OPTISOLV – Development, optimization and scale- up of biological solvent production. 3 rd International Meeting Porto Mantovano, December 01, 2014 Prof

OPTISOLV – Development, optimization and scale-up of biological solvent production.

3rd International Meeting Porto Mantovano, December 01, 2014

Prof. Dr.-Ing. Peter GötzKatja KarstensSergej Trippel

2

Introduction

OPTISOLV - Development, optimization and scale-up of biological solvent production. 3 rd International Meeting. Porto Mantovano, December 01, 2014

http://www.biologie.uni-rostock.de/mikrobiologie/Bilder/cellcycle.jpg24.07.2014

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

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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 with pH 5.6 Start and duration for 20 h at pH 5.6• Substrate: glucose Substrate: glucose• Substrate concentration: 60 g/L Substrate concentration: 100 g/L• Duration: 36 h Duration: 55 h

Observations: during a pH-unregulated fermentation significant higher concentrationof butanol was reached, compared to a pH-regulated fermentation

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Experimentation



Batch 1: Batch fermentation without pH regulation

time [h]

0 10 20 30 40

Glucose [g/L]

0

10

20

30

40

50

60

acids and solvents concentrations [g/L]

0

2

4

6

8

10

12

glucose [g/L] glucose fitted acetic acid [g/L] butyric acid [g/L] acetone [g/L] ethanol [g/L] butanol [g/L]

5

Experimentation



Batch 2:Batch fermentation with partially pH regulation

time [h]

0 10 20 30 40 50 60

Glucose [g/L] 0

20

40

60

80

100

120

140

acids and solvents concentrations [g/L]

0

2

4

6

8

10

glucose fittedacetic acid [g/L] butanol [g/L] ethanol [g/L] acetone [g/L] butyric acid [g/L] glucose [g/L]

pH regulation off

6

Experimentation



Effect of undissociated butyric acid

„The influence of the pH can be correlated with the critical role of the concentration ofundissociated 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

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Experimentation



Effect of undissociated butyric acidBatch fermentation without pH regulation

time [h]

2 4 6 8 12 14 16 18 22 24 26 28 32 34 36 38

0 10 20 30 40

h

unregulated pH

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

undissociated butyric acid conc. [g/L]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

µ µ fitted undissociated butyric acid

8

Experimentation



Effect of undissociated butyric acid

Batch fermentation with partially pH regulation

time [h]

2 4 6 8 12 14 16 18 22 24 26 28 32 34 36 38 42 44 46 48 52 54 56 58

0 10 20 30 40 50 60

[1/h]

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

undissociated butyric acid conc. [g/L]

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

µµ fittedundissociated butyric acid

pH regulation off

9

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

time [h]

10 20 30 40 60 70 80 90 110

120

130

140

160

170

180

190

210

220

230

240

0 50 100 150 200 250

µ [1/h]

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

pH

1

2

3

4

5

6

µ at pH 5.3time [h] vs µ at pH 4.3 pH

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Experimentation



After residence at pH 4.3, cells were able to resume their growth at pH 5.3

Continuous fermentation with transient pH shift

time [h]

10 20 30 40 60 70 80 90 110

120

130

140

160

170

180

190

210

220

230

240

0 50 100 150 200 250

µ [1/h]

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

pH

1

2

3

4

5

6

µ at pH 5.3time [h] vs µ at pH 4.3 pH

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Experimentation



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

Continuous fermentation, 1st bioreactor

time [h]

0 50 100

150

200

250

butanol conc. [g/L]

0

2

4

6

8

1

2

3

4

5

6

butanol [g/L] pH

Continuous fermentation, 2nd biorector

time [h]

0 50 100

150

200

250

0

2

4

6

8

pH

1

2

3

4

5

6

butanol [g/L] pH

12

Experimentation


Acquired knowledge

1. Regarding these two fermentations the fermentation without pH regulation exhibitshigher 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

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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 solvents2. The system can easily be modified3. Fermentation process is coupled to the on-line sterilization without termination of fermentation4. …and this lead to a prolonged continuous fermentation

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Outlook


Cascade of Continuous Stirred Tank Reactors (CSTR)

Further work:Series of 6 bioreactorspH regulation in the first bioreactor (pH 5.6)Nitrogen supply to all 6 bioreactorsTotal resident time 24 hoursD = 0.25 h-1

Expected butanol concentration about 8 g/L

pH 5.6

N2 N2 N2 N2 N2

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Mathematical Modelling


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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‘


cell B

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Objective:


(I) with cells in suspension(II) with immobilized cells

cell A


cell B

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Objective:


(I) with cells in suspension(II) with immobilized cells

Prediction of outflow composition

(A) with standard configurations (B) with advanced configurations

(additional feeding points, feedback loops, ...)

cell A


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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?


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

Assumptions and Simplifications:

20 OPTISOLV - Development, optimization and scale-up of biological solvent production. 3 rd International Meeting. Porto Mantovano, December 01, 2014





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)




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)



Our model has two types of agents:

General concept of the simulator:

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Agent-based modelingA 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.


1:1

Our model has two types of agents:


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Agent-based modelingA 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.

1: x


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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

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Implemented Framework


glucose biomass (Monod kinetic)

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Example of a simple biological system


INPUT

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Example of a simple biological system

glucose biomass (Monod kinetic)


OUTPUT

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How to correlate the biological parameters with the chemical parameters?

Empirical approach:

Find functions that fit the experimental data, i.e. Monod kinetics

- specific to conditions of the experiment

- depend on the quality of the experimental data + could reach very good accuracy (quantitative statements)


Rational approach:

Reconstruct what is happing inside the cells,

i.e. pH-dependent enzyme production

+ more general models - reduced complexity or

very huge models

limited accuracy (qualitative statements)

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)

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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

@ steady state

rmeta = f (pH, c_GLU) ?

It’s more complex than that!

rmeta = f (pH, c_BA, c_AA, c_GLU, c_BUT, ..., D(?) )

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Work in progress: Biological model based on an empirical approach


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.00

0.01

0.02

0.03

0.04

0.05

0.06

specific glucose uptake -qs [g glucose g-1 biomass h-1]

-q_S bioreactor 2 -q_S bioreactor 1

D [h-1]

data from Raganati, Procentese and Marzochella (unpublished)

Models from literature to work with:

1. Simplified metabolic model taking into account pH-dependent enzyme production and activity

extension for glucose uptake and biomass formation

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Perspective: Biological model based on a rational approach


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.

2. Large metabolic models/ Genome scale models considering not only C-balances but also redox-balance

adaptation to own purposes, introduce a kinetic compound

Papoutsakis 1984 Biotech Bioeng, 26(2):174-87.Dash et al. 2014 Biotech for Biofuels, 7:144.

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Thank you for your attention!


Documents

OPTISOLV – Development, optimization and scale- up of biological solvent production. 3 rd International Meeting Porto Mantovano, December 01, 2014 Prof