OPTISOLV –
Development,
optimization and scaleup of biological solvent
production.
3rd International Meeting
Porto Mantovano, December 01,
2014
Prof. Dr.-Ing. Peter Götz
Katja Karstens
Sergej Trippel
Introduction
Life cycle of Clostridium acetobutylicum
 Acidogenesis
 Vegetative phase

Acid production
 Transient phase


Decline of µ
Cell adjustment to new environment
 Solventogenesis
 Clostridial phase


Solvent production (butanol, ethanol,
acetone)
When butanol in the broth exceeds 150 mM,
cells initiate sporulation or lyse
http://www.biologie.uni-rostock.de/mikrobiologie/Bilder/cellcycle.jpg
24.07.2014
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Purpose: find optimal cell growth condition for coupled CSTRs
Batch 1: unregulated pH
Batch 2: partially unregulated pH
•
•
•
•
Start and duration for 20 h at pH 5.6
Substrate: glucose
Substrate concentration: 100 g/L
Duration: 55 h
Start with pH 5.6
Substrate: glucose
Substrate concentration: 60 g/L
Duration: 36 h
Observations: during a pH-unregulated fermentation significant higher concentration
of butanol was reached, compared to a pH-regulated fermentation
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Batch 1:
60
12
50
10
glucose [g/L]
glucose fitted
acetic acid [g/L]
butyric acid [g/L]
acetone [g/L]
ethanol [g/L]
butanol [g/L]
30
20
8
6
4
40
0
30
0
20
2
10
10
0
Glucose [g/L]
40
acids and solvents concentrations [g/L]
Batch fermentation without pH regulation
time [h]
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Batch 2:
Batch fermentation with partially pH regulation
10
120
glucose fitted
acetic acid [g/L]
butanol [g/L]
ethanol [g/L]
acetone [g/L]
butyric acid [g/L]
glucose [g/L]
pH regulation off
Glucose [g/L]
100
80
8
6
60
4
40
2
20
0
60
50
40
30
20
10
0
0
acids and solvents concentrations [g/L]
140
time [h]
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Effect of undissociated butyric acid
„The influence of the pH can be correlated with the critical role of the concentration of
undissociated butyric acid in the medium: cellular growth is inhibited above 0.5 g/L and
solvent production starts at an undissociated acid level of 1.5 g/L
Reducing the intracellular acid dissociation by lowering the intracellular pH also
favours the production of acetone and butanol“
Influence of pH and undissociated butyric acid on the production of acetone and butanol in batch cultures of
Clostridium acetobutylicum
Fréderic Monot, Jean-Marc Engasser, and Henri Petitdemang
Appl Microbiol Biotechnol (1984) 19 : 422-426
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Effect of undissociated butyric acid
Batch fermentation without pH regulation
1.4
µ
µ fitted
undissociated butyric acid
h unregulated pH
0.8
1.2
1.0
0.6
0.8
0.4
0.6
0.2
0.4
0.0
0.2
-0.2
0.0
undissociated butyric acid conc. [g/L]
1.0
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
-0.4
time [h]
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Batch fermentations under different conditions
Effect of undissociated butyric acid
Batch fermentation with partially pH regulation
1.0
µ
µ fitted
undissociated butyric acid
0.8
1.2
0.6
1.0
60
58
56
54
52
50
48
46
44
42
40
38
36
34
32
30
28
26
24
22
20
18
0.0
16
-0.4
14
0.2
12
-0.2
10
0.4
8
0.0
6
0.6
4
0.2
2
0.8
0
[1/h]
pH regulation off
0.4
undissociated butyric acid conc. [g/L]
1.4
time [h]
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Continuous fermentation
Purpose: test for the possibility of Clostridium acetobutylicum cells to
regenerate their vegetative growth after a pH change
Continuous fermentation with transient pH shift
6
0.25
0.20
5
0.15
pH
µ [1/h]
4
0.10
3
0.05
2
µ at pH 5.3
time [h] vs µ at pH 4.3
pH
0.00
1
240
230
220
200
210
190
180
170
150
160
140
130
120
100
110
90
80
70
50
60
40
30
20
0
10
-0.05
250
time [h]
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Continuous fermentation
Continuous fermentation with transient pH shift
0.25
6
0.20
5
0.15
pH
µ [1/h]
4
0.10
3
0.05
2
µ at pH 5.3
time [h] vs µ at pH 4.3
pH
0.00
240
230
220
200
210
190
180
170
150
160
140
130
120
100
110
90
80
70
50
60
40
30
20
1
0
10
-0.05
250
time [h]
After residence at pH 4.3, cells were able to resume their growth at pH 5.3
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Continuous fermentation
Continuous fermentation, 2nd biorector
6
8
6
6
5
6
5
4
4
4
4
2
3
2
3
0
2
0
2
pH
8
butanol [g/L]
pH
butanol [g/L]
pH
time [h]
250
200
150
100
50
1
0
250
200
150
100
50
1
0
butanol conc. [g/L]
Continuous fermentation, 1st bioreactor
time [h]
The two stage continuous fermentation shows a suitability of cascade of
CSTRs for cell suspension concerning butanol production
D = 0.075 h-1
t = 13.33 h
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Experimentation
Acquired knowledge
1. Regarding these two fermentations the fermentation without pH regulation exhibits
higher butanol concentration in the broth compared to pH regulated fermentations
2. Concentration of undissociated butyric acid should be considered as an important
factor for both the switch to solventogenesis and enhancement of butanol
production (relevant for modeling)
3. Fermentation processes continue in coupled bioreactors as it was shown at
Two Stage Continuous Stirred Tank Reactor (TS-CSTR)
4. It is possible to shift cells from acidogenesis to solventogenesis and back by
regulation of external pH
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Outlook
Cascade of Continuous Stirred Tank Reactors (CSTR)
Advantages:
1. Fermentation in big total volume is supposed to yield high
amounts of solvents
2. The system can easily be modified
3. Fermentation process is coupled to the on-line sterilization without
termination of fermentation
4. …and this lead to a prolonged continuous fermentation
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Outlook
Cascade of Continuous Stirred Tank Reactors (CSTR)
pH 5.6
N2
N2
N2
N2
N2
Further work:
Series of 6 bioreactors
pH regulation in the first bioreactor (pH 5.6)
Nitrogen supply to all 6 bioreactors
Total resident time 24 hours
D = 0.25 h-1
Expected butanol concentration about 8 g/L
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Mathematical Modelling
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Objective:
Simulation of the ABE-fermentation
process in a continuous bioreactor
composed of several bioreactor stages
(I) with cells in suspension
cell A
cell A‘
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Objective:
Simulation of the ABE-fermentation
process in a continuous bioreactor
composed of several bioreactor stages
(I) with cells in suspension
(II) with immobilized cells
cell A
cell B
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Objective:
Prediction of outflow composition
Simulation of the ABE-fermentation
process in a continuous bioreactor
composed of several bioreactor stages
(A) with standard configurations
(B) with advanced configurations
(additional feeding points,
feedback loops, ...)
(I) with cells in suspension
(II) with immobilized cells
cell A
cell B
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Which kind of question should be answered with such a simulator:
 Under which conditions the continuous fermentation is stable?
 Which parameters are especially critical for the stability of the system?
 Which configuration leads to:
• highest butanol (or solvent) concentration
• maximal glucose yield
• maximal butanol productivity
• weighted objective function
 Which configuration leads to a fast establishment of a steady state?
 How to choose the conditions during a switch of the feeding point?
 Which biological model can be used to describe the evolution of the system?
Which questions would you like to answer with the aid of the simulation?
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Assumptions and Simplifications:
 about the nature of interactions:
 Homogeneity in the individual stages of the bioreactor
 Volume and residence time in the tubes can be neglected
 One homogenous biomass subpopulation per bioreactor stage
 Empirically determined production rates
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Assumptions and Simplifications:
 about the nature of interactions:
 Homogeneity in the individual stages of the bioreactor
 Volume and residence time in the tubes can be neglected
 One homogenous biomass subpopulation per bioreactor stage
 Empirically determined production rates
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Assumptions and Simplifications:
 about the nature of interactions:
 Homogeneity in the individual stages of the bioreactor
 Volume and residence time in the tubes can be neglected
 One homogenous biomass subpopulation per bioreactor stage
 Empirically determined production rates
 about the quantity of cases included in the model:
 The temperature is constant.
 The substrate is glucose.
 We work with the wild-type strain C. acetobutylicum DSM 792.
 The bioreactor stages have constant and equal volumes.
 Series of bioreactor stages without feedback loops
 (The biomass is immobilized and does not grow)
 (The pH is regulated and thus independent from the organic acid concentrations)
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Assumptions and Simplifications:
 about the nature of interactions:
 Homogeneity in the individual stages of the bioreactor
 Volume and residence time in the tubes can be neglected
 One homogenous biomass subpopulation per bioreactor stage
 Empirically determined production rates
 about the quantity of cases included in the model:
 The temperature is constant.
 The substrate is glucose.
 We work with the wild-type strain C. acetobutylicum DSM 792.
 The bioreactor stages have constant and equal volumes.
 Series of bioreactor stages without feedback loops
 (The biomass is immobilized and does not grow)
 (The pH is regulated and thus independent from the organic acid concentrations)
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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General concept of the simulator:
Agent-based modeling
A simulation based on individual „agents“ acting according to pre-defined rules of conduct.
The evolution of the entire system is the result of the actions of the different agents within
the system.
 Our model has two types of agents:
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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General concept of the simulator:
Agent-based modeling
A simulation based on individual „agents“ acting according to pre-defined rules of conduct.
The evolution of the entire system is the result of the actions of the different agents within
the system.
 Our model has two types of agents:
1:1x
1:
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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General concept of the simulator:
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Implemented Framework
 Implementation is based on object orientated programming with MATLAB
 It allows the definition of:
 model_parameters: t0, tEND, dt
 initial_parameters:
nb, V_stage, F_in,
c_feed (1x8) ={X; GLU; ACT; ETH; BUT; AA; BA; pH}
c_stages (nbx8)
 biological_model_parameters:
depending on the model,
i.e. qs_GLU, Ks_GLU, Y (1x7)={Y_X/S; Y_S/S; Y_ACT/S; ...}
 Simulation delivers graphs for each bioreactor stage and stores data in an ascii-file
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Example of a simple biological system
 glucose  biomass (Monod kinetic)
INPUT
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Example of a simple biological system
 glucose  biomass (Monod kinetic)
OUTPUT
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
29
How to correlate the biological parameters with the chemical
parameters?
 Empirical approach:
 Rational approach:
Find functions that fit the experimental data, i.e.
Monod kinetics
Reconstruct what is happing inside the cells,
i.e. pH-dependent enzyme production
- specific to conditions of the experiment
- depend on the quality of the experimental data
+ could reach very good accuracy (quantitative
statements)
+ more general models
- reduced complexity or very huge models
 limited accuracy (qualitative statements)
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Work in progress: Biological model based on an empirical
approach
First idea: Extension of the matrice from the simplified model
But: in our CSTR growth is not limited by glucose, but by an unknown factor (nitrogen,
phosphate, cell density ?)
 µ = f (c_Y (c_X), c_BA, c_BUT, pH)
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Work in progress: Biological model based on an empirical
approach
Second idea: Start with a model for the biofilm bioreactor
where µ = 0
rmeta = f (pH, c_GLU)
@ steady state
data from Raganati,
Procentese and Marzochella
(unpublished)
?
specific glucose uptake
-qs [g glucose g-1 biomass h-1]
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0
0.2
0.4
D [h-1]
0.6
0.8
-q_S bioreactor 2
 It’s more complex than that!
-q_S bioreactor 1
rmeta = f (pH, c_BA, c_AA, c_GLU, c_BUT, ..., D(?) )
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Perspective: Biological model based on a rational approach
 Models from literature to work with:
1. Simplified metabolic model taking into
account pH-dependent enzyme production
and activity
Haus et al. 2011 BMC Systems Biology, 5:10.
Millat et al. 2013 AMB, 97:6451-66.
Thorn et al. 2013 Math Biosci, 241(2):149-66.
 extension for glucose uptake and biomass
formation
2. Large metabolic models/ Genome scale
models considering not only C-balances but
also redox-balance
Papoutsakis 1984 Biotech Bioeng, 26(2):174-87.
Dash et al. 2014 Biotech for Biofuels, 7:144.
 adaptation to own purposes, introduce a
kinetic compound
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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Thank you for your attention!
OPTISOLV - Development, optimization and scale-up of biological solvent production. 3rd International Meeting. Porto Mantovano, December 01, 2014
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