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Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Measurement• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
2
$$$
• 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
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
Co-cultures are better than monocultures
Co-cultures allow for specialty, robustness, and are inspired by nature.6
Co-cultures are better than monocultures
Cellulose Degrading Bacteria
Co-cultures allow for specialty, robustness, and are inspired by nature.7
Co-cultures are better than monocultures
Cellulose Degrading Bacteria
E. coli
extractionProduct
Simple sugars
Co-cultures allow for specialty, robustness, and are inspired by nature.8
CBP with communicating Co-Culture
Cytophaga hutchinsonii:
degrades cellulose, aerobic,
sequenced genome
and transformable
9
CBP with communicating Co-Culture
E. coli:
well characterized and produce a variety of products
Our product: biofuel
10
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
2
4
6
8
10
12
14
16
18
20
Time
Po
pula
tion
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 50
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time
Po
pula
tion
Bacteria 1
Bacteria 2
Preliminary Modeling Suggests that Co-Culture can
be Improved with Communication
11
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Measurement• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
12
Time
Co
nce
ntr
atio
n
Population 1
Product
Substrate Population 2
Modeling to Predict Co-Culture
Dynamics
13
Time
Co
nce
ntr
atio
n
Population 1
Product
Substrate Population 2
Modeling to Tune Populations –
Forward Design
14
Modeling Methods: Dynamic Flux
Balance Analysis
Start with the Flux Balance Analysis
Source: BiGG database, Orth et al. 2011
Av=0
Steady state
v
Maxμ=wTv
A
Stoichiometric Matrix
15
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 AnalysisThen consider extracellular dynamics
Flux Balance Model
Max μ=wTvs.t.: Av=0lb < 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=wTvs.t.: Av=0lb < 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
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 AnalysisThen consider extracellular dynamics
Flux Balance Model
Max μ=wTvs.t.: Av=0lb < 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
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture
• Grow E. coli and C. hutchinsoniitogether
• Separate populations
• Analyze carbohydrates
• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
18
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
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture
• Growing E. coli and C. hutchinsoniitogether
• Measurement
• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
20
http://femsle.oxfordjournals.org/content/362/14/fnv095.full
We developed a method of distinguishing
cells by shape
C. Hutchinsonii E. Coli
21
http://ucflow.blogspot.com/ Time
Voltage
Scatter height
Scatter width
We developed a method of distinguishing
cells by shape
22
We developed a method of distinguishing
cells by shape
Forward scatter height
C. Hutchinsonii cells
E. Coli cells
debris
Forward scatter width
23
Carbohydrate Analysis: Sulfuric Acid-
Phenol Method
+
y = 1.2971x - 0.0472R² = 0.9872
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Ab
sorb
ance
49
0 n
m
concentration (%wt/v)
round 1
round 2
round 3
average
linear regression
StandardsNaïve Co-culture Data
0 20 40 60 80 100 1203
4
5
6
7
8
Time [h]
Co
nce
ntr
atio
n [g
/L]
Total Cabohyrates Data
Total Carbohyrates Fitted Data
24
Modeling Predicts Naïve Co-Culture
Data
Data Model
25
0 20 40 60 80 100 120 1400
1
2
3
4
5
6
7
8
9
10
Time [h]
Co
nce
ntr
ation
[g
/L]
Biomass C. hutchinsonii
Biomass E. coli
Filter paper
Glucose
Xylose
Cellodextrins (2-7)
Xylo-oligosaccharides (2-7)
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
26
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
C. hutchinsonii E. Coli
AHL
Simple sugars
Circuit design tethers C. Hutchinsonii growth to E. Coli
30
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
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
32
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
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
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
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
37
SugarspdcadhB (BBa_K1705000)
Ethanol
‘tesA (BBa_K1705002) FadD (BBa_K1705003)
atfA(BBa_K1705001)
Producing biodiesel with E. coli
38
-10
0
10
20
30
40
50
60
0 5 10 15 20 25 30
Val
ue(
mA
U)
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
-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
Outline
Consolidated Bioprocessing
Designing our system
Implementation Details
• Cellulose as substrate
• Monocultures• Co-cultures• Bacteria choice
for CBP
• Modeling• Naïve co-culture• Circuit design
• C. Hutchinsoniias a new chassis
• Biodiesel
41
• Characterized co-culture
• Designed population control system
• Progress toward implementation
What have we achieved?
45
• 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
Stre
ngt
h
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 diacylglycerolacyltransferase (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
• Characterized co-culture
• Designed population control system
• Progress toward implementation
• Collaboration
What have we achieved?
47
• Addresses issue of population instability
• Modular system
• New use of cellulosic waste
• Future challenges:
• Scalability
• Genetic instability
Broader Context
48