3. $$$
• 120 million tons of
stover
• Cheap
• Not a food source
…. but difficult to
process
Corn Stover, Agriculture and Agri-food Canada, 2008.
Enzymes
Energy
Cellulose is lying around in abundance
3
4. CBP uses a single
reactor for all
production steps.
The CBP State of the
Art relies on
monoculture-based
processing, which isn’t
reliable
Consolidated BioProcessing will be our
vehicle
4
6. Co-cultures are better than monocultures
Co-cultures allow for specialty, robustness, and are inspired by nature.
6
7. Co-cultures are better than monocultures
Cellulose
Degrading
Bacteria
Co-cultures allow for specialty, robustness, and are inspired by nature.
7
8. Co-cultures are better than monocultures
Cellulose
Degrading
Bacteria
E. coli
extraction
Product
Simple sugars
Co-cultures allow for specialty, robustness, and are inspired by nature.
8
9. CBP with communicating Co-Culture
Cytophaga hutchinsonii:
degrades cellulose, aerobic,
sequenced genome
and transformable
9
10. CBP with communicating Co-Culture
E. coli:
well characterized and produce a
variety of products
Our product: biofuel
10
11. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
2
4
6
8
10
12
14
16
18
20
Time
Population
Bacteria 1
Bacteria 2
Herbert H.P. Fang, Heguang Zhu, Tong Zhang, Phototrophic hydrogen production from glucose by pure and co-cultures of Clostridium butyricum and Rhodobacter sphaeroides,
International Journal of Hydrogen Energy, Volume 31, Issue 15, December 2006, Pages 2223-2230, ISSN 0360-3199
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time
Population
Bacteria 1
Bacteria 2
Preliminary Modeling Suggests that Co-Culture can
be Improved with Communication
11
15. Modeling Methods: Dynamic Flux
Balance Analysis
Start with the Flux Balance Analysis
Source: BiGG database, Orth et al. 2011
Av=0
Steady
state
v
Max
μ=wTv
A
Stoichiometric
Matrix
15
16. Then consider extracellular dynamics
Flux
Balance
Model
Extracellular
dynamic mass
balances
Uptake/secretion
kinetics
Modeling Methods: Dynamic Flux Balance
Analysis
Modeling Methods: Dynamic Flux
Balance Analysis
Then consider extracellular dynamics
Flux Balance
Model
Max μ=wTv
s.t.: Av=0
lb < v < ub Extracellular Mass Balances
dX
dt
= μX
𝑑𝑆,𝑃
𝑑𝑡
= v
siX
Uptake/Secretion Kinetics
vs = fs(S,P)
S, P
vs
μ, vp
Modeling Methods: Dynamic Flux
Balance Analysis
Then consider extracellular dynamics
Flux Balance
Model
Max μ=wTv
s.t.: Av=0
lb < v < ub Extracellular Mass Balances
dX
dt
= μX
𝑑𝑆,𝑃
𝑑𝑡
= v
siX
Uptake/Secretion Kinetics
vs = fs(S,P)
S, P
vs
μ, vp
16
17. Then consider extracellular dynamics
Flux
Balance
Model
Extracellular
dynamic mass
balances
Uptake/secretion
kinetics
Modeling Methods: Dynamic Flux Balance
Analysis
Modeling Methods: Dynamic Flux
Balance Analysis
Then consider extracellular dynamics
Flux Balance
Model
Max μ=wTv
s.t.: Av=0
lb < v < ub Extracellular Mass Balances
dX
dt
= μX
𝑑𝑆,𝑃
𝑑𝑡
= v
siX
Uptake/Secretion Kinetics
vs = fs(S,P)
S, P
vs
μ, vp
We Overcame Many Significant Challenges in
Implementing Our Models
Gomez, J.A., Höffner, K. and Barton, P. I.: DFBAlab:
A fast and reliable MATLAB code for Dynamic Flux
Balance Analysis. BMC Bioinformatics 15(1)
(2014).
Valid metabolic
networks
Kinetic
parameters Computationally
feasible
Non-native
pathways
17
19. Naïve Co-Culture to tell us what to improve
E. coli
Salts
Filter paper C. hutchinsonii
humboldtmfg.com
drpinna.com
lbc.msu.eduStandardsingenomics.org
19
27. Pros of Continuous Culture
• Higher productivity
• No turnover time
Cons of Continuous Culture
• Susceptible to genetic instability
• Susceptible to contamination
• Impractical in industry
Changing Focus from Continuous to Batch
Culture
27
28. We used a toxin and antitoxin system to modulate C.
hutchinsonii population size
28
30. C. hutchinsonii E. Coli
AHL
Simple sugars
Circuit design tethers C. Hutchinsonii growth to E. Coli
30
31. Modeling predicts circuit failure and success
modes
Not enough
starting AHL
Threshold level of
E. coli too high
AHL
Simple
sugars
Success: C. Hutchinsonii to E.
Coli ratio improved
Success: C. Hutchinsonii to E.
Coli ratio increased
31
34. Origin of Replication Part Design
• PCR amplified chromosomal
origin of replication (oriC)
• BioBrick and MoClo compatible
• Part# Bba_K1705010
C. hutchinsonii Genome
Cloning Site
oriC
pSB1C3
oriC
34
35. Testing Selection Markers
• Inserted oriC in both CmR and
KanR backbones
• Cm resistance to 10 ug/mL
• Kan resistance at 30 ug/mL
Wild-Type Transformed
35
36. C. hutchinsonii Accomplishments
• Opened new, interesting chassis
• Culture and transformation methods
• Modularized origin of replication (Bba_K1705010)
• Selection with Kan and Chlor
• Future directions
• Native constitutive promoter (Bba_K1705012)
• Native RBS (Bba_K1705011)
36
39. -10
0
10
20
30
40
50
60
0 5 10 15 20 25 30
Value(mAU)
Time(min)
90% Methanol Standards
Methanol Ethyl Palmitate Standard Palmitic Acid Standard 1:1 Ethyl Palmitate:Palmitic Acid
Ethyl
Palmitate
Palmitic Acid
Developed a Method to Detect Biodiesel
39
40. -5
5
15
25
35
45
0 5 10 15 20 25 30 35 40 45
Experimental Samples with 90% Methanol
Not Induced Induced
Fatty Acid Ethyl Esters
(FAEEs)
We developed a method to separate a fatty acid
from its ethyl ester
40
45. • Characterized co-culture
• Designed population control system
• Progress toward implementation
What have we achieved?
45
46. • Characterized co-culture
• Designed population control system
• Progress toward implementation
0
0.2
0.4
0.6
0.8
1
1.2
BBa_J23100 BBa_J23102 BBa_J23107 BBa_J23106
Strength
Anderson Promoter Strengths
Part BioBrick #
C. hutchinsonii Origin of
Replication
BBa_K1705010
C. hutchinsonii RBS BBa_K1705011
C. hutchinsonii promoter BBa_K1705012
alcohol dehydrogenase (adhB) BBa_K1705000
Acyl-CoA diacylglycerol
acyltransferase (aftA)
BBa_K1705001
Leaderless thioesterase (‘tesA) BBa_K1705002
Acyl-CoA synthetase (fadD) BBa_K1705003
pLux BBa_K1705006
luxR BBa_K1705007
luxI BBa_K1705008
luxI LVA BBa_K1705009
RelE BBa_K1705004
RelB BBa_K1705005
What have we achieved?
46
47. • Characterized co-culture
• Designed population control system
• Progress toward implementation
• Collaboration
What have we achieved?
47
48. • Addresses issue of population instability
• Modular system
• New use of cellulosic waste
• Future challenges:
• Scalability
• Genetic instability
Broader Context
48
