54
Bacterial Consortia for the Improvement of Consolidated Bioprocessing Techniques 1

final_presentation

Embed Size (px)

Citation preview

Bacterial Consortia for the Improvement of

Consolidated Bioprocessing Techniques

1

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

State of the art of Consolidated

Bioprocessing

5

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

We used a toxin and antitoxin system to modulate C.

hutchinsonii population size

28

Any Gene Expression!

AHL

We used the Lux quorum sensing system for inter-

bacteria communication

29

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

Engineering C. hutchinsonii Requires Plasmids

Basic

Plasmid

33

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

What have we achieved?

42

• Characterized co-culture

What have we achieved?

43

• Characterized co-culture

• Designed population control system

What have we achieved?

44

• 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

Acknowledgements

49

Questions

50

Separating Populations

51

E. coli Growth on Dead C. hutchinsonii

52

Naïve Co-Culture data

53

Naïve Co-Culture data

54