Cultivation and
Bioprocessing Techniques &
Design of Experiments (DoE)
Dr. Simon K.-M. R. Rittmann
1. Closed batch (serum bottles, usually anaerobic)
2. Batch (e.g. Erlenmeyer flask, uncontrolled conditions)
3. Batch (bioreactor, (un)controlled conditions)
4. Fed-batch (bioreactor, controlled conditions)
5. Continuous culture (bioreactor, controlled conditions)
Rittmann et al. 2012, Rittmann et al. 2015
Cultivation &
Bioprocessing Techniques
Closed batch cultivation of Methanothermobacter marburgensis, Methanothermococcus okinawensis, Methanocaldococcus
villosus and Methanosarcina soligelidi in 120 mL serum bottles.
Closed batch
Mauerhofer et al. 2018
Closed batch
flush and
purge
weigh
determine
pressure
weigh
incubate
0 1 2 3 4 5 6
pressure
mass
4H2 + CO2  CH4 + 2H2O
Taubner & Rittmann, 2016
Closed batch
Cummulative H2O production of M. marburgensis, M. villosus and M. okinawensis in 120 mL serum bottles.
Growth conditions: T = 65, 80 and 60°C, respectively, V = 50 mL, n = 9, 3 and 3, respectively. Taubner & Rittmann, 2016
Closed batch
MER = methane evolution rate
OD = optical density Taubner & Rittmann, 2016
Closed batch
General mass balance
Bioprocessing techniques
Satzverfahren (batch)
Kontinuierliche Kultur (continuous culture)
Spektrum
Akademischer
Verlag, 2006
Batch & Fed-batch
Mass balance for batch and fed-batch
Batch & Fed-batch
Mauerhofer et al. 2018
Batch
Biological CH4 production – fed-batch
© Simon K.-M. R. Rittmann
Fed-batch
Fed-batch fermentation of Methanothermobacter marburgensis in the Eppendorf bioreactor system.
Fed-batch
Mauerhofer et al. 2018
Fed-batch
Growth (OD578nm) of M. okinawensis (left) and M. marburgensis (right) in fed-batch cultivation mode. Run 1 and run 2 are replicates.
Abdel Azim et al., 2017
Fed-batch
M. okinawensis M. marburgensis
Methane evolution rate (MER) of M. okinawensis (left) and M. marburgensis (right) in fed-batch cultivation mode. Run 1 and run 2 are replicates.
Abdel Azim et al., 2017
M. okinawensis
M. marburgensis
Fed-batch
Abdel Azim et al., 2017
Specific methane evolution rate (qCH4) of M. okinawensis (left) and M. marburgensis (right) in fed-batch cultivation mode. Run 1 and run 2 are
replicates.
M. okinawensis
M. marburgensis
Fed-batch
Results from the exponential fed-batch cultivation using M. marburgensis. For each run (colour legend) are presented the values of X, x, µ on the xaxis.
Run 3 (orange bar) had the highest biomass (X [g]) and biomass concentration (x [g L-1]).
Abdel Azim et al., 2017
Exponential fed-batch
M. marburgensis
Results from the exponential fed-batch cultivation using M. marburgensis. For each run the values MER, qCH4, CH4 offgas are presented on the xaxis.
Run 2 (red bar) showed the highest MER and qCH4. During run 6 the highest CH4 off-gas concentration was obtained.
Abdel Azim et al., 2017
M. marburgensis
Exponential fed-batch
Model for biomass concentration (x) calculated from the exponential fed-batch data of M. marburgensis.
Abdel Azim et al., 2017
Exponential fed-batch
Mauerhofer et al. 2018
Continuous culture
Mass balance for continuous culture
Continuous culture
Spektrum
Akademischer
Verlag, 2006
Liquid limitation
Continuous culture
Principles of continuous
culture bioprocessing
 Liquid limitation: thin line
 Gas limitation: bold line
Continuous culture
Seifert et al., 2014
Continuous culture
Continuous culture
Rittmann et al. 2012
vvm [L L-1 min-1] D [h-1]
qCH4[mmolg-1h-1]
Continuous culture
Rittmann et al. 2018
Rittmann et al. 2018
Continuous culture
Figure 1. An overview of dynamic
process conditions which can be
used in bioprocess development.
 A: shift-up
 B: shift-down
 C: ramp-up
 D: ramp-down
 E: pulse
 F: oscillation
Spadiut et al. 2013
Dynamic process conditions
Dynamic
condition
Modus
operandi
Changed condition Identification/Optimisation of
Shift
rapid change
of
parameter(s)
followed by
stable
condition(s)
physical parameters (D, T, rpm, vvm, light intensity, feed
profile)
chemical parameters (pH, nutrient concentrations,
osmolality)
physiological parameters (µ, qi)
maximum physiological capacity (µmax, qs,max)
productivity (qp,max)
maintenance energy
stress response
metabolism
morphology changes
limitations
Ramp
continuous
and slow
change of
parameter(s)
productivity
yields
growth and production kinetic
morphology
viability
limitations
physiological capacity
Pulse
sudden
change of
parameter(s)
productivity
uptake rates
yields
growth kinetics
short time cellular response
product and metabolite release
unscramble physiological and metabolic changes
strain characteristic parameters (qs, qp)
Oscillation
controlled
short up and
down ramp(s)
in a defined or
changing
frequency
and/or
amplitude
productivity
growth kinetics
metabolic and physiological optimisation
heat and stress response
quality improvement
metabolite formation
Dynamic process conditions
Spadiut et al. 2013
Rittmann et al. 2012
Identification of the maximum specific CH4 evolution rate (qCH4,max).
Dynamic process conditions
Bernacchi et al. 2014
Dynamic process conditions
Mauerhofer et al. 2018
Anaerobic cultivation techniques
Mauerhofer et al. 2018
Anaerobic cultivation techniques
Mauerhofer et al. 2018
Anaerobic cultivation techniques
Mauerhofer et al. 2018
Anaerobic cultivation techniques
• DoE was fouded by Ronald Fisher (UK), who basically
developed factoral experiments as well as ANalysis Of VAriance
• George Box developed basis for optimisation of DoE designs
(Response Surface Modeling (RSM)
• „To find out what happens if you change something, is necessary to change it.“
• „Esentially all models are wrong, but some are useful“
• Within the DoE concept Gen‘ichi Taguchi (Japan) developed a
qualitative approach (Taguchi-Methodology)
Gen‘ichi Taguchi
George Box
Ronald Fisher
Introduction – DoE
Why do we need DoE?
• Which (process/cultivation/environmental) parameters have
which influence on which variables (response of an organism)?
• How can we determine with a minimum of experiments which
parameters and interactions of parameters are
beneficial/detrimental for the cultivation of an organism in an
experimental design space?
Introduction – DoE
Why do we need DoE?
• Classical way to perform an experiment is to vary one
parameter (factor) at a time
 OVAT (one-variable-at-a-time)
Drawbacks
• Time consuming
• Interactions
• Maybe the optimum will not be identified
Introduction – DoE
Why do we need DoE?
• Determine parameters (independent variables), which
influence responses (dependent variables).
• Optimise cultivation (process)
• Improve growth, product quality, quantity...
DoE requires
• Planning of randomised experiments
• Dicipline
• Application of statistics
http://www.umetrics.com
Introduction – DoE
Source: http://www.gmpua.com/World/GMPManual/daten/autorenteil/kapitel_07/07_i.htm
(face centered)
DoE designs
1.) Screening
• Full or fractional factorial designs
• Resolution V designs are best to be used, but also
• Resolution IV designs are possible
2.) Optimization or modelling
• Response Surface Model (RSM)
• Cetral composite, Box-Behnken or Taguchi-design
3.) Verification
Types of DoE experiments
Screening
• Good for first experiment(s)
• Can consider lots of variables
• Usually only two levels of each variable
• Relatively few runs
• Limited if any ability to identify interactions
• (depending on the design)
• Risky?
P.G Mathews, 2012
DoE – Screening designs
P.G Mathews, 2012
Screening
• Useful for estimating main
effects and interactions
• Fractional factorial design can
be used for screenign many
factors to find the significant few
DoE – Screening designs
Color coding represents the design resolution:
green = resolution V design or higher, yellow = resolution IV design and red = resolution III design
Design Expert (Stat-Ease Inc., USA)
DoE – Screening designs
Factorial Effects Aliases
[Est. Terms] Aliased Terms
[Intercept] = Intercept
[A] = A + BCE + DEF
[B] = B + ACE + CDF
[C] = C + ABE + BDF
[D] = D + AEF + BCF
[E] = E + ABC + ADF
[F] = F + ADE + BCD
[AB] = AB + CE
[AC] = AC + BE
[AD] = AD + EF
[AE] = AE + BC + DF
[AF] = AF + DE
[BD] = BD + CF
[BF] = BF + CD
[ABD] = ABD + ACF + BEF + CDE
[ABF] = ABF + ACD + BDE + CEF
Factor Generator
E = ABC
F = BCD
Factorial Effects Defining Contrast
I = ABCE = ADEF = BCDF
Factorial Effects Aliases
[Est. Terms] Aliased Terms
[Intercept] = Intercept
[A] = A
[B] = B
[C] = C
[D] = D
[E] = E
[AB] = AB + CDE
[AC] = AC + BDE
[AD] = AD + BCE
[AE] = AE + BCD
[BC] = BC + ADE
[BD] = BD + ACE
[BE] = BE + ACD
[CD] = CD + ABE
[CE] = CE + ABD
[DE] = DE + ABC
Factor Generator
E = ABCD
Factorial Effects Defining Contrast
I = ABCDE
25-1 26-2
Resolution 5 design Resolution 4 design
DoE – Screening designs
Optimization
• Good follow-up experiment to a screening experiment
• Fewer variables - generally the most important ones
• Often three or more levels of each variable
• Provide a more complex model for the process
DoE – Optimisation designs
P.G Mathews, 2012
Central composite
• Each numeric factor is varied over 5 levels
• plus and minus α (axial points)
• plus and minus 1 (factorial points)
• usually three to six center points
If factorial factors have to be added the central composite design will be doublicated for every combination of the categorial factor levels
DoE – Optimisation designs
DoE – Examples closed batch
Taubner et al. 2018
CH2O [µL L-1] NH4Cl [g L-1]
turnoverrate[h-1]
Turnover rate in [h-1] as function of CH2O and NH4Cl concentrations. The turnover rate reached its maximum value at low CH2O concentration. At
high CH2O concentration the turnover rate is higher for low NH4Cl concentrations. This study was based on a DoE approach. Taubner et al. 2018
DoE – Examples closed batch
Organism:
Nitrososphera viennensis
Factors:
• Ammonia concentration 1, 2.5 and 4 mM
• Pyruvate concentration 0.1, 0.8 and 1.5mM
• Temperature 37, 42 and 47 °C
Calculation of:
• NH4 uptake rates [mmol L-1 h-1]
• NO2- production rates [mmol L-1 h-1]
• Cell counts
• Specific growth rate [h-1] from NO2- production rate
Stieglmeier et al., 2014
DoE – Examples batch
µ [h-1]
Temperature [°C] c(pyr) [mM]
The graph illustrates the effect of pyruvate concentration (c(pyr)) [mM] and temperature [°C] on the growth rate (µ) [h-1] of EN76T at a fixed ammonium concentration (c(NH4
+)) of 2.5 mM. Based on
the results of the closed batch cultivation and the subsequent generated response surface model (RSM), the optimal conditions for the cultivation of EN76T within this three-factorial design space
could be retrieved. The optimal cultivation conditions using µ as target value for maximization were calculated as follows: c(pyr) = 1.15 mM, c(NH4
+) = 2.03 mM, temperature = 42.02 °C, with a
desirability of 0.854. Data points of the individual experiments are presented in red or rose colour.
DoE – Examples batch
Mauerhofer et al. 2018
M. thermaggregans
DoE – Examples fed-batch
M. thermaggregans
Mauerhofer et al. 2018
DoE – Examples fed-batch
Run DM
[h-1]
pH DS
[L L-1 d-1]
T
[°C]
rpm vvm
[L L-1 min-1]
ratio
(H2/CO2)
DN
[L L-1 d-1]
CNH4+
[mmol L-1]
x
[g L-1]
MER
[mmol
L-1 h-1]
qCH4
[mmol
g-1 h-1]
CH4
offgas
[Vol.%]
Y(X/CH4)
[C-
mol/mo
l]
rx
[C-
mmol
L-1 h-1]
DoR-balance C-
balance
N-
balance
1 0.043 6.16 0.012 60 654 0.16 3.0 0.014 54.2 0.92 16.4 17.8 4.5 0.07 1.21 96.8% 75.3% 84.1%
2 0.055 7.83 0.013 60 1243 0.19 4.9 0.051 111.9 1.35 45.1 33.4 13.6 0.05 2.25 95.7% 101.8% 142.3%
3 0.211 7.84 0.010 60 1242 0.49 3.0 0.019 26.6 1.14 105.0 91.9 11.6 0.07 7.35 98.5% 80.7% 84.5%
4 0.057 6.16 0.049 61 1243 0.50 5.0 0.016 37.4 3.80 114.0 30.0 12.9 0.06 6.73 94.6% 99.5% 95.0%
5 0.21 6.16 0.053 61 1245 0.20 3.0 0.042 58.5 0.88 71.4 80.9 28.4 0.08 5.72 101.9% 95.6% 103.5%
6 0.059 6.15 0.011 70 1242 0.50 3.0 0.062 128.7 2.76 99.2 36.0 10.5 0.05 4.96 97.2% 124.0% 108.3%
7 0.202 6.15 0.012 70 1245 0.20 5.1 0.016 38.2 0.79 65.2 82.8 23.8 0.08 4.89 103.3% 93.7% 82.8%
8 0.161 7.85 0.009 69 650 0.16 3.1 0.044 64.3 0.27 17.8 66.0 5.0 0.08 1.33 101.6% 96.6% 100.7%
9 0.056 7.85 0.044 70 1245 0.19 2.9 0.014 44.4 1.75 65.2 37.2 26.0 0.05 3.00 97.0% 95.1% 110.4%
10 0.207 7.84 0.051 69 1245 0.49 5.0 0.048 44.7 1.29 111.3 86.7 12.7 0.07 8.13 98.2% 110.0% 101.9%
11 0.110 6.98 0.033 63 951 0.28 4.0 0.034 47.8 1.30 60.7 46.7 11.7 0.07 4.37 101.1% 82.4% 92.3%
12 0.111 6.99 0.025 64 947 0.29 3.9 0.018 37.8 1.26 61.5 48.7 11.5 0.07 4.31 103.7% 82.1% 94.0%
13 0.114 6.99 0.026 65 950 0.30 4.0 0.017 49.0 1.16 54.6 47.0 9.4 0.07 4.04 96.7% 129.8% 109.1%
14 0.107 6.99 0.020 65 946 0.29 3.9 0.026 45.6 1.05 59.5 56.7 11.0 0.06 3.39 95.1% 106.0% 105.2%
15 0.061 6.14 0.055 70 1245 0.20 5.1 0.045 100.3 1.77 67.8 38.2 25.8 0.05 3.32 101.1% 90.9% 109.3%
16 0.065 5.53 0.010 67 1242 0.49 2.9 0.055 96.6 1.98 99.2 50.0 10.7 0.04 3.97 95.4% 71.2% 112.5%
17 0.064 8.41 0.053 68 1242 0.19 2.9 0.004 18.2 2.06 66.4 32.2 27.1 0.06 4.01 106.0% 76.0% 93.0%
18 0.212 5.6 0.059 59 1242 0.19 2.9 0.047 39.7 0.84 71.2 85.0 31.5 0.08 5.43 103.5% 85.4% 84.3%
Multivariate model generation from a nona-factorial DoE
DoE – Examples conti culture
Bernacchi et al. 2014
Plot of nitrogen dilution rate (DN) [d-1] versus medium
dilution rate (DM) [h-1] in order to analyze growth to
product yield (Y(x/CH4)). Individual levels of Y(x/CH4) are
indicated through lines and boxes within the graph. The
analysis shows that Y(x/CH4) varies by adjusting DN, DM, or
both. An increase of DN reduces Y(x/CH4). However, an
increase of DM increases Y(x/CH4).
Plot of the nitrogen dilution rate (DN) versus the agitation speed in
order to analyze Y(X/CH4) [C-mol/mol]. Individual levels of Y(x/CH4) are
indicated through lines and boxes within the graph. Y(x/CH4) is
highest at the lowest investigated range of both factors.
DoE – Examples conti culture
Bernacchi et al. 2014
• Modde (Umetrics, Sweden)
• Design Expert (Stat-Ease Inc., USA)
• Statistica (StatSoft, USA)
• R Commander
DoE – Software