BILS 2015 TU Wien exputec BioVT Pharmaceutical Engineering "Quicker Ways to a Scalable Process Based on Sound-Science Based Methodologies" Christoph Herwig

1. Quicker Ways to a Scalable Process Based on
Sound-Science Based Methodologies
Christoph Herwig
February 11th 2015

2. 18/04/16 Ch. Herwig
Our Mission in
Bioprocess Technology
2

3. 18/04/16 Ch. Herwig
Status quo of bioprocess design
The Scaling Tasks
Investigate! Redo! Hope!Stomach decision !
Process
Development
Piloting ManufacturingScreening
Scale-up Scale-up Scale-up
Productivity
Waste
Process Development Time
Revenue
Period
3

4. 18/04/16 Ch. Herwig
Challenges that can be solved by
faster process
development and
higher titers
prevent and
identify scale-up
effects
prevent failed
batches
4

5. 18/04/16 Ch. Herwig
How can these challenges be
addressed?
• Clear and proven workflows for data
analysis
• Tools to process data and ensure data
quality
• Efficient use of chemometric and
mechanistic data science tools
• 10

6. 18/04/16 Ch. Herwig
Presentation Workflow
• Goal: Quicker Ways to a Scalable Process Based on Sound-Science
Based Methodologies
• Processing Strategies to Understand the Production Platform and to
Allow Transferability from Scale to Scale and from Product to
Product “
6
Conclusions
Model
Building
Tutorial
Linking
MVDA
results to
metabolic
models
Information
extraction
and
Statistical
analysis
Big Data
Processing

7. 18/04/16 Ch. Herwig
GET A GRAB ON YOUR DATA
Big Data Processing
7

8. 18/04/16 Ch. Herwig
Typical industrial process data
8
0 0.2 0.4 0.6 0.8 1
0
500
1000
LEISTUNG MEAS (HK)
0 0.2 0.4 0.6 0.8 1
0
0.5
1
F Druck
0 0.2 0.4 0.6 0.8 1
0
50
100
Substrat 3 Menge
0 0.2 0.4 0.6 0.8 1
0
1
2
Dosierung 2
0 0.2 0.4 0.6 0.8 1
0
1
2
3
Summe Substrat
0 0.2 0.4 0.6 0.8 1
0
500
1000
biol. Wärmeleistung
20
30
Substrat 2
40
60
Product

9. 18/04/16 Ch. Herwig
Data import, data alignment and
contextualization
• USP & DSP automatic data import
• Excel spreadsheet, text documents, LIMS,
PIMS
• Data contextualization
• measurement units
• campaign, phase definition
• operators
• Data survey & overview
• overview plots
• data density plots
à All data in one format that can easily be analyzed and explored
Exputec Software
Excel
LIMS
PIMS
9

10. 18/04/16 Ch. Herwig
Principal Component Analysis:
Raw Data
• Principal component analysis on individual runs to quantify
variations and detect relationships among variables.
10
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Energieeintrag (HK)
Druck
S3 Menge
Dosierung 2
Summe Substrat
Bio Wärmeleistung
S2
Product
S1
Nebenprodukt
Component 1
Component2
B169012
1 2 3
0
10
20
30
40
50
60
70
80
90
100
Principal Component
VarianceExplained(%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%

11. 18/04/16 Ch. Herwig
GET A HINT
Information extraction and statistical analysis
11

12. 18/04/16 Ch. Herwig
Information extraction –
Completion of data sets
• Tools to derive meaningful descriptors
• Control quality tools
• Descriptors that describe the control quality of
process parameters, such as pH, pO2
• RMSE, outlier detection
• Bioprocess suite deriving scalable descriptors
• µ, qs, CER, OUR,
• Yields
• kinetic constants
• Calculation of missing entities via combination of
dvariables and sound science first principles
à Automatic extraction of meaninful descriptors for different
process phases, USP & DSP processes
0 5 10 15 20 25
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
feed time (h)
myreccalc(1/h)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Yxsreccalc(c-mol/-cmol)
12

13. 18/04/16 Ch. Herwig
PCA on scalable parameters for
individual runs
• Principal component
analysis on individual
runs to
• build mechanistic
hypothesis and
• detect number of
independent mechanisms
-1 -0.5 0 0.5 1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
qX
qP
qS3
qNP
qSsum
Component 1Component2
B162254
1 2
0
10
20
30
40
50
60
70
80
90
100
Principal Component
VarianceExplained(%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
-1 -0.5 0 0.5 1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
qX
qP
qS3
qNP
qSsum
Component 1
Component2 B163552
1 2
0
10
20
30
40
50
60
70
80
90
100
Principal Component
VarianceExplained(%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
13

14. 18/04/16 Ch. Herwig
Gathered data to statistical
information
14
• Raw data from DoE experiments transferred into information
• Influence of pH, pCO2 and pO2 on specific rates and yields
VCD
Glucose
SPECIFICRATEYIELD

15. 18/04/16 Ch. Herwig
Hypothesis Generation using
MVIA Results
• Processing of data (concentrations, flows) into
specific rates and yield coefficients (µ, qs)
• Tools
• Principal Component Analysis
• MLR
• PCR
• Factor Analysis
• Multi-way methods
• …
High Performer Low Performer
à Identify and understand trends and correlations
à Identify interactions across unit operations

16. 18/04/16 Ch. Herwig
NEEDING A SEGREGATED VIEW
OF YOUR CATALYST?
Cell-based Analytics
16

17. 18/04/16 Ch. Herwig
Analytical methods
§ Flow Cytometer
§ Producers / Non – producers
§ Apoptotic cells
§ Dead cells: viability staining
(Cedex)
§ Lysed cells (DNA, protein, LDH)
§ Product analytics (HPLC)
§ Product quality
800msec
25%

18. 18/04/16 Ch. Herwig
Segregation of Biomass
-Lysis-
18
Bio ma ss
l y sis
ph y sio l o g y
m a mm a l ia
0 500 1000 1500 2000 2500
-10
-8
-6
-4
-2
0
2
rdLDH(mU/(mL*h))
Initial LDH (mU/mL)
0 2000 4000 6000 8000 10000
-70
-60
-50
-40
-30
-20
-10
0
10
rdDNA(ng/(ml*h))
Initial DNA (ng/mL)
a) b)
0
10000
20000
30000
40000
50000
rpLDH(µU(mL*h))
rmeasLDH
rcorrLDH
d)
0 50 100 150 200 250
time (h)
0
50000
100000
150000
200000
250000
rpDNA(pg/(mL*h))
rmeasDNA
rcorrDNA
c)
0 50 100 150 200 250
time (h)
Degradation rates of a) DNA and b) LDH in fermentation supernatants with respect to initially detected extracellular concentrations of DNA or
LDH. Volumetric rates of c) DNA release into culture supernatants and d) LDH release into culture supernatants from direct measurements of
the respective marker (rmeas) and corrected for marker degradation (rcorr).

19. 18/04/16 Ch. Herwig
Segregation of Biomass
-Lysis-
19
Bio ma ss
l y sis
ph y sio l o g y
m a mm a l ia
b)
0 25 50 75 100 125 150 175
0.00
2.50E6
5.00E6
7.50E6
1.00E7
1.25E7
1.50E7
1.75E7
Cells/mL
time (h)
VCC
TCC
LCC
d)
0 50 100 150 200 250
0.00
2.50E6
5.00E6
7.50E6
1.00E7
1.25E7
1.50E7
Cells/mL
time (h)
VCC
TCC
LCC
c)
0 50 100 150 200 250
0.0
5.0E6
1.0E7
1.5E7
2.0E7
2.5E7
Cells/mL
time (h)
VCC
TCC
LCC
a)
0 50 100 150 200 250
0
2
4
6
8
10
12
Glc(g/L)
time (h)
0
1
2
3
Gln,Lac(g/L)
Glucose
Lactate
Glutamine
4
pH, 1
pH, 2 pH, 3
pH, 1

20. 18/04/16 Ch. Herwig
Segregation of Biomass
-Lysis-
20
Bio ma ss
l y sislysis – results
Lysis has a influence on the physiological interpretation!
lysis should be considered
ph y sio l o g y
m a mm a l ia
b)
0 50 100 150 200 250
-0.02
0.00
0.02
0.04
µ(1/h)
time (h)
µVCC
µLCC

21. 18/04/16 Ch. Herwig
LINK THE HINT TO
MECHANISTICS
Linking Cluster Analysis to Metabolic Models
21

22. 18/04/16 Ch. Herwig
3 4 5 6 7 8 9
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Process Time [h]
Variables
Data Analysis to understand lactate production
and consumption in Cell Culture
Prediction of qLac days by a
PLS-R model
Prediction itself not very useful,
since it is very time consuming
to measure all 66 variables
However, the weights of the
PLS-R model can be used to
find mechanistic links
11 Batches from a DoE
66 variables (Specific rates, MFA-results etc.)
8 Samples each
-2 0 2 4 6 8 10
-2
-1
0
1
2
3
4
5
6
7
8
Actual
Predicted
1 2 3 4
0
10
20
30
40
50
60
70
80
90
100
Number of PLS components
PercentVarianceExplainediny
22

23. 18/04/16 Ch. Herwig
0 2 4 6 8 10
0
1
2
3
4
Process Time [d]
0 2 4 6 8 10
0
1
2
3
4
Process Time [d]
0 2 4 6 8 10
0
1
2
3
4
Process Time [d]
Cluster 1
Cluster 2
Cluster 3
Cluster 4
PLS-R variable importance (VIP)
for qLac
Cluster detection (k-means cluster analysis) based
on PLS-R VIP to detect main correlations with
response (e.g.: qLac) à identify mechanistics
VIP > 1 variable is significant
VIP < 1 variable is insignificant
Phase detection by k-means cluster analysis (blues
lines)
Average VIPs
23

24. 18/04/16 Ch. Herwig
Use simplified metabolic flux
models
• Only the Central Carbon
Metabolism is considered
• Redox and energy metabolism
• Amino acid metabolism
• But which measurements are now
really omportantn in which phase?
24

25. 18/04/16 Ch. Herwig
Glc GPI ald GAPDH pgk eno pepkin g6Pdh prodh
1
2
3.1
4.1
5.2
6.2
7.3
8.3
Variables
ProcessTime[d]
0
0.5
1
1.5
2
Ala Arg Asp Asn Glu Gln Gly His Ile Leu Lys Phe Pro Trp Tyr Val mu qp Formate NH4 Lip Anap PPP_nox gpt asp-got aspg gls leu cat ile cat shmt phe deg serc3 mehf sink
1
2
3.1
4.1
5.2
6.2
7.3
8.3
Variables
ProcessTime[d]
Met Ser Thr Cit Pyr Suc pdh cisac icdh akgdh succdh fum maldh val cat thr deg Resp
1
2
3.1
4.1
5.2
6.2
7.3
8.3
Variables
ProcessTime[d]
0
0.5
1
1.5
2
arg cat glud his deg lys deg met deg Trp deg Tyr cat NH3 sink
1
2
3.1
4.1
5.2
6.2
7.3
8.3
Variables
ProcessTime[d]
0
0.5
1
1.5
2
Clusters variable importance(VIP) for
Lactate production/consumption
Cluster 1: Important variables for qLac for all time points; mainly related to glycolysis à Overflow / Lactate
Cluster 3: Important variables for qLac at the early phase (plus late phase); mainly related to TCA cyle activity
Cluster 4: Important variables for qLac at the late time points; probably related to nutrient limitation / stationary phase
Red: important to predict qLac; a value > 1 means the variable is significant
Blue: not important to predict qLac; a value < 1 means the variable is insignificant
Cluster 2: Less important variables for qLac
25

26. 18/04/16 Ch. Herwig
Mechanistics insights by target
oriented use of statistical tools
• Highly target-oriented analysis of large data sets by combination of
different data driven methods
• Do not get lost in the woods!
• Automated clustering of process
variables in to physiologically
meaningful groups with regard
to the response variable
(e.g.: qLac)
• Mechanistic insight can be
acquired from data driven
methods if the tools are
applied appropriately
26

27. 18/04/16 Ch. Herwig
GET IT UNDERSTOOD AND
OPTIMIZED
Mechanistic Model Building Tutorial
27

28. 18/04/16 Ch. Herwig
The Modelling Work Flow
Structure
Identification
Parameter
Estimation
Model Validation
Model
Refinement
Model Use
YES
NO
Experimental
Data
Priori
Knowledge
28

29. 18/04/16 Ch. Herwig
Mechanistic Model for
Glucose Uptake Kinetics
29
§ Propose hypothesis:
​ 𝑞↓𝐺𝑙𝑐 = 𝑓( 𝐺𝑙𝑐)
§ Formulate equation:
​ 𝑞↓𝐺𝑙𝑐 =​ 𝒒↓𝑮𝒍𝒄, 𝒎𝒂𝒙 ∙​[ 𝐺𝑙𝑐]/
[𝐺𝑙𝑐]+​ 𝑲↓𝑮𝒍𝒄
§ Estimate parameters
individually by minimizing cost
function ∑↑▒(​
𝑦↓𝑖, 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 −​
𝑦↓𝑖, 𝑚𝑜𝑑𝑒𝑙 )²
§ Compare with literature

30. 18/04/16 Ch. Herwig
Toolset for model calibration
• Model calibration:
• finding suitable estimates for model
parameters
• Sensitivity and identifiability analysis:
• Link to your measurements and
processing platform
30

31. 18/04/16 Ch. Herwig
Mechanistic optimization
• Use a kinetic model for model-based
optimization
• Development of mechanistic process
model
• In-silico optimization using optimization
algorithms
• Identification of optimal operating
conditions
• Control Implementation
à higher productivity using less experiments
à Knowledge on mechanistic relationships can
be transferred to the next product
31

32. 18/04/16 Ch. Herwig
CONCLUSIONS
32

33. 18/04/16 Ch. Herwig
Our approach for bioprocess design
33

34. 18/04/16 Ch. Herwig
Process
Parameters
Process
Variables
Vfeed
cfeed
Vgasin
cgasin
rpm
…
.
.
QAs
cproduct
cBDW
cO2out
cCO2out
… !
Predic?ve
Processing
Inverse
Analysis
Predic?ve
Processing
Structured
Process Development From good data via understanding
to prediction
34
CONSISTENT
DATA SET
INFORMATION &
EXPERIMENTAL DESIGN
KNOWLEDGE &
MODELLING
OPTIMIZED & PREDICTIVE
CONTROL

35. 18/04/16 Ch. Herwig
Monitoring
Process
Parameters
(Inputs)
Process
Variables
(Outputs)
Vfeed
cfeed
Vgasin
cgasin
rpm
…
.
.
QAs
cproduct
cXL
xO2out
xCO2out
…
Data Quality and Consistency
Physiological Process Control
Physiological Experimental Design
!
Mul?variate Control along
Design Space (MIMO) Mechanis?c Modeling
Real-?me Implementa?on
CELL
Plenty of tools available!
Need to be transferred for industrial use!
35
Physiological Informa?on Extrac?on
Experimental Automa?on
& Parallel Approaches
Model Predic?ve Control
Op?mal Control
Model Based Op?miza?on Predic?ve
Processing
Mechanis?c
Hypotheses Genera?on
Mul?variate Informa?on Processing SoR Sensors Inverse
Analysis

36. 18/04/16 Ch. Herwig
Take Home
Workflow
36
• Bioprocess development and manufacturing challenges can be
solved by process data science
• Proven workflows and a tailored toolset for bioprocess data
analysis and process optimization is necessary
Tranfer
knowledge to
other
process /
product / site
Optimize the
process by
mechanistic
models
Link your hint
to
mechanistic
hypotheses
Get a hint
using
information
extraction and
statiscial tools
Get a grab on
your data by
tailored
workflows

37. 18/04/16 Ch. Herwig
Investigate! Redo! Hope!Stomach decision !
Process
Development
Piloting ManufacturingScreening
Scale-up Scale-up Scale-up
Productivity
Waste
Process Development Time
Revenue
Period
Benefits through
shown Approach
37
Process
Development
Pilo?ng Manufacturing Screening
Scale-up
Increase produc1vity
by sustained op1miza1on, scalability and
elimina1on of fail batches
Cut Process Development Time by 50% Revenue Period
Inves?gate! Verify! Control & predict! Explore!
Scale-up Scale-up

38. 18/04/16 Ch. Herwig
Process
- undisclosed
Design & CQAs
- Freedom during design
- Proof of Concept for
controlling QTPP
Proven Pa?ent Benefit
- QTPP
- Efficacy, Safety
- Clinical Phase I, II & III
Process
- complete disclosure
New Product Biosimilar
Proof of Concept
Mechanis?cal Models via Physiology
First Principles
Metabolic Founda?ons
PlaZorm Knowledge
Risk Management
Verified Scale Down Models
Design Methodology
Relevance for
new products & biosimilars
Design & CQAs
- Fixed CQA limits from
ini?al approval
Proof of Similarity
38

39. 18/04/16 Ch. Herwig
Thank you
for your attention!
Univ.Prof. Dr. Christoph Herwig
Vienna University of Technology
Institute of Chemical Engineering
Research Division Biochemical Engineering
Gumpendorferstrasse 1a/ 166 - 4
A-1060 Wien
Austria
emailto: christoph.herwig@tuwien.ac.at
Tel (Office): +43 1 58801 166400
Tel (Mobile): +43 676 47 37 217
Fax: +43 1 58801 166980
URL : http://institute.tuwien.ac.at/chemical_engineering/bioprocess_engineering/EN/
https://www.facebook.com/BioVTatTUWien
39

40. 18/04/16 Ch. Herwig
BACK UP SLIDES
40

41. 18/04/16 Ch. Herwig
IMPLEMENTATION FOR PROCESS
CONTROL
Get it ON-LINE!
41

42. 18/04/16 Ch. Herwig
Computational environment for real-
time implementation and control
• Object oriented design of
different data storage and
processing components.
• Classes are able to store
data, perform specific
functions, and communicate
with each other
42
Process Information
Management System
OPC server
Calculations:
feeding rates
offgas analysis
volume calculation
outlier detection
…
Observer
Bioprocess object
yu(t0)
y
Model-predictive
controller & PID
u(t0)
xpred
u(t1)
y: measurements/outputs (e.g. offgas)
u: system inputs (e.g. feeding rates)
x: system states (e.g. concentrations)

43. 18/04/16 Ch. Herwig
Calculation of respiratory rates and
outlier detection
43
Bioprocess
Calculator offgas
Calculator outlier
Observer
FAIR,FO2,O2_offgas,CO2_offgas
OUR, CER, RQ
OUR, CER outliers removed
7.3565 7.3566 7.3566
x 10
5
0
0.1
0.2
0.3
0.4
Original signal
New signal 7.3565 7.3566 7.3566
x 10
5
0
0.5
1
G
Grubbs distance
7.3565 7.3566 7.3566
x 10
5
0
0.02
0.04
0.06
stdandard deviation
7.3565 7.3566 7.3566
x 10
5
0
0.1
0.2
0.3
0.4 New signal

44. 18/04/16 Ch. Herwig
Real-time implementation via particle
filter estimations
0 50 100 150
0
5
10
15
20
25
Time [h]
[g/l]
Biomass
0 50 100 150
0
5
10
15
20
A0 and A1 (soft-sensor)
[g/l]
Time [h]
0 50 100 150
0
0.05
0.1
0.15
0.2
Time [h]
[C-mol/h]
Biomass conversion rate (soft-sensor)
0 50 100 150
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time [h]
[g/l]
Glucose (measured)
0 50 100 150
-2
0
2
4
6
8
10
Time [h]
[g/l]
Gluconate (measured)
0 50 100 150
0
1
2
3
4
5
6
Time [h]
[g/l]
Penicillin (measured)
0 50 100 150
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
Time [h]
[mol/h]
OUR (measured)
0 50 100 150
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
Time [h]
[C-mol/h]
CER (measured)
measured
soft-sensor
A0
A1 measured
soft-sensor
measured
soft-sensor
measured
soft-sensor
measured
soft-sensor
measured
soft-sensor
measured
soft-sensor
44

45. 18/04/16 Ch. Herwig
LIFE CYCLE SOLUTION
GET IT DONE YOURSELF
45

46. 18/04/16 Ch. Herwig
CMLCM: Computational model
life-cycle management
46

47. 18/04/16 Ch. Herwig
Enabling Method
Implementation
Customiza?on &
Implementa?on
of
Tools & Solu?ons
Quan?fica?on
Data
Informa?on
Knowledge
Scale-Up
Op?miza?on
Risk Reduc?on
Quality
47
Manufacturers
CMOs

48. Get There Faster.
Exputec‘s Software Solutions by INCYGHT
§ Efficient implementation of our solutions at your site using
Exputec INCYGHT software
Batch 1 Batch 2 Batch N
Campaign analysis
. . .
MVIA
(Multivariate
Information
Analysis) for
extraction of
mechanistic
information out of
procee data
M-DoE
(Mechanistic
Design of
Experiments) for
reduced number
of experiments.
Multivariate
statistical methods
for exploratory
data analysis:
hypothesis
generation and
testing
Proven workflows
for identification of
sources of
variability,
increasing process
robustness, and
optimization.
48

49. 18/04/16 Ch. Herwig
a) Tradi?onal feed profile design b) Physiological feed profile design
Accelerate!
Wechselberger et. al. 2012
Speed up by using
physiological information in DoEs!
Ø Careful selection of physiological factors for the DoE
significantly reduces number of experiments
49

50. 18/04/16 Ch. Herwig
Ø Dynamic experiments increase information & throughput
Accelerate!
a) Quick iden?fica?on of scalable
rela?onships
b) Dynamic feeding profiles based
on specific substrate uptake rate
Zalai et. al. 2012
Speed up by using physiological
information & dynamics!
50

51. 18/04/16 Ch. Herwig
NECESSARY TO LOOK MORE
DETAILED INTO BIOMASS?
51
Bio ma ss

52. 18/04/16 Ch. Herwig
Segregation of Biomass
-Lysis-
52
Bio ma ss
l y sisCell Lysis
deIinition:
“loss of integrity of a cell (destruction of the cell membrane)”
motivation:
lysed cells were once "produced" and can be included in the description of
the growth kinetics
lysed cells could serve as a nutrient source
an inIluence on the product quality can not be excluded
measurement methods:
classiIication over intracellular substances in the supernatant
mammalians:
• LDH measurements in the supernatant
• DNA measurements in the supernatant
microbials
• C-balance
ph y sio l o g y
m a mm a l iaM ic r o bia l s

53. 18/04/16 Ch. Herwig
Quality by Design
Process Parameters
Temperature
Stirrer Speed
Dissolved Oxygen
pH
Air Flow
Pressure
Feedrate
Nutrient concentrations
Inducer concentration
Biomass concentration
Induction Time
Conductivity
Redox level
Strain
Expression cassette
…
Product quality attributes
Enzyme activity
Titer
Purity
Stability
Batch-to-batch variability
Efficiency
Cost of product
Space-time-yield
Protein folding
Glycosylation pattern
Viability
Ease of further processing
(downstream)
Potential risks for end-user
…
???
18/04/16 53

54. 18/04/16 Ch. Herwig
Process Parameters
Temperature
Stirrer Speed
Dissolved Oxygen
pH
Air Flow
Pressure
Feedrate
Nutrient concentrations
Inducer concentration
Biomass concentration
Induction Time
Conductivity
Redox level
Strain
Expression cassette
…
Product quality attributes
Enzyme activity
Titer
Purity
Stability
Batch-to-batch variability
Efficiency
Cost of product
Space-time-yield
Protein folding
Glycosylation pattern
Viability
Ease of further processing
(downstream)
Potential risks for end-user
…
Quality by Design
???
54

55. 18/04/16 Ch. Herwig
Current interpretation of QbD:
Cooking Recipe: DoE
Specific Ac?vity
[kU/gbiomass]
Induc?on Phase
Temperature
[°C]
Induc?on Phase
Feeding
Exponent k
“Design Space”
Data
CPPs
CQA
CQA
55
CPPs

56. 18/04/16 Ch. Herwig
DOE: Insignificant factors-
all for nothing?
• Factors turn out to be insignificant
• Variance cannot be explained by the
original factors
• Possible reasons:
• 1) wrong factors were chosen for
investigation
• 2) noise on experiment higher than effects
of factors
• How to proceed?
à Go beyond MLR analysis for the efficient
exploitation of DoEs!
-0,10
-0,05
-0,00
0,05
x_EFB
µ_FB
maxspectitersupg/g
N=9 R2=0,604 RSD=0,002799
DF=6 Q2=-0,347 Conf. lev.=0,95
-0,10
0,00
0,10
x_EFB
maxspectiterpelletg/g
N=9 R2=0,0
DF=6 Q2=-1,
Factor
Transformation
MVDA
Knowledge
56

57. 18/04/16 Ch. Herwig
-0,10
-0,05
-0,00
0,05
x_EFB
µ_FB
maxspectitersupg/g
N=9 R2=0,604 RSD=0,002799
DF=6 Q2=-0,347 Conf. lev.=0,95
-0,10
0,00
0,10
maxspectiterpelletg/g
Run DoE
MLR analysis on
original factors
Data Processing,
Analyze more
descriptors of the
process
MVDA
Exploratory analysis
Hypythesis generation:
New factors
Sort out co-linear
factors (e.g.variance
inflation factor)
MLR analysis on
transformed factors
Recycling of DoE Results
57
-1,5
-1,0
-0,5
0,0 µ
maxspectitersupg/g
N=9 R2=0,510 RSD=0,002881
DF=7 Q2=0,313 Conf. lev.=0,95
-1,5
-1,0
-0,5
0,0
µ
maxspectiterpelletg/g
N=9 R2=0,335 RSD=0,003226
DF=7 Q2=0,047 Conf. lev.=0,95

58. 18/04/16 Ch. Herwig
Linking metabolite production and
substrate uptake
58
Hypothesis:
overflow metabolism is
coupled to high glucose
consumption rate
a) Threshold
𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑≈−0.11 [​ 𝑚 𝑚𝑜𝑙/​
10↑9 𝑐𝑒𝑙𝑙𝑠 ∗ℎ ]
b) Linear equation
𝑓𝑜𝑟 ​ 𝑞↓𝑔𝑙𝑐 < 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑
​ 𝑞↓𝑙𝑎𝑐 =−0.25−2.21∙​ 𝑞↓𝑔𝑙𝑐
𝑅𝑀𝑆𝐸 = 0.028 [​ 𝑚 𝑚𝑜𝑙/​10↑9
∗ 𝑐𝑒𝑙𝑙𝑠∗ℎ ]

59. 18/04/16 Ch. Herwig
Vom Labor in den Prozess
2x
@50%
Toolset Integration
Data- Knowledge
Management
Process
Understanding
59

60. 18/04/16 Ch. Herwig
Sensitivity of model outputs to
variations in one model parameter
40 datasets were simulated by applying ±2% variations on a process value
60
0 100 200
0
10
20
30
40
cXLtot
Time [h]
0 100 200
0
0.5
1
cS1
Time [h]
0 100 200
0
5
10
cPEN
Time [h]
0 100 200
0
2
4
6
cPOX
Time [h]
0 100 200
-0.2
-0.15
-0.1
-0.05
0
OUR
Time [h]
0 100 200
0
1
2
3
4
5
cA0
Time [h]
0 100 200
0
10
20
30
40
cA1
Time [h]
0 100 200
0
2
4
6
8
10
cS2
Time [h]
0 100 200
0
0.05
0.1
0.15
0.2
CER
Time [h]
0 100 200
0
0.05
0.1
0.15
0.2
rX
Time [h]
0 100 200
-0.25
-0.2
-0.15
-0.1
-0.05
rS1
Time [h]
0 100 200
-0.3
-0.2
-0.1
0
0.1
0.2
rS2
Time [h]

61. 18/04/16 Ch. Herwig
Identifiability of parameter
subsets
• The colinearity index measures the degree of linear
dependence of model parameters.
• It equals unity if the columns are linearly dependent.
• If it exceeds 10-15, then the corresponding parameter
subset is poorly identifiable.
61
0 1 2 3 4 5 6 7
0
5
10
15
20
25
30
35
40
45
50
minimumofcollinearityindex
size of a parameter sets
Parameter Unit
Optimized
Value
Literature
Value Reference
𝒀 𝑳𝒂𝒄
𝑮𝒍𝒄
𝑚𝑚𝑜𝑙/𝑚𝑚𝑜𝑙 −1,11 −1,10 (Lee et al., 2003)
𝒒 𝑮𝒍𝒏,𝒂𝒗𝒓.𝒑𝒆𝒓 𝒎𝑴 𝑮𝒍𝒏 𝑚𝑚𝑜𝑙/(𝑚𝑀 ⋅ 𝑐𝑒𝑙𝑙 ⋅ ℎ) −1,99 ⋅ 10−11
−4 ⋅ 10−11 (Lee et al., 2003)
𝑲 𝑮𝒍𝒄 𝑚𝑀 8,75 2,25 (Aehle et al., 2012)
𝒒 𝑳𝒂𝒄,𝑪𝒐𝒏𝒔𝒖𝒎𝒑𝒕𝒊𝒐𝒏 𝑚𝑚𝑜𝑙/(𝑐𝑒𝑙𝑙 ⋅ ℎ) −1,80 ⋅ 10−10
𝒒 𝑮𝒍𝒄,𝒎𝒂𝒙 𝑚𝑚𝑜𝑙/(𝑐𝑒𝑙𝑙 ⋅ ℎ) −3,37 ⋅ 10−10
−1,8 ∗ 10−10 (Aehle et al., 2012)
µ 𝒎𝒂𝒙 ℎ−1 0,024 0,035 (Craven et al., 2013)
𝑲µ,𝑮𝒍𝒄 𝑚𝑀 3,81 4,8 (Dhir et al., 2000)
µ 𝒅𝒆𝒂𝒕𝒉,𝒎𝒂𝒙 ℎ−1 0,015 0,019 (Dhir et al., 2000)
𝑲𝒊,𝑮𝒍𝒄 𝑚𝑀 1,58
µ 𝒅𝒆𝒂𝒕𝒉,𝒄𝒐𝒏𝒔𝒕 ℎ−1 0,0015 0,00266 (Borchers et al., 2013)
𝑲𝒍𝒚𝒔𝒊𝒔 ℎ−1 0,004 0,04 (Craven et al., 2013)
𝒀 𝑵𝑯 𝟒
+
/𝑮𝒍𝒏 𝑚𝑚𝑜𝑙/𝑚𝑚𝑜𝑙 −0,54 −0,68 (Craven et al., 2013)

62. 18/04/16 Ch. Herwig
Validation of parameter estimation via
independent experiments
62