ATF by a factor 0.637 compared to TFF. This is experimentally validated by cultures of HEK (human embryonic

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kidney) 293 cells subjected to shear stress by a perfusion system that affects growth and metabolism using these cell separation devices (paper III). Thanks to optimization of mixing and oxygen transfer in a screening system for perfusion process, very high cell densities above 100 x 106 cells/mL of mammalian cells were achieved (paper IV). Keywords: Microorganism, cells, hydrodynamics, turbulence, numerical simulation, CFD, Resonance, Wave bioreactor, ATF, TFF, perfusion, design feeding strategy

Sammanfattning Mikroorganismer och djurceller är odlade omgivna av en vätska vilken förser dem

med näringsämnen och tar bort avfallsprodukter. Både i naturen och i industriella processer kan celler utsättas för aggressiva miljöer, såsom turbulent flöde eller skjuvflöde orsakade av vätskans rörelse. Den hydrodynamiska kraften som genereras i dessa flöden kan påverka distributionen av cellerna och ibland leda till cellskador. För att kunna förutsäga cellernas hydrodynamiska respons på en mer systematisk väg behöver man förstå den bakomliggande mekanismen och utforska effekten av hydrodynamisk kraft i dessa miljöer. Hydrodynamik är särskilt viktig inom perfusionsprocesser med hög celltäthet vilket är en attraktiv cellodlingsteknik pga hög produktivitet av biologiska produkter. Det finns emellertid många utmaningar inom perfusionssprocesse med hög celltäthet och utrymme for processförbättringar då det är brist på information om hur den hydrodynamiska kraften påverkar cellrespons. Sammantaget syftar denna uppsats huvudsakligen till att belysa hydrodynamikens påverkan på mikroorganismer eller odlade celler både natulrigt och under bioprocesser. I synnerhet studeras skjuvspänning i naturmiljö och bioreaktor som drivs i perfusionsläge.

I detta arbete studeras huvudsakligen hydrodynamik i natur och bioprocess inklusive tre flödesfall. Den första studien undersöks effekten av turbulens på det marint liv genom att utföra direkta numeriska simuleringar (DNS) av rörliga mikroorganismer i isotrop homogen turbulens. Clusteringsnivån för mikroorganismer med en föredragen svagriktning (t ex gyrotaxis) studeras. Den andra studien använder Computation Fluid Dynamics (CFD) för att simulera fluidflödet inuti en Wave bioreactor väska. Fenomenet att blanda, syreöverföringshastighet och skjuvspänning i nio olika driftsförhållanden av våghastigheter och vinklar diskuteras. I den tredje studien analyseras cellrespons inklusive tillväxt och metabolism på skjuvflöde i en cellretentionsanordning, med ihåliga fiberfilter för en specifk perfusionsprocess. Teoretiska beräkningar och experimentvalidering utförs för att jämföra två filtreringslägen: (i) tangentflödesfiltrering (TFF) eller (ii) tangentflödesfiltrering (ATF). Ytterligare optimeringar avseende blandning och matning utförs i en screeningsskala av perfusionssystem med omrörd tankbioreaktor med cellseparationsanordning.

Huvudfynden kan sammanfattas enligt följande: sfäriska gyrotaxisvimmare visar betydande kluster, medan prolate swimmers förblir mer jämnt fördelade på grund av deras stora känslighet för den lokala skjuvningen. Dessa resultat kan förklara hur rena hydrodynamiska effekter kan förändra mikroorganismernas ekologi, till exempel variera deras form och deras preferensorientering (papper I). Simuleringarna av Wave bioreactors visar att blandnings- och skjuvspänningen ökar med gungningsvinkeln men att ökande gunghastigheter inte behöver förknippas med ökande blandnings- och skjuvspänning. Ett resonansfenomen är ansvarigt för det faktum att den lägsta studerade gunghastigheten genererade den högsta fluidhastigheten, samt blandnings- och skjuvspänningen (papper II). Teoretiska hastighetsprofilbaserade beräkningar föreslog en lägre skjuvspänning för ATF med en faktor 0,637 jämfört med TFF. Detta var experimentellt validerat av kulturer av

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HEK (human embryonal njure) 293 celler utsatt för skjuvspänning genom ett perfusionssystem som påverkade tillväxt och metabolism med användning av olika ? cellseparationsanordningar (papper III). Vid optimering av blandning och syreöverföring utifrån ett screeningssystem för perfusionsprocessen uppnåddes mycket höga celldensiteter över 100 x 106 celler/mL däggdjurs celler (papper IV).

Nyckelord: Mikroorganism, celler, hydrodynamik, turbulens, numerisk simulering, CFD, resonans, Wave bioreactor, ATF, TFF, perfusion, designmatningsstrategi

To my family

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List of publications This thesis is based on the following publications, which are referred to in the text by their roman numerals: I Caijuan Zhan, Gaetano Sardina, Enkeleida Lushi, Luca Brandt, Accumulation of motile

elongated micro-organisms in turbulence, Journal of Fluid Mechanics, 739, 22-36.

II Caijuan Zhan, Erika Hagrot, Luca Brandt, Veronique Chotteau, Study of

hydrodynamics in Wave bioreactor by Computational Fluid Dynamics reveals a resonance

phenomenon, Chemical Engineering Science, 2019, 193, 53-65.

III Caijuan Zhan, Hubert Schwarz, Ray Field, Richard Turner and Veronique Chotteau,

Hydrodynamic shear stress in hollow filter for perfusion culture of human cells.

(Manuscript)

IV Hubert Schwarz, Ye Zhang, Caijuan Zhan, Magdalena Malm, Raymond Field,

Richard Turner, Chris Sellick, Paul Varley, Johan Rockberg, Véronique Chotteau, Small-

scale bioreactor supports high density HEK293 cell perfusion culture for the production of

recombinant Erythropoietin. (Manuscript accepted by Journal of Biotechnology, under

revision)

The following publication is not included in this thesis:

Caijuan Zhan, Hubert Schwarz, Ye Zhang, Ray Field, Richard Turner and Veronique

Chotteau, Intensified continuous cultivation process for biopharmaceutical production by

HEK293 cells with designed feeding of glucose. (Manuscript)

Zhang Y, Zhan CJ, Girod P-A, Martiné A, Chotteau V. Optimization of the cell specific

perfusion rate in high cell density perfusion process. (Manuscript)

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Author's contributions Paper I: CJZ Performed simulations and postprocess; wrote the manuscript.

Paper II: Experimental study designed by CJZ in concertation with VC, experiments

were performed by CJZ and EH. CJZ performed simulations, generated analytical results

and postprocess; wrote the manuscript.

Paper III: Experimental designed by CJZ in concertation with VC, experiments were

carried out by CJZ and HS. CJZ Performed analytical solution and wrote the manuscript.

Paper IV: Experimental designed by HS, YZ, CJZ and VC, experiments were performed

by HS and YZ. CJZ performed hydrodynamics analysis.

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List of abbreviations and symbols Abbreviation Description

ATF BHK CAD CFD CHO CSPR DO DNS DT EDR ELISA EPO FDM FEM FVM Glc Gln HE HEK HF mAbs MVC/mL VOF OUR PBD PBU PBT PDE POI PRESTO! rhEPO rms RT STRs TFF UDF UF VOF

Alternating tangential flow filtration Baby hamster kidney Computer aid design Computational Fluid Dynamics Chinese Hamster Ovary Cell Specific Perfusion Rate Dissolved oxygen Direct numerical simulation Disk turbine Energy dissipation rate Enzyme-linked immunosorbent assay Erythropoietin Finite difference method Finite element method Finite volume method Glucose Glutamine High efficiency turbine Human embryonic kidney Hollow fiber cartridge Monoclonal antibodies Million viable cells per milliliter Volume of fluid Oxygen Uptake Rate Pitch Blade downward Pitch Blade upward Pitched blade turbines Partial differential equations Product of interest Pressure Staggering Option Recombinant human erythropoietin Root mean square Rushton turbine impeller Stirred tank reactors Tangential Flow Filtration User-defined function ultrafiltration Volume of fluid

Symbol Description Unit A Rocking angle ° Aarea Surface area m2 C* Saturated dissolved oxygen concentration % CL Oxygen concentration % C Concentration of metabolite in Bioreactor mM (millimolar) Cin Concentration of metabolite from feeding mM (millimolar) 𝒄𝒑 Specific heat at constant pressure J/(kg K) CV Viable Cell density MVC/mL D Perfusion rate BV/day 𝒆 Thermodynamic internal energy m2/s2 f body force exerted on fluid N g gravity m/s2 H Height m KLa oxygen transfer rate coefficient h-1 KH Heat conduction coefficient W/(m2K) L characteristic length m N Agitation speed 1/s D impeller diameter m P Pressure Pa Po Impeller power number - Re Reynolds number - q Cell specific consumption/production rate 𝜇mol/106cell/day Q Volumetric flow rate m3/s r Radial position in pipe m R Radius of the pipe m S Rocking speed rpm t Time s T Temperature K u Fluid velocity in X direction m/s 𝒖' Root mean square velocity in X direction m/s V Working volume mL v fluid velocity in Y direction m/s 𝒗' Root mean square velocity in Y direction m/s Vbleed Cell bleed volume mL Vin Volume into the bioreactor mL Vout Volume out the bioreactor mL w fluid velocity in Z direction m/s 𝒘' Root mean square velocity in Z direction m/s Wo Womersley number -

Greek symbol Description Unit 𝝆 Density kg/m3

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𝛎 Fluid kinematic viscosity m2/s 𝝁 Liquid dynamic viscosity Pa×s 𝛈 Kolmogorov length scale m �̇� Angular speed of the rocking motion rad/s 𝜶 Volume fraction % 𝛚 Vorticity s-1 𝝉 Shear stress Pa×s 𝛃 Arbitrary parameter m 𝜺 Energy dissipation rate W/m3 𝜺max Maximum energy dissipation rate W/m3 ⟨𝜀⟩ Average specific energy dissipation rate m2/s3 𝛑 Mathematical constant - 𝛄 Shear rate s-1 𝝉 Shear stress N/m2

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Table of Contents 1. Overview .................................................................................................................. 22. Introduction to the cells ........................................................................................... 42.1 Microorganisms .......................................................................................................... 42.2 Ecological importance of cells .................................................................................... 52.3 Mammalian cells culture for protein production .......................................................... 62.3.1 Perfusion process .................................................................................................... 83. Introduction to hydrodynamics ............................................................................ 113.1 Flows Classification ................................................................................................. 113.2 Characteristics of turbulent flow ............................................................................... 123.3 Computational Fluid Dynamics (CFD) ..................................................................... 153.3.1 Numerical methods ............................................................................................... 153.3.2 CFD applications .................................................................................................. 183.3.3 CFD advantages ................................................................................................... 194. Hydrodynamics applications from turbulence in natural flows to industrial bioprocesses ....................................................................................................................... 214.1 Numerical simulation of microorganism motility/motion in natural systems ............. 214.2 Animal cells response to hydrodynamic force ........................................................... 224.3 Hydrodynamics in Bioprocess .................................................................................. 234.3.1 Hydrodynamics in Stirred tank bioreactor ............................................................. 254.3.2 Hydrodynamics in Wave bioreactor ...................................................................... 304.3.3 Hydrodynamics in perfusion process..................................................................... 325. Perfusion process optimization ............................................................................. 375.1 Animal cells metabolism in bioprocess ..................................................................... 375.2 Feeding optimization in perfusion process ................................................................ 396. Conclusions and perspectives ................................................................................ 41Acknowledgements............................................................................................................ 44References ......................................................................................................................... 46

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1. Overview

This cross-disciplinary study, combining fluid mechanics and biotechnology, is mainly

aimed at investigating the influence of hydrodynamics forces, primarily shear stress, on cells in

liquid environment. In particular two applications are studied, microorganism in natural ocean

environment and mammalian cells in bioprocess. As background for our study, the literature of

microorganisms and cells bioprocess and their importance in ecological and biopharma industry

application respectively are reviewed in Chapter 2. Mammalian cell culture for protein

production, especially perfusion mode, is introduced. Chapter 3 discusses the mechanism of

different fluid flow (laminar and turbulent) and the methodology of computational fluid

dynamics employed to investigate the fluid flow. The text presents the numerical methods,

applications and advantages of Computational Fluid Dynamics. Chapter 4 discusses

hydrodynamics force in nature flow and bioprocesses including stirred tank bioreactors, Wave

bioreactor and perfusion processes. Animal cell response to hydrodynamic force is also reviewed

in this chapter. In Chapter 5, the optimizations of a perfusion process are carried out from

hydrodynamics and cell metabolism point of view. In particular, the optimization of perfusion

bioprocess from the important aspect of balancing the mixing for homogenization and the shear

stress is discussed.

The main research and contributions are presented in four papers. In nature ocean, the

macroscopic phenomena of marine landscape are influenced by the interactions between the flow

and the motility of bacteria and phytoplankton. The effect of turbulence on marine life is

therefore a key research question that also has relevance on the understanding of hydrodynamic

effects on altering the ecology of micro-organisms due to their shape and their preferential

orientation (Paper I—microbes in turbulence). In industrial application, a understanding the fluid

mechanism and how the flow influences the cells are critical for the biotechnology engineers for

the design and optimization of bioprocesses and equipment. The fluid motion can result in

complex fluid structure or turbulence, which is responsible for both mixing and shear stress in

the cultivation process. Mixing is necessary to achieve the homogeneity of the nutrients and

aeration. However, shear stress can arrest the cell growth and lead to cell damage [1].

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Understanding the mixing and shear stress is important in order to obtain satisfying homogeneity

of bioreactor without cell damage (Paper II—Wave bioreactor). Perfusion processes have

recently attracted a lot of interest and can help to meet the increasing demand in

biopharmaceutical production. These processes are more complex and include additional

hydrodynamics issues due to the cell separation device compared to batch and fed-batch

processes. The cell retention device introduces an extra shear source and can have a negative

effect on the cells and process performance. Therefore, understanding the shear stress and the

cell response to shear stress is beneficial to choose proper operating conditions (Paper III—

animal cell in ATF and TFF). Different impeller systems are investigated in small scale stirred

tank bioreactors, for their influence on the mixing and oxygen transfer performance. Then a

screening scale perfusion process with low shear stress and good mixing are demonstrated for

its excellent performance. It can achieve very high cell density (108 cells/mL) for human cell

culture (Paper IV—high cell density in screening scale).

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2. Introduction to the cells The smallest unit of life is the cell [2], which is the basic structural, functional, and

biological unit of all known living organisms. There are two main types of cells, eukaryotic,

which contain a nucleus, and prokaryotic, which do not. Prokaryotes are single-celled organisms,

while eukaryotes can be either single-celled or multicellular.

The number of cells in plants and animals varies from species to species, humans contain

more than 10 trillion (1013) cells. However, the whole organism has been generated by cell

divisions from a single cell, which is the vehicle for the hereditary information that defines the

species [3].

2.1 Microorganisms Microorganisms simply refer to a form of life that fall within the microscopic size range,

and the microorganism may exist in its single-celled form or in colony of cells. Indeed, some

microorganisms are large enough to be seen without a microscope, so this definition is not

entirely satisfactory either. Some scientists would argue that the distinguishing features of

microorganisms are small size, unicellular organization, and osmotrophic (feeding by absorption

of nutrients).

2.1.1 Motility of microbes

Many microorganisms exhibit motility as a main trait distinguished from passive particles.

Planktonic microbes swim by means of flagella-corkscrew-like appendages that rotate to propel

them. While locomotion allows them to move relatively to the ambient flow, to seek food or

escape predators, they can detect the physical and chemical gradient of their environment.

Depending on the external stimulus, the behavior is categorized for example as geotaxis [4],

phototaxis [5] and chemotaxis [6], etc. These directional responses bias their motility

accordingly. Many phytoplankton have asymmetric distributions of mass within the organism

(bottom-heaviness) and are re-oriented by the balance between torque generated by viscous and

a gravitational torque, termed as gyrotaxis [7]. This feature induces an accumulation of cells

heavier than water at the top and the occurrence of a bioconvective instability that develops from

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an initially uniform suspension, without unstable density stratification and without any

background flow [8].

2.1.2 Diverse morphology

Microorganisms are very diverse and one of the fundamental determinate of microbial life

is morphology. The most abundant ocean bacteria and archaea have very small cell volumes and

the majority of them is smaller than about 0.6 mm in their largest dimension, and many are less

than 0.3 mm, with cell volumes as low as 0.003 mm3 [9]. The wide range of body size of marine

microbes is critical to predict feature of ecological system. The effect of advection on uptake is

strongly size dependent [10]. Naselli-Flores [11] summarized the results of phytoplankton

morphological variability under different environmental constraints. The behaviors of prolated

particles differ from the ones of spheroidal particles while they immerse in a fluid with laminar

motion [12]. A prolate shape is believed to be advantageous in terms of nutrient uptake [13].

2.1.3 Animal cells

Animals are a large and incredibly diverse group of organisms. Making up about three-

quarters of the species on Earth, they run the gamut from corals and jellyfish to ants, whales,

elephants, and, of course, humans. Animal cells, which are eukaryotic, do not have a cell

wall. The lack of a rigid cell wall allowed animals to develop a greater diversity of cell types,

tissues, and organs [14]. Specialized cells that formed nerves and muscles [15]—tissues

impossible for plants to evolve—gave these organisms mobility [16]. The animal kingdom is

unique among eukaryotic organisms because most animal tissues are bound together in

an extracellular matrix by a triple helix of protein known as collagen [17]. The fact that no other

organisms utilize collagen in this manner is one of the indications that all animals arose from a

common unicellular ancestor. Bones, shells, spicules, and other hardened structures are formed

when the collagen-containing extracellular matrix between animal cells becomes calcified.

2.2 Ecological importance of cells More than 70% of the Earth is covered by ocean and most of life in the ocean is microbial

life. Marine microbes are called all ocean living organisms smaller than 100 µm [18] in the

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biosphere. Even 0.5 km below the seafloor within marine sediments, there are still metabolically

diverse and active microbial communities buried deep [19]. Microbes constitute more than 90%

of ocean biomass and make up a hidden majority of life that flourishes in the sea [20]. The

microbial world accounted for all known life forms for nearly 50% to 90% of Earth's history,

life itself likely began in the ocean [20]. From the ice-covered polar regions of the Arctic and

Antarctic to the boiling hydrothermal vents, microbes are everywhere and they help to shape the

features of our planet.

Microorganisms are essential in mediating various biochemical cycles and play many

important roles in the Earth system [21]. Microbial decomposition of soil organic matter releases

60 Pg of carbon dioxide to the atmosphere each year [22], which constitutes about 25% of natural

carbon dioxide emissions. In the short-time frame, warming will stimulate carbon dioxide release

from soils [23]. However, long-term carbon emissions will decline, since soil microbes

metabolize a larger portion of the available carbon at higher temperatures and produce new

microbial biomass [24]. Microorganisms could either greatly help in climate change mitigation

or prove disastrous by exacerbating anthropogenic climate change through positive-feedback

mechanisms [25]. They are also the major primary producers in the ocean, and dictate much of

the flow of marine energy and nutrients [26]. Microbes are involved in cycling vital elements

such as carbon and nitrogen, breaking down wastes and dead organisms into simpler substances

that, for instance, plants can use in photosynthesis [27].

Oceans provide the largest reservoirs of phytoplankton, which produce nearly half of the

world's oxygen [28]. Phytoplanktonic organisms are typically heterogeneously distributed and

often form thin layers. These thin layers affect phytoplankton reproduction [29] and predation

[30] in coastal marine system. Phytoplankton also produces vertical patchiness in the optical and

acoustical properties of the water column and provide a mechanism for maintenance of species

diversity and habitat partitioning in the pelagic zone.

2.3 Mammalian cells culture for protein production The development of recombinant protein technology has enhanced the interest in

mammalian cell culture bioprocesses. The first human therapeutic protein to be licensed from

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recombinant technology was recombinant insulin (Humulin from Genentech) [31], in 1982. The

relative structural simplicity of this molecule allowed its large-scale production to be developed

in Escherichia coli, which is fast growing and robust compared to mammalian cells [32].

However, it was soon realized that many subsequent targets for recombinant therapeutics were

more complex and required the post-translational metabolic machinery only available in

eukaryotic cells. In 1986, human tissue plasminogen activator became the first therapeutic

protein from recombinant mammalian cells to obtain market approval [33]. Since then, cultivated

mammalian cells have become the dominant system for the production of recombinant proteins

for clinical applications, due to their capacity for proper protein folding, assembly and post-

translational modification [34]. Thus, the quality and efficacy of a protein can be superior when

expressed in mammalian cells versus other hosts such as bacteria, plants and yeast.

The preference for the mammalian expression system is due to their ability to synthesize

proteins that are similar to those naturally occurring in human with respect to molecular

structures and biochemical properties [35]. Mammalian cell lines have the basic machinery to

express and secrete recombinant protein, and a huge number of cell lines from various tissues

and species with suitable growth properties is available. Two hamster cells lines, Chinese

hamster cell ovary (CHO) cells and Baby hamster kidney (BHK) cells and two mouse cell lines,

NS0 (myeloma) and SP2-0 (hybridoma), supply most of the biopharmaceutical industry [36].

Among the mammalian-based expression systems, CHO cell is by far, the most commonly used

cell line and considered as the workhorses of the biopharmaceutical industry. It is involved in

the production of over 70% of recombinant biopharmaceutical proteins, most of them

being monoclonal antibodies (mAbs) [37, 38]. A majority of today's mammalian cell culture

processes for biopharmaceutical production are based on cells that are cultivated in suspension,

which enables large-scale production [39].

Compared to CHO cells, human cell line, such as HEK (human embryonic kidney) 293

cells, can maintain glycosylation more resembling human system and completely eliminate non-

human constituent from biopharmaceutical products providing potentially lower

immunogenicity, greater biological activity and increased half-life [40]. HEK293 cells, widely

used for over 35 years, have been also widely used for transient expression of recombinant

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proteins or viral vector [41, 42]. This cell line is probably the most used cell line to express

research grade recombinant proteins. Due to easily growth and fast and easy transient

transfection procedure, these cells are also potentially suited for use in high-throughput

recombinant gene expression facilities [43].

2.3.1 Perfusion process

Four cell culture modes are usually distinguished batch, fed-batch, perfusion and

chemostat. Among these, perfusion can support very high cell density with high productivity

and maintain longer cultivation length. Due to this fact, the bioreactors used in perfusion mode

can be at least one order of magnitude smaller than the ones used for fed-batch processes with

equivalent production of the product of interest (POI) [44]. This represents major advantages of

reduce footprint and thus lower capital expenditure but also increased flexibility and

transferability due to a wide compatibility with disposable equipment. Therefore, perfusion is

becoming an attractive option for the production of recombinant proteins [45, 46]. The perfusion

process provides a continuous renewal of the culture medium, generates stable favorable

environment in the bioreactor, which can benefit the cell metabolism, growth and health as well

as the product of interest [47]. Perfusion processes of animal cell cultures have been used to

produce various substances such as monoclonal antibody, recombinant proteins or baculovirus

[35, 48-50]. Chemostat is continuous cell culture without cell retention, is often used to gather

steady state data about an organism in order to generate a mathematical model relating to its

metabolic processes. This operation mode is not favorable for mammalian cells due to their slow

growth.

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Fig. 1 Schematic representation of a perfusion process

In a perfusion process, the working volume is kept constant during the whole process. The

culture medium is continuously renewed by removal of conditioned medium compensated by

feeding the same amount of fresh medium, as shown in Fig.1. The retention device separates the

cells from cell-free supernatant, which is continuously removed from the bioreactor into the

harvest line.

Fig. 2 Schematic diagram of (a) tangential flow filtration (TFF) and (b) alternating tangential flow

filtration (ATF)

The perfusion processes are more challenging from technical and sterility point of views

[51, 52]. An important challenge is a reliable cell retention device, which is critical for well-

functioning perfusion process. The separation device can be mounted inside or externally to the

Fresh medium

Cell broth

Cell

Harvest

Cell free supernatantCell separation

device

Bioreactor

Constant working volume

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bioreactor. Three main cell separation principles are used: (i) filtration, (ii) acceleration/gravity,

(iii) electric field [52]. Many cell separation devices have been developed, e.g. gravity-based cell

settlers, spin filters, centrifuges, cross-flow filters, tangential and alternating tangential flow

filters, vortex flow filters, acoustic settlers, hydrocyclones [51-53]. Among these, tangential flow

microfiltration is one of the preferred method for perfusion nowadays [54]. Tangential flow

filtration (TFF) can minimize the filter fouling since the particles are not pressed into the filter

membrane. It is possible to change the external filter when it is clogged. Perfusion with hollow

fiber (HF) filter-based cell separation is very efficient and has shown to support very high cell

density for biopharmaceutical manufacturing [45, 46, 55]. Further improvement has been

introduced by alternating tangential flow filtration (ATF), which can further reduce the filter

fouling by creating a back flush in the filter membrane. As shown in Fig. 2b, in the ATF, the cell

broth is pumped from the bioreactor to the HF and vice versa thanks to a diaphragm pump

mounted at one end of the HF; while the cell broth is circulated only in one direction in the TFF

using a peristaltic pump.

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3. Introduction to hydrodynamics

Hydrodynamics is a discipline of studying liquids in motion and has a wide range of

applications, including e.g. calculating forces and moments on aircraft, determining the mass

flow rate of petroleum through pipelines, predicting the weather patterns, understanding

nebulae in interstellar space and modelling fission weapon detonation.

Hydrodynamics or fluid dynamics offers a systematic structure, which underlies

these practical disciplines—that embraces empirical and semi-empirical laws derived from flow

measurement and used to solve practical problems. The solution to a fluid dynamics problem

typically involves the calculation of various properties of the fluid, such as flow

velocity, pressure, density, and temperature, as functions of space and time.

This chapter begins with the flow classification and is then followed by the description of

turbulence and its characteristics. Computation Fluid Dynamics, which is an important tool to

investigate fluid mechanics, is reviewed here and its advantages over experiments are listed.

Then the hydrodynamics in bioreactor and bioprocess are introduced.

3.1 Flows Classification The flow of fluids can be classified according to various criteria. According to the motion

(path) of the fluid particles, a flow can be characterized as laminar flow and turbulent flow. When

the fluid particles move in smooth, layered fashion (where no substantial mixing of fluid occurs),

the flow is laminar. An important non-dimensional number, the Reynolds number, predicts the

flow pattern in different fluid flow situations. The Reynolds number can be defined as 𝑅𝑒 = =>?

,

and is the ratio of inertial forces to viscous forces. In this expression, 𝜐 is the kinematic viscosity

of the fluid, L is characteristic length and u is the characteristic velocity of the fluid. Usually for

pipe flow, three flow regimes can be characterized by the Reynolds number:

I. for low Reynolds numbers, less than 2300, the behavior of a fluid depends mostly on its

viscosity and the flow is steady, smooth, viscous or laminar;

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II. for intermediate Reynolds numbers between 2300<Re<4000, the flow is transitional —

partly laminar and partly turbulent;

III. for Reynolds numbers higher than 4000, the momentum of the fluid determines its

behavior more than the viscosity and the flow is unsteady, churning, roiling, or turbulent.

In case of turbulent flow, the fluid particles move in a chaotic, “tangled” fashion

(significant mixing of fluid occurs). Because of the high degree of activity associated with

turbulent eddies and their fluctuating velocities, the viscous energy dissipation inside a turbulent

flow can be much larger than in a laminar flow [56].

3.2 Characteristics of turbulent flow

Fig. 3 Turbulent flow of smoke

Turbulence was recognized as a distinct fluid behavior by Leonardo da Vinci more than

500 years ago. In 1937, Taylor and von Karman [57] proposed the following definition of

turbulence: ‘Turbulence is an irregular motion, which in general makes its appearance in fluids,

gaseous or liquid, when they flow past solid surfaces or even when neighboring streams of the

same fluid flow past or over one another’. It is characterized by the presence of a large range of

excited length and time scales. The irregular nature of turbulence stands in contrast to laminar

motion and adds more difficulties to measure and predict the turbulent flow motion. Virtually

all flows of practical engineering interest are turbulent. We can get some ideas about the nature

of turbulence observing the efflux from a smoke stack (see Fig.3). However, it is very difficult

to give a precise definition of turbulence. Alternatively, we can list some of the characteristics

of turbulent flows.

13

I. Irregularity

One characteristic is the irregularity, or randomness, of all turbulent flows. This makes a

deterministic approach to turbulence problems impossible and one relies on statistical methods

instead.

II. Diffusivity

The large diffusivity, which causes rapid mixing and increased rates of momentum, heat,

and mass transfer, is another important feature of all turbulent flows. The flow cannot be termed

as turbulent if a flow pattern looks random but does not exhibit spreading of velocity fluctuations

through the surrounding fluid.

III. Large Reynolds numbers

Turbulent flows always occur at high speeds or large domains, in other words, when

inertial forces dominate the flow. This is quantified by a large value of Reynolds number,

probably the most important non-dimensional parameter in fluid mechanics.

IV. Three-dimensional vorticity fluctuations

Vorticity dynamics plays an essential role in the description of turbulent flows. The

vorticity, which is a pseudovector field, defined as the curl (rotational) of the flow

velocity vector. Turbulence is rotational and three-dimensional and is characterized by high

levels of fluctuating vorticity.

V. Dissipation

The major distinction between random waves and turbulence is that waves are essentially

non-dissipative (though they often are dispersive), while turbulence is essentially dissipative [58].

Viscous shear stresses perform deformation work, which increases the internal energy of the

fluid at the expense of the kinetic energy of the turbulence. Turbulence needs a continuous supply

of energy to make up for these viscous losses. If no energy is supplied, turbulence decays rapidly.

Random motions, such as gravity waves in planetary atmospheres and random sound waves

(acoustic noise), have negligible viscous losses and, therefore, are not turbulent.

VI. Continuum

14

Turbulence is a continuum phenomenon, governed by the equations of fluid mechanics.

Even the smallest scales occurring in a turbulent flow are ordinarily far larger than any molecular

length scale [58].

Turbulence is the dominant physical process in the transfer of momentum and heat, in the

dispersion of solutes and small organic or inorganic particles, in lakes, reservoirs, seas, ocean

and fluid mantles. Turbulent motion occurring naturally in the ocean on scales ranging from

millimeters to hundreds of kilometers. Oceanic turbulence has the same properties shared by

turbulence in the other naturally occurring fluids. The knowledge of turbulence and its effect is

crucial in understanding how the ocean works and in constructing numerical models to predict

how, in the future, the ocean will adjust as the forcing by the atmosphere is altered by changes

in the world’s climate [59]. Due to the wind above the sea surface, the ocean is covered by waves,

many of them breaking and injecting their momentum and bubbles of air from the overlaying

atmosphere to the underlying seawater. Intermediately under the surface, even at great depths,

the water is generally in the state of irregularly and variable motion that is referred to as

turbulence, although there is no simple and unambiguous definition of this term.

Turbulent motion occurs over a wide range of length and time scales, this variation is an

important characteristic of turbulent flows and responsible for the difficulty encountered in the

numerical and theoretical analysis of turbulent flows. The largest eddies in the flow account for

most of the transport of momentum and energy, this kind of eddy is only bounded by the physical

boundaries of the flow. The size of largest eddies has a scale of L, referred to as integral length

scale. The smallest length scales existing in a turbulence regime are those where the kinetic

energy is dissipated into heat. From the arguments leading to Kolmogorov's first similarity

hypothesis, the only influence factors of the behavior of the small-scale motions are the overall

kinetic energy production rate, which is equal to the dissipation rate, and the viscosity. The

dissipation rate will be independent of the viscosity, but the scales at which this energy is

dissipated will depend on both the dissipation rate and viscosity. A length scale can be formed

only based on the dissipation m2/sec3 and viscosity m2/sec and can be expressed as

η = B?!

CDE/G

(1)

15

This length scale, called Kolmogorov length scale, is the smallest hydrodynamic scale in

turbulent flows.

3.3 Computational Fluid Dynamics (CFD) Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical

analysis to solve and analyze problems involving fluid flow and heat transfer [60]. It replaces

the governing partial differential equations (PDE) of fluid flow by a set of algebraic equations,

which can be solved using digital computers. CFD can provide a qualitative (and sometimes

even quantitative) prediction of fluid flows in the domain of interest, by opensource code or

commercial software. CFD enables scientists and engineers to perform ‘numerical experiments’

(i.e. computer simulations) in a ‘virtual flow laboratory’ [61].

3.3.1 Numerical methods

Numerical methods are the core of the CFD process. Researchers and engineers develop

two fundamental aspects in CFD, i.e. model and numeric. Modeling includes physical and

mathematical models. Computer aid design (CAD) is used to define the domain of interests, both

the geometry and the physical bounds. The governing equations and boundary conditions are

building up the mathematical models. All physical processes can be described by PDEs (partial

differential equations), which are a combination of differential terms (rates of change) describing

a conservation principle. The focus of numeric development is to provide efficient, robust, and

reliable algorithms for the solution of PDEs.

3.3.1.1 Physical and mathematical model

The first problem of performing CFD simulations is to specify the simulation domain, i.e.

what geometry should be included. Appropriate choose of geometry is critical, good

approximations and simplifications allow an analysis with reasonable effort. CAD software

package can be used to generate the geometry. Moreover, physical conditions are required on

the boundaries of the flow domains.

16

Theoretically, all the physical properties of fluid flow can be expressed in terms of

mathematical equations. The basic equations are the three physics laws of conservation:

1) conservation of Mass: Continuity Equation;

2) conservation of Momentum: Momentum Equation of Newton’s Second Law;

3) conservation of Energy: First Law of Thermodynamics or Energy Equation.

The main structure of thermo-fluids examinations is directed by governing equations that

are based on the conservation law of fluid’s physical properties. Usually, Navier-Stokes

equations are used to refer to all of these equations and can be written as:

HIHJ+ ∇ ∙ (𝜌𝑢) = 0 (2)

H=HJ+ (𝑢 ∙ ∇)𝑢 = − E

I∇𝑝 + U

I∇V𝑢 + 𝐹 (3)

𝜌𝑐Y ZH[HJ+ (𝑢 ∙ ∇)T] = 𝐾_∇V𝑇 + 𝜙 (4)

here, u is the fluid velocity, p is the pressure, 𝜌 is the density, F is the external force per unit

mass, 𝜇 is the dynamic viscosity, 𝑇 is the temperature and 𝐾_is a heat conduction coefficient.

𝜙 is the viscous dissipation term and 𝑐Y is the specific heat at constant pressure. These equations

can be used to model a wide range of fluid flow configurations, describe how the velocity,

pressure, temperature, and density of a moving fluid are related. They are highly nonlinear

second order partial differential equations in four independent variables since, in general, flows

are unsteady and three dimensional [62].

3.3.1.2 Discretization method

The governing equations for fluid flow are not usually amenable to analytical solutions.

Therefore, the CFD process requires the discretization of governing PDEs. There are various

methods of discretization and it involves two steps. First, the volume of interests is divided into

discrete cells (the mesh). The mesh may be uniform or non-uniform, structured or unstructured,

consisting of a combination of hexahedral, tetrahedral, prismatic, pyramidal or polyhedral

elements. Then PDEs are discretized and solved inside each of these subdomains. Typically,

there are three discretization methods to solve the approximate version of the system of equations:

17

finite volume method (FVM), finite element method (FEM), or finite difference method (FDM).

According to the specific governing equation (compressible flow or incompressible flow), mesh

type and the expected order of accuracy, one of these three discretization method can be chosen

for simulation.

Among these three methods, the FDM is the oldest and is easiest to understand when the

physical grid is Cartesian. FDM is based upon the application of a local Taylor expansion to

approximate PDE. It uses a topologically square network of lines to construct the discretization

of the PDE, which is a potential bottleneck for handling complex geometries in multiple

dimensions [63]. This subsequently becomes the motivation to develop the finite element and

finite volume techniques. A FEM discretization is based upon a piecewise representation of the

solution in terms of specified basis functions, which is a set of elements (vectors) in a vector

space. The FEM was first used to solve problems of solid and structural analysis, and extend

applications in thermal analysis, fluid flow analysis, piezoelectric analysis, and many others [64].

The FVM is a numerical technique that transforms the partial differential equations

representing conservation laws over differential volumes into discrete algebraic equations over

finite volumes (or elements or cells) [60]. Similar to the finite difference or finite element

method, the first step is to discretize the computational domain into finite volumes and then for

each discrete element the governing equations transformed into algebraic equations. The system

of algebraic equations is then solved to compute the values of the dependent variable for each of

the elements. The resulting approximate solution is discrete, but the variables are typically placed

and stored at cell centers rather than at nodal points. "Finite volume" refers to the small volume

surrounding each node point on a mesh.

The FVM is a common approach used in CFD codes, as it has an advantage

in memory usage and solution speed, especially for large problems, high Reynolds number

turbulent flows, and source term dominated flows (like combustion) [65]. In the finite volume

method, some of the terms in the conservation equation are turned into face fluxes and evaluated

at the finite volume faces. Because the flux entering a given volume is identical to that leaving

the adjacent volume, the FVM is strictly conservative [66]. Moreover, the FVM can be

formulated in the physical space on unstructured polygonal meshes and mass, momentum,

18

energy conserved even on coarse grids. These characteristics have made the FVM quite suitable

for the numerical simulation of a variety of applications involving fluid flow and heat and mass

transfer and this method is used in many commercial or open-source CFD packages. With the

development of efficient and iterative solvers, FVM is now capable of dealing with all kinds of

complex physics and applications and results in the advances of CFD [66].

3.3.2 CFD applications

CFD is very powerful and spans a wide range of research and industrial application areas.

The following show six common applications for CFD.

I. Turbulence: The equations governing turbulent flows usually has no analytical solutions

and CFD method with turbulence model is utilized to predict the evolution of these

turbulent flows. Many studies have been carried out in this area and are devoted to

develop numerical methods and improve the simulation accuracy. This area includes

aerodynamics of aircraft and vehicles [67], hydrodynamics of ships [68] and

meteorology for weather prediction [69] and so on.

II. Thermal analysis: heat transfer coefficient in car radiator [70] and temperature

distribution in gas turbine [71].

III. Turbomachinery: flows inside rotating passages, diffusers, etc. [72].

IV. Interaction with structure: flow over a cascade of airfoils [73].

V. Chemical process engineering: mixing and separation, polymer molding [74].

VI. Biomedical engineering: blood flows through arteries and veins [75].

Typically, commercial CFD software are used for CFD simulation since it is easy to

perform and user friendly to interpret the simulation results. Some of the commonly used CFD

software are ANSYS Fluent, ANSYS CFX, FloWizard, PHOENICS, Star-CCM+, and

OpenFOAM [76].

19

CFD provides useful information on the underlying transport phenomena, such as

heat, momentum, and mass transfer. A number of crucial process parameters are correlated to

the fluid dynamic behavior.

In biotechnology, CFD allows to predict key properties such as mixing characteristics,

potential shear stress, and gradients of key parameters such as temperature, pH, or nutrient

concentration. For bioprocesses, based on this knowledge, CFD has been used to improve the

design of reactor, mixing or heat exchange. Therefore, CFD emerges as an important tool in the

biotechnology area and used in biotechnology processes, such as microbial fermentation,

mammalian cell culture, chromatography, ultrafiltration (UF), microfiltration, refolding, and

freeze-drying [76]. Recent developments in computer power and numerical accuracy have

extended its application to more complex or multiphase processes.

3.3.3 CFD advantages

The role of CFD in engineering predictions has become so strong that today it can be

viewed as a new ‘third dimension’ in fluid dynamics, the other two dimensions being the

classical cases of pure experiment and pure theory [77]. Quite some comparisons have been

made between the experiments and CFD simulations [61]. The unique advantages of CFD over

experiment-based approaches in fluid systems design have been concluded as follows.

I. Cost-effective: CFD simulation uses computer to perform virtual experiments and the

cost tend to decrease with the increasing power of computer.

II. Controlled parameter: CFD can study the conditions, which are difficult or impossible to

reach with experiments.

III. Comprehensive information: CFD allows to examine any location in the region of

interest and gives details of a set of thermal and flow parameters.

IV. Flexibility: CFD is able to choose the domain of interests or isolate one specific

phenomena to study.

20

The reliability is a main concern for CFD simulation and its accuracy depends on the mesh,

mathematical model and discretization method. Not all the models are reliable and many physical

phenomena still cannot be simulated.

21

4. Hydrodynamics applications from turbulence in natural flows to industrial bioprocesses

4.1 Numerical simulation of microorganism motility/motion in

natural systems Reports have considered the interactions between a complex flow and swimming micro-

organisms. DeLillo, Boffetta & Cencini [78] describe the spatial distribution of gyrotactic

spherical micro-organisms transported by three-dimensional turbulent flows generated by DNS

(direct numerical simulations). They show that coupling gyrotactic motion to turbulent flow

produces small-scale patchiness (smaller than the Kolmogorov scale) in the swimmer

distribution. Durham [79] examined whether gyrotaxis can generate cell patchiness in turbulent

flow using experiments and numerical simulations. They found that accumulation in

downwelling regions is the dominant means of aggregation also in turbulent flow and showed

that patchiness is not significantly affected by the Taylor–Reynolds number.

In the present investigation (Paper I), we performed DNS (direct numerical simulation)

of the dynamics of non-spherical swimmers in realistic turbulent flows. Motile micro-organisms,

modelled as prolate spheroids, in isotropic homogeneous turbulence. We demonstrated how the

clustering and trapping of swimmers without a preferential alignment observed in simple cellular

or vortical flows (e.g. Torney & Neufeld [80] Khurana et al. [81]) was significantly reduced in

a three-dimensional time-dependent flow and exceeds that of a Poisson distribution for elongated

cells, whereas spherical cells remain uniformly distributed. Elongated micro-organisms show

some level of clustering in the case of swimmers without any preferential alignment whereas

spherical swimmers remain uniformly distributed.

In addition, we performed a simulation study of small-scale patchiness in the distribution

of gyrotactic micro-organisms in vortical or turbulent flows. We confirmed that bottom heavy

cells tend to accumulate in downwelling flows, a fact exploited by settling larvae changing the

offset of the centers of buoyancy and of gravity to preferentially accumulate in updrafts,

22

favorable for dispersal, or downdrafts, favorable for settlement [82]. In particular, we

investigated how the gyrotactic clustering phenomenon in turbulence is modified by the

elongation of the ellipsoidal swimmers. When all other conditions are equal, we found that

clustering is highest for spherical gyrotactic swimmers and decreases the more elongated the

swimmers become. Due to their large sensitivity to the local shear, these elongated swimmers

react more slowly to the action of vorticity and gravity and therefore do not have time to

accumulate in a turbulent flow. These results show how purely hydrodynamic effects can alter

the ecology of micro-organisms that can vary their shape and their preferential orientation.

Compared to literature, our work expands recent studies of small-scale patchiness in the

distribution of gyrotactic micro-organisms in vorticial or turbulent flows. Our finding

numerically describes that micro-organisms that can actively change their shape, such as

Ceratocorys horrida [83], have an active control mechanism to alter their distribution and favor

encounters or uptake.

4.2 Animal cells response to hydrodynamic force Animal cells are delicate compared to microorganisms. A variety of animal cell types is

specialized for different tasks, and they need a quick exchange of substances and information

between each other. Therefore, they cannot be protected by a cell wall like bacteria. Animal cells

have a plasma membrane consisting of a phospholipid bilayer. This makes the cell flexible and

the interchange easier but on the other hand they lack the protection of the rigid cell wall [84].

Due to this fact, many animal or mammalian cells are shear or mechanical force sensitive, and

many studies have found that high levels of shear affect the viability and growth of cells [85, 86].

A sufficiently intense force will destroy cells outright, while a force of lesser magnitude may

induce various physiological responses, including death, without necessarily causing any

obvious physical damage [87]. Culture and processing of cells invariably expose them to

variously intense hydrodynamic forces. And the perceived “shear sensitivity” of animal cells has

been, and continues to be, a source of significant concern and confusion [88].

In cell culture, animal cells are either attached or in suspension. The cells attached to

microcarriers, which are supporting matrix allowing for adherent cells to growth, can lose their

23

viability once they have been removed from the microcarrier due to intense mixing. The removal

of cells attached to microcarriers is clearly a function of the agitation speed of the system. This

observation led to link the Kolmogorov microscale to cell damage [89, 90]. In contrast to

anchorage dependent animal cells, a large number of studies indicate that suspended animal cells

can withstand, and in fact, thrive, and produce functional products at significantly higher

agitation than hydrodynamic conditions that can remove cells from microcarriers [91].

For suspended cells, the response to the hydrodynamics force is highly cell line dependent

[86, 92]. The response of suspended mammalian cells to hydrodynamic forces is hard to predict

due to a lack of understanding. Thresholds of shear and bubble damage are likely not fixed values

but are cell type, cell line, media, process, flow regime and bioreactor dependent [93, 94]. Cell

damage caused by bubbles and foam can be significantly reduced by adding shear-protective

substances (e.g. Pluronic®–F68) [47]. CHO cell and HEK293 cell are common cell lines for the

production pharmaceuticals. To the best of our knowledge, there is no study of the shear response

of HEK293 or comparison between CHO cells and HEK293 cells. There are, however, some

studies characterizing the shear sensitivity for different human cells lines, which could have

similar properties as HEK293 cells. Mollet et al. [95] demonstrated that the human leukemic cell

line THP1 is approximately two order of magnitude more sensitive to EDR than CHO cells, as

measured in a flow contraction device. McCoy et al. [96] has demonstrated that other human

cancer cell lines, in this case a prostate derived line, LnCap, was also quantitatively more

sensitive to cell lysis and non-lethal, cell surface marker receptor changes were observed in

response to hydrodynamic forces. Chalmers et al. [88] combined the available information from

their group and other group with relevant studies on the Energy dissipation rate. They grouped

cell damage and EDR values into non-lethal and lethal effects on cells. In this comparison, they

used EDR, described in Chapter 3, as point of comparison. In short, based on these studies, cells

attached on a support such as microcarrier are more fragile than the cells in suspension; and CHO

cells are more robust than human cell lines.

4.3 Hydrodynamics in Bioprocess Hydrodynamics forces are present almost everywhere in biotechnological processes, such

as in the bioreactors, chromatography, ultrafiltration (UF), microfiltration and so on. Among

24

these, the bioreactor is usually the largest and most common fluid environment in bioprocess.

Bioreactors used in biotechnological applications are usually designed to meet the requirements

of the cell-culture environment by addressing parameters such as temperature, oxygen, pH,

nutrients, metabolites and biologically active molecules [97]. Mixing, which relates to the fluid

structure, is generally essential and is needed to enhance the mass transfer, which in turn often

significantly impacts both the product yield and quality [76]. Thus, considering

‘microenvironment’ [98] surrounding each cell is critical information for the whole bioprocess.

CFD modeling can overcome experimental limitations and enables a full characterization

of three-dimensional flow fields in bioreactors with simple and complex geometries. Sucosky et

al. [99] employed CFD to characterize the flow field within a spinner flask under operating

conditions used in cartilage tissue engineering. CFD techniques were used to predict mixing and

gas-liquid mass transfer in a 250 ml shake flask operating over a range of shaking frequencies

[100]. These authors compared their predictions with available experimental observations

reported previously for shake flasks and mechanically stirred bioreactors. Williams, Saini et al.

[101] applied Fluent CFD models to calculate flow fields, shear stress, and oxygen profiles

around nonporous constructs, which simulated the cartilage development in a concentric cylinder

bioreactor. Their species mass transport modeling for oxygen demonstrated that the fluid-phase

oxygen transport to the constructs was rather uniform, leading to the conclusion that, in this

bioreactor, chondrocyte growth would not be subject to oxygen limitation. Other examples can

be found for instance in the review of Singh and Hutmacher [102].

Cell damage potentially correlates with Kolmogorov eddy sizes, which is a function of the

ratio between the turbulence intensity (represented by the local energy dissipation rate, ε, and

the kinematic viscosity of the liquid, ν). When the Kolmogorov scale of the flow is close to the

size of a cell, the cells are experiencing the most severe damage. Energy dissipation rate was

proposed by Blustein and Mackros [103] to characterize cell damage for blood cells. EDR is

taking the nine potential types of fluid stress into account. Chalmers et al. [88] combined their

results with relevant studies on EDR, and grouped cell damage and the specific EDR values into

non-lethal and lethal effects on cells. At the most fundamental level, EDR does not take into

consideration all of the complexities of the flow, especially in turbulent systems. In contrast to

25

the Kolmogorov’s mixing microscale, EDR is a scalar parameter that is independent of the flow

regime (turbulent/laminar) and accounts for both shear and extensional components of three-

dimensional flow.

4.3.1 Hydrodynamics in Stirred tank bioreactor

The stirred-tank reactor (STR) is a major bioreactor type employed in aerobic fermentation

processes. This type of bioreactor has impellers for mechanical stirring, which is intended to

enhance mixing, to break bubbles and to increase the turbulence of the liquid medium [104].

Stirred-tank reactors provide a good mass and heat transfer rates and avoid the formation of

aggregates. A detailed mechanistic view on mixing, homogenization of the culture broth and

sufficient mass and heat transfer in stirred tank bioreactors for mammalian cell culture has been

discussed by Sieblist et al. [105, 106]. The hydrodynamics in STR including mixing and fluid

forces is discussed in this section.

4.3.1.1 Agitation and aeration in STR

In the stirred-tank reactors, the mixing level is a function of stirring speed and sparging

rate, i.e. the power input. Mixing increases oxygen transfer and provides a homogeneous

environment for cell growth. Therefore, it is essential for animal cell culture. Mixing in

bioreactors, introduced by the complex fluid structure (vortex and eddies), involves a range of

physical phenomena, such as mass transfer (nutrients, oxygen, CO2 and so on) and heat transfer.

On the other hand, the hydrodynamics forces related to stirring and sparging are responsible for

cell damage by inhibiting growth and/or productivity or changing the quality of the product [107].

These effects are very important for the bioreactor design, especially the impeller and sparging,

and make the optimization of mixing more challenging.

The mass transfer coefficient for oxygen (KLa), which describes the efficiency with which

oxygen can be delivered to a bioreactor for a given set of operating conditions, can be written as:

cd"ce= Kga(C∗ − Cg) (5)

26

where Cg is the oxygen concentration and C∗is the air-saturated dissolved oxygen concentration

in the liquid phase. The oxygen concentration difference across the liquid-side boundary layer

at the gas-liquid interface is the driving force for oxygen transfer. The KLa is widely used to

quantify the mixing efficiency and provision of oxygen in the bioreactor, and therefore it is used

as criterion towards optimization of the impeller design for better oxygenation.

The impeller accomplishes three major tasks, distributing uniform solids suspension,

mixing and dissolving oxygen into the liquid phase, and maximizing the gas-liquid interface area

[108]. For some decades, there has been an interest in impeller selection to give the most efficient

operation in the STRs. The type of impellers [109] and number of impellers [110] were studied

to understand how these parameters affect mixing and mass transfer. The most studied impellers

have been the standard Rushton turbines, different pitched blade turbines and various propellers

as well as combinations of two or three of them [111]. The main goal of the impeller selection

or design is to ensure sufficient mixing but create low shear hydrodynamic environment. For

this purpose, wide range of impellers have been studied and compared. The Rushton impeller,

commonly used in microbial cultures, is a radial flow impeller and create excellent mixing. It is

considered less suitable for cell culture as it creates high shear stress induced cell damage for

shear sensitive cells. Regarding the criterion of less severe local energy dissipation at the same

bulk average energy dissipation level, Ma et al. [98] suggested that hydrofoils are superior to

pitched blade turbines (PBT) and PBTs are superior to Rushton turbines. For suspended cells,

which is the most spread way used for nowadays’ biopharmaceuticals production process, the

perception that cells can be easily damaged by agitation canbe misleading. Animal cells are quite

robust and almost any impeller can be chosen [112]. Mixing efficiency can be enhanced by

increasing the impeller speed, decreasing the impeller distance [113, 114], and utilizing axial

flow impellers [115].

Impellers combination can also enhance mixing, and different impellers combinations

have been examined in different studies. Arjunwadkar et al. [113] compared nine dual-impeller

combinations comprising disk turbine (DT), pitched blade turbine downstream (PTD), and

pitched blade turbine upflow (PTU). They found that the DT–PTD combination was the optimum

due to its efficient gas hold-up and lower energy dissipation level. Mishra and Joshi [114]

27

reported that the DT–PTD combination gave higher fluid transport at the same level of energy

consumption as the DT–DT combination or a single DT. Many studies conclude that an impeller

creating an axial flow profile is often used on top of the bioreactor to facilitate good recirculation

of bubbles in the bioreactor and good axial mixing [94, 116]. This kind of impeller combination

can benefit for mixing in fed-batch and perfusion process, when the feed is added on the top of

liquid phase [98, 117].

Extensive studies have compared the effectiveness of various impeller configurations

using directly the KLa data. For example, Moucha et al. [115] present 18 impeller configurations

and eliminated the effect of gas phase without taking into account simultaneous nitrogen

transport by using pure oxygen. They concluded that the influence of the impeller configuration

on the KLa, which is a function of power number, is not clear. Fujasova et al. [118] studied the

mass transfer rate of seven types of impellers in 29 triple configurations. They found that 3RT

(Rushton turbine impeller), RT+2PBD (Pitch Blade downward) and RT+2PBU (Pitch Blade

upward) were the most efficient impeller combinations in triple configuration.

There are several methods for oxygen supply, such as surface aeration, bubble aeration

(sparging), bubble-free membrane aeration. In very small scale (screening scale) bioreactor,

surface aeration can ensure sufficient oxygen supply for not very high cell density. However, in

large scale bioreactor, sparging is commonly used to deliver efficient air-fluid mixing. As

reviewed in next section, the sparging induce more severe cell damage and create much higher

energy dissipation rate comparing to stirring. Therefore, proper selection or design of sparging

is critical for increasing aeration and minimizing cell damage. Czermak et al. [119] concluded

that macrospargers with a simple design (pipe, ring) are quite satisfying, even if they generate

large bubbles in the mm range, and therefore low KLa. Alternatively, microspargers (made of

ceramic, sintered stainless steel or polymeric materials such as polyethylene) were created [116,

120]. Due to the very small bubbles (several hundred µm range), high KLa value can be achieved

[119]. However, micro bubbles, of which diameter is closer to cell size, induce more cell damage

compared to macro bubbles due to the higher energy dissipation when there burst at the liquid

phase and create foam problem since the generated foam is quite stable. The detrimental effects

of bubbles or foam formation can be overcome by applying low volumetric gas flow rates, and

28

by using protecting agents such as Pluronic F68 [121]. Gary et al. [122] developed a model to

determine the best sparger hole size that can meet the requirements of aeration and CO2 stripping

at the same time.

The existence of turbulence enhances the mixing and oxygenation. However, turbulence

has long been lacking detailed physical analysis, defined as remains "unsolved" in the sense that

a clear physical understanding of the observed phenomena does not exist [123]. The first

principle of turbulence and the mathematical models describing the hydrodynamic forces

operating on cells in most bioprocessing equipment do not exist and probably never will [124].

Thus, numerical simulations become a promising method to obtain the fluid information and

fluid-cell interaction. The computational models show promising results and seem to be able to

predict the gas–liquid flow for any flow regime. Regarding the CFD simulation, the most

concerning issue is the prediction accuracy and researchers develop models for better agreement

with experiment results [125-127].

4.3.1.2 Hydrodynamic force related cells damage in STRs

In the stirred-tank reactors, agitation and aeration are needed to meet the oxygenation

requirement for animal cell culture. However, complex fluid structure (vortex and eddies)

produced by turbulent or complex laminar flow and bubble introduced by sparging can lead to

under-appreciated problem of fluid-mechanical cell injury [76]. Excessive shear can result in a

loss of viability and cell disruption. Even low-level fluid forces can cause injury-related cell

alterations without cell death, and these alterations can result either in dramatic adverse patient

responses or in reduced biological effectiveness [91]. Here, hydrodynamic forces for aeration

causing cell damage and culture mixing are discussed.

Turbulent flow is beneficial for mixing. However, the interactions within turbulent flow

create a very complex situation, resulting in a lack of full understanding for interfacial

phenomena in the cell–air–medium system. Moreover, the complexity of cellular mechanical

properties and the cells’ physiological responses to physical strain are not fully cleared.

Consequently, the actual mechanisms by which cells are damaged as a result of hydrodynamic

forces are still not well understood [98]. The energy dissipation rate has been widely used to

29

quantify local mixing performance in stirred tanks [128] even Kolmogorov’s mixing microscale

is based on the local energy dissipation rate. Therefore, it seems quite appealing to use this

parameter to correlate the hydro-dynamical cell damage in diverse culture devices. For several

decades, researchers have try to quantify the EDR in stirred tank bioreactors [129-131] and

correlated the cell damage to the EDR [85, 132]. The direct measurement of EDR in stirred tank

bioreactors can be a very difficult task and therefore studies have been carried out to estimate

the EDR. Zhou and Kresta [133] examined the effect of tank geometry (number of baffles,

impeller diameter, and off-bottom clearance) on the maximum EDR for four impellers (the

Rushton turbine, the pitched blade turbine, the fluidfoil turbine, A310; and the high-efficiency

turbine, HE3). They suggest that the maximum EDR value can be estimated as followings:

𝜀lmn = 𝐸 ∙ 𝑁q ∙ 𝐷V ∙ 𝜌 (6)

where N is the agitation speed (1/s); D is the impeller diameter (m); 𝜌 is the fluid density (kg/m3),

E is a non-dimensional constant depending on impeller type, impeller diameter/vessel diameter,

and off-bottom distance/vessel diameter. The average energy dissipation rate, ⟨𝜀⟩, is defined as

follows [134]:

⟨𝜀⟩ = s#∙t!∙u$

v (7)

where V is the reactor volume and the impeller power number 𝑃𝑜 depends on the stirrer type

and the flow characteristics. For example, PB impellers have 𝑃𝑜 numbers of 1.5 or below, while

RTs have 𝑃𝑜 values of 5 or higher [134].

Direct sparging aeration, which is effective and simple, is used to meet the oxygenation

requirement for cell culture. However, sparging related cell damage can effect suspended cell

culture and process scale-up [88]. It is widely accepted that cell-bubble interactions create more

severe physical cell damage in bioreactors than agitation [135-137]. Therefore, investigation of

bubble-related fluid mechanisms is beneficial to understand the cell damage and to design the

aeration. Three areas have been suggested for the regions of damage: the bubble injection region,

the bulk medium through, which bubbles rise, and the bubble disengagement region at the air-

medium interface [138]. In bioreactors where the sparger lies under the impeller, bubble coalesce

and breakup near the impeller region are potential causes of cell damage. Various theoretical and

30

experimental studies have demonstrated that the bubble disengagement region can be a major

source of cell damage [135, 138-140]. Bubble burst at the liquid-gas interface is the main cause

of damage in cells grown in suspension due to the high released energy [135, 141].

The energy dissipation rate is also used to quantify the bubble related dissipated energy

leading to cell damage. With computer simulation, Boulton-Stone et al. [142] and Garcia-

Briones et al. [85] calculated the maximum energy dissipation rate, which was many orders of

magnitude higher than the EDR typically generated by an impeller. The energy dissipation rate

during bubble disruption depends on the size of the bubble, being largest for the smallest bubbles.

This result has been experimentally validated by several studies [143, 144]. Comprehensive

comparisons of hydrodynamic forces on cells with various conditions, including agitation and

sparging, have been reviewed in several reports using EDR as criterion [88, 95, 98].

4.3.2 Hydrodynamics in Wave bioreactor

An ideal bioreactor should be perfectly mixed but also present a low shear stress or

hydrodynamic force not damageable for the cells. In 1999, Singh [145] introduced the disposable

wave-induced bioreactor, consisting of a bag on a rocking tray agitating the cell culture with a

back and forward wave-like movement. The Wave bioreactor, designed to provide excellent

mixing and gas transfer with reduced shear stress, can provide an interesting alternative for the

culture of cells highly sensitive to hydrodynamic conditions. The Wave bioreactor design is

simpler than the stirred bioreactor due to the absence of a sparger and presently, these systems

are available up to working volumes of 500 L. The cellbag for Wave bioreactor is single-use,

which reduces the procedures of cleaning and sterilization, the cross-contamination validation,

the risk of contamination, and enhances the process safety [146]. The single-use technology

could save more than 60% compared to stainless-steel based equipment in biopharmaceutical

production [147].

Various examples demonstrating the possibility to adapt processes performed in stirred

tank bioreactors or roller bottles to the Wave bioreactor have been reported for the culture of

animal cells [46, 148-151]. The Wave bioreactor is often reported for the production of immune

cells, stem cells or viral vectors for cell therapy, gene therapy or vaccine, since it is disposable,

31

generates satisfying culture homogenization, potentially at low shear stress [152-154]. Cells

adhering on a support such as microcarriers are much more sensitive to shear stress than

suspension cells, as reviewed by Chalmers [155]. Wave bioreactors have been shown to be very

adequate for the cultivation of cells on microcarriers [21, 156-158].

CFD has been employed to give detailed flow information inside the Wave bioreactor.

Öncül et al. [157] numerically investigated the key flow properties in the Wave bioreactors for

two reactor sizes (2 L and 20 L cellbags) at target agitation of 15 rpm and 7˚ motion angle, using

the CFD code ANSYS-FLUENT 6.3. Their results showed that the flows stayed in the laminar

regime, confirming that gentle mixing conditions took place. These authors also found that the

shear stress levels were below known threshold values leading to damage of animal cells.

However, there is no systematic study of fluid mixing and shear stress distribution in different

operating conditions, and lack of adequate information for choosing operating parameters to

achieve the desired mixing and shear stress.

To optimize the favorable conditions for the cells, such as high mixing and low shear stress,

requires a deep understanding of the hydrodynamic inside a Wave bioreactor bag, i.e. cellbag.

In the present investigation (Paper II), we performed detailed numerical simulations by Ansys-

FLUENT to characterize the flow conditions in a 10 L cellbag of Wave bioreactor.

Numerical simulations were carried out to investigate the fluid structures for 9 different

operating conditions of speed and angle. The influence of these operating parameters on the

mixing level and the shear stress induced by the liquid motion intensities were studied. It was

found that the mixing and shear stress increased with the rocking angle but that increasing speeds

were not systematically associated with increasing mixing and shear stress.

Our results pointed out as well that the natural frequency of the culture was an important

factor in the fluid motion and thus the vorticity, the mixing and the shear stress. The resonance

phenomenon, which was determinant in the effect of the speed, could be an explanation that the

lowest studied speed, 15 rpm, had the highest level of mixing and shear stress.

32

Fig. 4 2D simulation of the vorticity distribution for one operating condition of Wave bioreactor during one cycle time.

The present results extend the knowledge of shear stress and oxygen transfer in Wave

bioreactors. This information can help the selection of the operation settings generating favorable

hydrodynamic conditions of oxygen transfer while minimizing the shear damage. For the first

time, the phenomenon of natural frequency is used to explain the hydrodynamics in the

bioreactor. This phenomenon can also have an impact on the Wave bioreactor power input. This

study paves the way towards a systematic method to select these parameters as function of the

mixing need and shear tolerance of the cells.

4.3.3 Hydrodynamics in perfusion process

Cell separation techniques for perfusion cultures have been previously reviewed [51-53],

and a satisfying cell retention device is critical in perfusion processes. Among many cell

retention devices, the hollow fiber cartridge is an efficient option that can achieve very high cell

density for animal cells productions [46, 55, 149]. Tangential flow filtration based on hollow

fiber filter can minimize the fouling since the particles are not pressed into the filter membrane.

Introduced by Shevitz [159], alternating tangential flow filtration can further reduce the filter

fouling by creating a back flush in the filter membrane and the alternating flow direction at the

filter surface.

In perfusion process, the cell separation is carried out with a dedicated device. This latter

can be a source of shear stress supplementary to the stress occurring in the bioreactor. In the case

of perfusion process of stirred tank bioreactor equipped with tangential flow filtration, the shear

33

stress or the hydrodynamics force are coming from the two main parts, i.e. the bioreactor and the

cell retention device. Three main fluid mechanisms are responsible for cell damage in these

perfusion system, as illustrated in Fig. 5. 3D shear stress and hydrodynamics force related to

sparing and agitation occur in the bioreactor, and 2D shear stress in the hollow fiber. In cell

retention device, there is hydrodynamics force due to the fluid (cell broth) circulation and can

therefore potentially lead cell damage. The sparging and agitation were described in section 4.3.1;

the focus of this section is the shear stress in the hollow fiber.

Fig. 5 Hydrodynamics force for cell damage in perfusion system by tangential flow filtration in the case of ATF

4.3.3.1 Hydrodynamics in hollow fiber (pipe flow)

Originally developed in the 1960s, hollow fiber membranes are packed into cartridges with

hundreds or thousands of hollow fibers [160]. The basic element are hollow fibers, which are

made of a selectively permeable material, i.e. filter, they have a very small diameter and provide

a very large surface area. The hollow fibers are aligned substantially parallel to each other in

bundles and positioned within a cylindrical housing. Hollow fiber membrane devices have a

wide range of applications including ultrafiltration, artificial kidney dialysis and the cultivation

of animal cells [161]. Let us analyze the hydrodynamics in the hollow fiber lumen.

Consider the flow in a hollow fiber cartridge at steady state is fully developed laminar flow

in a pipe, i.e. Poiseuille flow [162, 163]

Feed in

Permeate

DiaphragmPump

2D Shear from hollow fiber

3D Shear from Bioreactor agitation

hydrodynamic force from sparging

34

cy%cz

= {G|z}~&

(8)

where Q is the volumetric flow rate, r is the radial position, and R is the radius of the pipe. The

shear rate and shear stress in the hollow fiber can be calculated as follows

γ = �y%�'(

(9)

where g is the shear rate. Combine the equation 9 and 10, in a pipe the shear rate is given by

𝛾 = G|}~!

(10)

When the fluid is a Newtonian fluid, this equation can be derived to express the EDR, ε as

ε = 𝜇 Bcy%czDV= 𝜇 ∙ E�|

)z)

})~* (11)

𝜇 is the dynamic viscosity. The value of dUz/dr is largest at the wall of the pipe, where r = R, so

the maximum local EDR in this case is

ε��� = 𝜇 ∙ E�|)

})~+ (12)

Many studies have showed that shear stress from pipe flow can induce lethal or non-lethal

cell damage. Augenstein et al. [164] observed cell damage at average wall shear stresses between

10–200 N/m2, for HeLa S3 and mouse L929, through different sizes of capillary tubes. McQueen

and Bailey [165] build a gradual converging and diverging flow channel so that laminar flow

could be maintained at substantially high Reynolds number, and found that the destruction of

mouse myeloma cells started at a wall shear stress of 1,500 dyns/cm2.

In the present investigation (Paper III), we studied the shear stress effect in two tangential

flow filtration systems (ATF and TFF) for perfusion processes of HEK293 cells secreting EPO.

HEK293 cells, interesting for the production of therapeutic glycoproteins, are known as more

hydrodynamic force sensitive. In order to obtain the optimal hydrodynamics conditions with

reduced mechanical damage, a better understanding of the shear stress level and its effect on the

cells is important.

35

Firstly, a theoretical relationship of the average shear stress in the ATF and the TFF

systems was established. Very few studies have compared the hydrodynamic environments in

ATF and TFF, and there is no theoretical study of shear stress in ATF and TFF. As a part of my

PhD work, the fluid mechanisms in ATF and TFF have been investigated. The shear stress in

these two systems was compared as part of Paper III. Different flow circulation modes are used

in the ATF and the TFF, as shown in Figure 2; the ATF flow is driven by a diaphragm pump in

two directions while it is directed in one direction in the TFF with a peristaltic pump. In a TFF,

the re-circulation fluid velocity is constant with time, while in an ATF it has a sinusoidal profile

varying between a maximum value and zero during a half cycle. Consequently, the shear rate in

the HF of a TFF, 𝛾!"", is constant while it is varying in an ATF. The maximal absolute value of

the shear rate in the ATF is equal to the constant flow in the HF and achieved twice per cycle

but the instantaneous absolute shear rate is lower most of the time. In the range 0 toπ, the average

absolute shear rate in the ATF, 𝛾#!" , can be expressed as a function of its maximum absolute

value (equal to 𝛾!""), as follows;

(13)

where u is the velocity of the fluid (m/s), �y,��

= �� is the constant shear rate occurring in the

TFF, ωis the frequency of the ATF cycle (s-1) calculated from the flow rate of the ATF, t is time.

Detailed calculation is given in Paper III. This result implies that the average absolute shear

rate is lower in the ATF than in the TFF and is V} ≈ 0.637 of the shear rate in the TFF, although

the instantaneous shear rate in the ATF system has a maximum equal to the constant shear rate

in the TFF system. So, the average absolute shear stress in the ATF, 𝜏#!" = 𝜏!"" ∙$%≈ 0.637𝜏!"".

The effect of the shear stress on HEK293 cells was investigated. This factor was confirmed

by experimental evidences both for the cell growth rate and the cell metabolic behavior. Further,

we characterized the different cell-shear interaction regions and deepened the understanding of

the cells cultured in hollow fiber-based perfusion systems. We identified a shear stress threshold,

common for ATF and TFF systems, above which reduced cell growth and altered cell

metabolism were observed for HEK293 cells. These values of flow rates and shear rates were

!!"# =∂U!∂y ∙

sin !" ∙ !"#!!

! = !!"" ∙2! ≈ 0.637!!""

36

here identified in an experimental model of short-term batch cultures and compared between

ATF and TFF systems. Our results indicate lower shear stress is achieved in ATF system. We

documented these cell responses to shear stress in both ATF and TFF system. We found shear

dependent cell growth and metabolism and further optimize the hydrodynamic conditions for the

perfusion process of HEK293 cells.

37

5. Perfusion process optimization Protein therapeutics are playing an increasingly important role for human health.

Productivity improvement and maintained protein quality, e.g. the structure, number and location

of post-translational N-glycan's [36], are the ultimate goals for biopharmaceutical process. In

order to achieve high productivity bioprocess, one can either improve the cell specific

productivity by genetic manipulation or push the cell to very high cell density at given cell

specific productivity. To achieve high productivity, different strategies can be applied: (i) cell

culture mode, which can achieve and maintain high cell density (ii) optimization of culture media

composition for cell growth and production (iii) maintenance of cell favorite environment.

Compared to batch and fed-batch, perfusion can provide a constant favorable environment for

the cells, which benefits metabolism and growth, cell health and the product of interest [47]. Due

to an increasing interest for perfusion processes, mammalian cell cultures in perfusion mode

have been used to produce various substances such as monoclonal antibody, recombinant

proteins and baculovirus [48, 166]. Cell favorite environment would ideally be free from

detrimental hydrodynamics force and toxic bi-products accumulation. Optimization of perfusion

process should address both aspects. First, design the bioprocess, especially the bioreactor, to

meet requirement of good mixing and low level of hydrodynamics force. The hydrodynamics of

perfusion process has been discussed in section 4. Second, understanding the cell metabolism is

a general requirement to design a proper feeding strategy and optimal productivity.

5.1 Animal cells metabolism in bioprocess Glucose and glutamine are very important compounds in culture media, providing the

required precursors for energy and the biosynthetic pathways to the cells. Glycolysis, TCA cycle,

and glutaminolysis are key metabolic pathways of mammalian cell [167]. Through glycolysis,

mammalian cells consume glucose as the main carbon source for energy production.

Glutaminolysis is the prevalent pathway through which mammalian cells assimilate organic

nitrogen for biomass synthesis while releasing ammonium as the main by-product [168, 169].

In batch cultivation, mammalian cell lines display an inefficient metabolic phenotype

characterized by high rates of glucose to lactate conversion [170] and partial oxidation of

38

glutamine (glutaminolysis) to ammonia and non-essential amino acids, e.g., alanine and aspartate

[171-175]. In consequence, animal cell culture exacerbated formation of lactate and ammonium

ion [176-178]. The accumulation of these by-products can cause a reduction of the growth and

product titer [179-181].

Lactate can cause a significant reduction of the pH of the medium, which can inhibit the

cell growth, but this effect can be avoided with a controlling system that maintains the pH within

an optimal range. In general, concentrations lower than 20 mM lactate do not affect cell growth

or productivity. A lactate concentration of 20-40 mM impairs the productivity and more than 40

mM inhibits cell growth [178]. For example, Cruz et al. [182] reported that the specific

productivity of BHK cells for the complex recombinant fusion protein production can be reduced

up to 40% when the lactate concentration is increased to 60 mM.

Ammonia or ammonium ion is recognized as one of the most important inhibitory

substances accumulating in cell cultures and has a stronger impact on cell culture. Ammonia is

a product of both cellular metabolism and chemical decomposition of glutamine in the medium

[183]. The effects of elevated concentrations of ammonia on mammalian cell cultures have been

reported, including the cessation of cell growth [184-190], a decline in productivity [191], the

inhibition of virus proliferation in cells [192, 193] and specific alterations of protein

glycosylation [194-196]. The precise mechanism of ammonium ion toxicity in cells is not clear,

even though this effect has been shown to depend on pH [181]. Many studies have assessed the

effect of ammonium ion on the culture of mammalian cells, particularly on CHO cells, the

experimental model studied has used exogenous ammonium ion sources (e.g. NH4Cl) [176, 189,

197, 198]. However, the effect of endogenous ammonium ion could produce a different response

in cell metabolism [181].

Among all the possible nutrients that can be added to culture media for cells, amino acids

might be among the most important both for cell growth and for productivity [199]. Many studies

have provided information about amino acids consumption of CHO cells, cell growth,

metabolism, IgG titer, and glycosylation in correlation with the dynamics of glucose and amino

acid consumption.

39

5.2 Feeding optimization in perfusion process The high cost is one of the limitation of perfusion process, such as medium cost. CSPR

(cell specific perfusion rate) is the ratio of perfusion rate and cell density, is the perfusion rate

per cell. Operating perfusion at constant CSPR creates consistent microenvironment for the cells

in the culture. For certain cell line, the ‘richness’ of the culture media, also known as ‘medium

depth’, is determining the CSPR. The “push-to-low” optimization technique was used to

examine the minimum CSPR, and it has been developed by the substantial decreasing of CSPR.

Pushing CSPR to low levels would significantly increase titer, approaching the range of fed-

batch titers [200]. Some studies have successfully employed a CSPR approach for the perfusion

rate and achieved very high cell density [46]. The use of cell specific perfusion rates provides a

straightforward basis for controlling, modeling and optimizing perfusion cultures [201, 202].

For process development purpose, it is attractive to develop small scale screening systems

with low cost and small footprint. A small-scale system is enable to utilize multiple reactions in

parallel and generate more experimental data at lower costs in a shorter time frame. Many

attempts to develop screening system include shake flasks [203], microfluidic reactors [204],

microtiter plates [205] and small-scale stirred reactors [206]. However, the limitation of these

screening systems is that they are not mimic of STR [207].

In Paper IV, we developed a screening system, which is a stirred tank bioreactor equipped

with a cell separation device based on HF for the perfusion process with <250 mL working

volume. The hydrodynamics of the bioreactor with different impellers or impeller combination

were investigated and mixing performance of these system were evaluated. Optimization of

oxygen transfer was performed in order to meet the requirement of high cell density. The system

was first used for CHO cells and then modified for HEK293 cells. Higher oxygen transfer and

more gentle flow condition were adopted for HEK293 cells. Very high cell densities above 100

x 106 cells/mL of mammalian/human cells were achieved in this system in a minimized working

volume below 0.25 L. Normal process temperature (37 °C) and mild hypothermia (33 °C) as

well as different cell specific perfusion rates from 10 to 60 pL/cell/day were applied to study the

performance of the culture.

40

This work provided as well evidence that a very low CSPR of 10 pL/cell/day can be

sufficient to establish a steady-state culture of HEK293 cells with a high sustained viable cell

density and viability. We found the volumetric productivity linearly increased with the cell

density, remaining unchanged with mild hypothermia however decreased when an extremely

low CSPR was applied appealing for medium improvement in future work. The shift from high

to low CSPR strongly reduced the nutrient uptake rates. Our results confirm HEK293 cells can

be used for intensified perfusion processes.

41

6. Conclusions and perspectives This study was mainly aimed at exploring the hydrodynamics of microbes/cells in nature

flow (paper I) and bioprocess (paper II, paper III and paper IV). In the first part of the work

(paper I), the effect of turbulent flow on marine life of motile micro-organisms with one

preferential swimming direction (e.g. gyrotaxis) was investigated by performing direct

numerical simulations. These results highlighted that clustering is highest for spherical

gyrotactic swimmers and decreases as the swimmers become more elongated. Our finding

explained that micro-organisms, such as Ceratocorys horrida [83] (Zirbel et al. 2002), have an

active control mechanism to alter their distribution and favor encounters or uptake.

The second part of the thesis explored the hydrodynamics in the perfusion process

occurring in a Wave bioreactor (paper II), hollow fiber filter acting as a cell separation device

(paper III) and in a stirred tank bioreactor (paper IV). Paper II discussed the flow conditions

under different rocking parameters (speed and angle) inside a Wave bioreactor, which was

recognized as an attractive option for cultivation of sensitive cells. Resonance phenomenon was

featured as the main reason of unexpectedly high fluid motion occurring at lower rocking speed.

The data indicated that the natural frequency of the bioreactor is an important factor in the

dynamics, the mixing and the shear stress of the fluid in this type of reactor. The results provided

a reference for achieving certain flow condition and shear stress level in a Wave bioreactor. The

present study paved the way towards a systematic method to optimize processing/perfusion

parameters as function of the desired mixing and shear tolerance of the cells.

Paper III probed the shear stress effect in two tangential flow filtration systems (ATF and

TFF) for perfusion processes of HEK293 cells secreting EPO. A theoretical relationship between

the average shear stress of ATF and TFF describing lower shear stress in ATF was established.

This relationship was validated by cultures of HEK293. The experimental results show that a

high shear stress provokes not only a reduced cell growth but also a different metabolism.

Surprisingly, moderate shear stress had positive effects on the cells for the glucose metabolism

and increased production of the produced recombinant protein (EPO). Different cell-shear

interaction regions were characterized. In particular, a threshold of shear stress is identified.

42

Paper IV studied the optimization of mixing performance in a bioreactor of perfusion

process. A small-scale perfusion bioreactor system using hollow fiber cartridges for tangential

flow filtration was developed to mimic a ‘real’ perfusion. The hydrodynamics of the bioreactor

with different impellers or impeller combination were investigated. The results showed that the

combination of a marine impeller at the bottom and a Rushton-type impeller at the top can

achieve the highest KLa. Furthermore, this work provides evidence that a very low CSPR of 10

pL/cell/day is sufficient to establish a steady-state culture of HEK293 cells with high sustained

viable cell density and viability.

As a whole, this thesis improved the understanding of interaction between microbes/cells

and fluid flow. Biological systems can be complex and difficult to control, engineers usually use

rules-of-thumb to guide for operating and optimization. This stems from the fact that a broad

range of disciplines is involved in bioprocessing and requires a large know-how. Scientists

working in this area are constantly confronted with biological, chemical, physical, engineering,

and sometimes medical questions [208]. Especially, fluid mechanics is critical in scale-up,

bioreactor performance, including mixing, oxygenation and shear stress. Turbulent flow usually

occurs in bioreactors and downstream and is an unsolved problem from the mathematical point

of view. CFD is used to solve turbulence problems and confirmed as inexpensive and reliable

tool. We obtained detailed fluid information in the nature turbulence and a Wave bioreactor

within a short time frame by CFD. Systematic study of cell response to shear stress have been

documented and adapted into previous studied [92]. Our results focused on the shear stress in

hollow fiber filters in perfusion process. It was clearly showed that the shear tolerance of

HEK293 cells was lower than today’s workhorses of the biopharmaceutical industry CHO cells.

To the best of our knowledge, our results were the first time i) point out a resonance phenomenon

have a predominant effect on the fluid motion in a Wave bioreactor; ii) set up a theoretical

relationship of average shear stress of ATF and TFF in perfusion process; iii) achieve very high

cell densities above 100 x 106 cells/mL of mammalian/human cells in a minimized working

volume below 0.25 L.

Recommendations for future researches can be to expand on four directions. i) We have

demonstrated the patchiness of microorganism depends on their shape and preferred orientation,

43

it would be interesting to simulate chemotaxis microbes [6], which is the phenomenon that

directs organisms' movements according to certain chemicals gradient in their environment. The

main challenge will be finding a good model to describe swimming strategy for chemotactic

swimmers in turbulence. ii) Limited by computing power, 2D simulations was performed to

characterize the fluid properties. 3D simulations could be carried out in the future in Wave

bioreactors with more accurate results and more detail hydrodynamics information. A large

amount of conditions and bioreactor sizes can be performed for simulations, enriching the

references for operating Wave bioreactors. iii) A systematic investigation of the global functional

response of HEK293 cells to shear stress by transcriptomics or proteomics can consider to be the

next step of paper III. This information can provide a deep understanding of the shear stress

effects on HEK293 cells occurring in TFF and ATF systems during perfusion operation. iv) In

perfusion processes, feeding medium optimization including perfusion medium and feeding

strategy is a promising area. Two parts of work can be performed to extend work of paper IV.

Firstly, one can design a proper feeding strategy, which will control the metabolite formation

and reduce the toxic byproducts accumulation. The other part is medium optimization, including

overall enrichment of all the components and a specific increase of the concentration of selected

components.

44

Acknowledgements There are many people that have earned my gratitude for their contribution in my research

and personal life. Without their support and encouragement, this thesis would never happen.

More specifically, I would like to thank five groups of people: my supervisors, my colleagues,

my collaborators, funding agencies, and my family.

My supervisors

I would like to express my first and foremost gratitude to my supervisor--Véronique

Chotteau. I very appreciate that she helped me to enter biotechnology field and open a new

research field after my Licentiate. Her guidance, advice and constant feedback support me

through my PhD study and research, many times I got inspirations during our discussions.

I also grateful to Prof. Luca Brandt for supervising my Licentiate research. Many thanks

to my supervisors, Gen Larsson and Antonius Van Maris, for discussions and keep me on the

right track.

Funding

Thanks to Chinese Scholarship Council for funding my Licentiate research. Thanks to the

Wallenberg Centre for Protein Research with co-funding of the Knut and Alice Wallenberg

Foundation and AstraZeneca for my PhD research.

Collaborators

Many thanks to Swedish Orphan Biovitrum for proving the Wave bioreactor system and

to Prof. Alper A. Öncül and Dominique Thévenin for generously kindly their data in Wave

bioreactor. Thank you to Johan Rockberg and Magdalena Malm for kindly provide HEK293

cells.

Colleagues

I would also like to thank all members in Industry Biotechnology: Guna, Andres, David,

Magnus, Gustav, Monica, Mariel Martin and all the others. Especially, thanks to colleagues in

45

Animal Cell Technology group (CETEG), Atefeh Shokri, Caronlina Åstrand, Ye Zhang, Erika

Hagrot, Hubert Schwarz, Liang Zhang, Nils Aronld Brechmann and Emil Sundäng Peters.

My Family

Finally, my deep and sincere gratitude to my family for their continuous and unparalleled

love, help and support. Especially, Mama, Baba, Meimei, thanks for immeasurable love and

support, I have owed you too much. I would also like to thanks my parents-in-law for help and

support. Yuli, thank you being my husband, friend and colleague, it is our magical decision to

come and explore Sweden together. The last word goes for Jasper, my little son, whose sweet

smile makes me feel full of energy.

46

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