WHAT I

LEARNED AT

CSHL SYNBIO

AKA NERD CAMP

https://secure.flickr.com//photos/99852795@N06/show/

COURSE INFORMATION

16 students

• 1 tenured undergrad university

• 1 Office of Naval Research

• 1 Industry

• 4 Postdocs

• 9 Graduate students

4 instructors

• Jeff Tabor/ Rice University

• Julius Lucks/ Cornell University

• Karmella Haynes/ Arizona State University

• David Savage/ UC-Berkeley

• Participants

• Richard Murray/ CalTech

• Eric Klavins/ UW

• Pam Silver/ Harvard

• Adam Arkin/ UC-Berkeley

• Jeff Boeke/ JHMI

• Dan Gibson/ JCVI

• Michelle Chang/ UC-Berkeley

• Harris Wang/ Columbia

• Justin Gallivan/ Emory

• Michael Jewett/ Northwestern

• Ron Weiss/ MIT

• Andy Ellington/ UT

• Jeff Hasty/ UC-San Diego

Cold Spring Harbor Laboratory

Schedule

• 9-11 Lecture

• 1-3 Lab work

• 3-4:30 Lecture

• 4:30 – 6 Lab work

• 7 – 8 Lecture

• 8 - 11 Lab work

• 11 – 12 Bar work

• 12 - ??? Lab work

LABORATORY

TECHNIQUES

• Golden Gate Cloning

• Gibson Cloning

• MAGE

• TXTL cell free breadboarding

SYNTHETIC BIOLOGY

Definitions:

(1) The modern synthesis of biology and engineering.

(2) The use of biological components to design circuits,

devices and systems.

PARTS CIRCUITS DEVICE SYSTEMS

To be able to make circuits need to be able to assemble multiple parts at a time

GOLDEN GATE ASSEMBLY: NO MORE

MULTIPLE CLONING SITES

Engler et al. PLOS ONE 3, e3647 (2008)

Engler et al. PLOS ONE 4, e5553 (2009)

Standard cohesive-end cloning cuts and ligates at the recognition site.

Requires the use of a MCS in the vector.

Limitation: only 1 part at a time

• Type II restriction enzymes

cut N bases away from

recognition site.

• BsaI recognizes GGTCCTC

• Skips a base

• Leaves 4 base overhang.

• Digestion and ligation

occur in the same step.

• As digestion occurs the GOI is

irreversibly ligated into the

destination plasmid

• Multiple GOI can be ligated

into a single vector

because of specific

overhangs.

• No need for MCS

• Very cheap

GOLDEN GATE ASSEMBLY ALLOWS MULTIPLE

PARTS TO BE ASSEMBLED AT ONCE

PCR WITH GG ALLOWS THE

ASSEMBLY OF ANY GOI INTO ANY

PLASMID

Limitation: Designing multiple inserts can be time consuming

GIBSON ASSEMBLY ALLOWS

ASSEMBLY OF MULTIPLE PARTS AT

THE SAME TIME

• No restriction enzymes

needed.

• DNA fragments are

created with >25 bp

overlap to adjacent

sequence.

• All fragments are mixed

into a single reaction

containing exonuclease

to create sticky ends

Similar ways: SLIC, CPEC, SLiCE, and GeneArt

GIBSON ASSEMBLY

VERY EASY TO USE

• Up to 100 mb assembly was made.

• Along with Yeast TAR, this was used to create the minimal Mycoplasma

mycoides into Mycoplasma capricolum.

• < $10 per reaction

MAGE: CAPABLE OF MODIFYING MORE

THAN ONE GENE AT A TIME

• Multiplex genome

engineering and

accelerated

evolution

• Existing genomic

templates are used

as scaffolds to

produce new

engineered

variants.

• Uses synthetic

Okazaki fragments

to mutate the

genome.

• Allows for in situ

directed evolution Wang et al. Nature 460 (2009)

Wang, Church. Meth Enzymol, 498 (2011)

MAGE

• Deletion of mutS increases efficiency 100X

• Knock out mutS, MAGE, and then enable mutS.

• No selection marker required

• Steps

• OD ~ 0.6

• Heat shock/chill 4C

• Electroporation of DNA

• Recover cells

• Repeat cycles

• Limitations:

• Only working in E. coli.

• Time consuming

EXAMPLES OF MAGE

USES

Expand genetic code

Replace all TGA or TAG stop codons with TAA

Will free up codon for another amino acid (xeno DNA)

Multiple gene knockouts

Hypermutations

Optimize RBS

Phenotypic plasticity / Robustness

Directed Evolution of biosynthetic pathways

CELL FREE SOLUTIONS ALLOW

FOR THE PROTOTYPING OF

SYNBIO CIRCUITS

PROTOTYPING BIOLOGICAL CIRCUITS USING TXTL AND RNA ATTENUATORS

Instructor: Julius Lucks, PhD: Cornell University

TA: Mellissa Takahashi: Cornell University

Chris Fall, PhD: Office of Naval Research

Shaima Al-Khabouri: Montreal, Canada

Vipul Singhal: CalTech

SYNBIO CENTRAL GOAL:

ENGINEER GENE CIRCUITS

Independent Target

Regulation

Signal

Integration

Signal

Propagation

Signal

Amplification

Regulatory

Feedback

Independent Target

Regulation

Signal

Integration

Signal

Propagation

Signal

Amplification

Regulatory

Feedback

Arbitrary gene network

Decompose

Synthesize

RNA IS VERSATILE AND

REGULATES GENE

NETWORKS AT MANY LEVELS

RNA Functions

Transcription Regulation

mRNA Stability

Translation Regulation

Splicing Regulation

Chromosome Regulation

Gene

5’ UTR 3’ UTR

Gene

5’ UTR 3’ UTR

Transcription

Translation

Stability StabilityRegulation

RNA’S VERSATILITY IS A TOOL TO

ENGINEER EXPRESSION

Gene

5’ UTR 3’ UTR

RNA

Molecular Interactions

Small Molecule

Protein

Transcription

Translation

Stability StabilityRegulation

Control

RNA’S VERSATILITY IS A TOOL TO

ENGINEER EXPRESSION

Larson et. al., Cell 132, 2008

GCCGAG

A

AGGUUA

A

C G A U UG

Folding

Free Bases Can

Pair to Other RNAs

G C C G A G A AGGUUAA4 Bases

UUUUUUUU

Intrinsic Terminator Hairpin

DNA

RNA

RNA Polymerase

RNA Transcription

RNA FOLDS CAN REGULATE

TRANSCRIPTION

RNA-SENSING TRANSCRIPTION

SIMPLIFIES NETWORKS

Transcriptional regulator: pT181 – RNAI/RNAII

In vivo – E. coli

ON OFF

21

RNA STRUCTURES CAN CONTROL TRANSCRIPTION

IN VIVO

TWO MAIN CHALLENGES FOR

SYNTHETIC DEVICES

• Living systems are

nonlinear systems

• Unpredictable behaviors

• Evolution

Richard M. Murray, Caltech CDS/BBESB 6.0, 9 Jul 2013

• Add’l proteases, RNAses, etc

• Modulate pH, ATP, etc

• Vary component concentrat’n

• Extract: cytoplasmic proteins

• Amino acid mix

• Buffer + NTP, RNAP, etc

TXTL

TXTL

vesic

le

origam

i

Implementation iterations (slow)

Cell-Free Biomolecular Breadboards

Key characteristics of the cell-free (TX-TL) breadboard (Shin & Noireaux, ACS Syn Bio, 2011)

• Inexpensive and fast: ~$0.03/ul for reactions; typical reactions run for 4-6 hours

• Easy to use: works with many plasmids or linear DNA (PCR products!)

- Can adjust concentration to explore copy number/expression strength quickly

• Flexible environment: adjust energy level, pH, temperature, degradation

TX-TL breadboard components

• Bulk reactions: 10 ul, 10-25 variations ([DNA], [inducers], etc) in a plate reader

• Droplet-based microfluidics: 0.3 ul, 50-100 variations + merge/split/etc

• Vesicle-based reactions (“artificial cells”): 1-100 fl, 100-1000 phospholipid vesicles

• Spatial localization using DNA origami: 1000 copies w/ 10-100 nm spatial ctrl

• Reaction-based modeling: MATLAB/Simbiology toolbox, with resource limits

Related approaches: Litcofsky et al (Nature Methods, 2013); Chappel et al (NAR, 2013)

2

Model

Prototyping

Debugging

System

Model

Model

Design iterations (fast)

Model

http://www.openwetware.org/wiki/breadboards

Sun et al, JoVE 2013 (a)

Richard Murray

• Can we use cell free systems to ‘model’ RNA genetic circuits?

• Co-develop experimental and computational methods

• Goal: create a paradigm shift in the way we prototype circuits

26

IT IS POSSIBLE TO PROTOTYPE RNA

CIRCUITS USING CELL FREE

TRANSCRIPTION/TRANSLATION SYSTEM

http://www.openwetware.org/wiki/breadboards

PHASE I – TESTING

COMPONENTS

27

• Basic Expression of GFP or RFP

module

• DNA/RNA load on the TXTL

resources

• Antisense Repression Titration

• Cross Talk

• Plasmid and Linear DNA

We can express Att-1 (Attenuator) GFP in TX-TL system in Plasmid and

Linear forms

28

ABLE TO EXPRESS RNA NETWORK

IN TX-TL SYSTEM

DATA

0

10000

20000

30000

40000

50000

60000

0 20 40 60 80 100 120

RF

U

Time (min)

Plasmid

0.25 nM

0.5 nM

1 nM

2 nM

0

10000

20000

30000

40000

50000

60000

70000

-30 20 70 120

RF

U

Time (min)

Linear

0.125 nM

0.25 nM

0.5 nM

1 nM

1.000 A -1 GFP

An -1

A -2 GFP

An -2 Anti = antisense

Att = attenuator: reduces

the power of a signal

29

DATA

0

50

100

150

200

250

0 2000 4000 6000 8000

RF

U

Time (sec)

GFP expression with and without antisense sequence

Att1-GFP + scrambled DNAAtt1-GFP + antisense1

Att2-GFP scrambled

Att2-GFP + antisense2

0

50

100

150

200

250

Att1-GFP + scrambled

DNA

Att1-GFP + antisense1

Att2-GFP scrambled

Att2-GFP + antisense2

RF

U

Mean GFP expression with and without antisense sequence -

2 hour time-point

A -1 GFP

An -1

A -2 GFP

An -2

1.014

ANTISENSE REPRESSION WORKS IN

TXTL.

0

2000

4000

6000

8000

10000

12000

14000

Att1-GFP + scrambled Att2 + anti1 Att1 + anti2 Att2-GFP + scrambled

RF

U

GFP Expression - Cross Talk

30

SLIGHT CROSSTALK BETWEEN ANTISENSE

MOLECULES AND OTHER ATTENUATOR

DATA

A -2 GFP

An -1

A -1 GFP

An -2

1.012

31

qPCR verification of RNA levels

DATA

2.001

0

200

400

600

800

1000

1200

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45 50 55 60 65

RF

U

mR

NA

(n

M)

time (min)

GFP

anti1

anti2

GFP (Fl)

Inhibit Rnase in experiment

Tease out transcription and degradation individually

modelingexperiment

PHASE II – TESTING A NOVEL 3 LAYER

CASCADE

Anti 2

Att2 Anti1 Anti1

Att1 GF

P

Ribozyme

Level 3

Level

2

Level

1

32

Antisense biases towards OFF

repressing the repressor (Level

2) should INCREASE GFP

production

0

50

100

150

200

250

RF

U

33

3 LEVEL CASCADE- SUCCESS

DATA

An 2

A 2 An 1 An 1

A 1GFP

Ribozyme

1.013

2 Hour Time Point

Increasing level 3 blocks repression by level 2

and INCREASES GFP Expression

3

2

1

34

Phase III – Single Input Module

Concentration Dependent Expression

Anti-1

Att-1 Anti-2

Att-2

Att-2 Att-2

RFP

GFP

Double Att-2 sequence should

require less Anti-2 for repression.

As Anti-1 increases, we predict that

RFP increase should precede GFP

Increase.

35

Computational PredictionModeling

time/min

10 20 30 40 50 60 70 80 90 100 110

0

100

200

300

400

500

protein sfGFP*

0nM Anti

2nM Anti

8nM Anti

0 20 40 60 80 100 1200

100

200

300

400

500

600protein RFP*

0nM Anti

2nM Anti

8nM Anti

An -1

A -1 An -2

A -2

A -2 A -2

RFP

GFP

RFP levels higher than GFP levels

Rate of RFP increase also higher

DNA DNA:RNAP:RNA

att

DNA:RNAP:RNA att-att

RNA att-att-GFP

+ DNA + RNAP

NTP

RNA PolyNTP

NTP

DNA:RNAP:RNA att:RNA

anti

DNA:RNAP:RNA att-att:RNA

anti

DNA:RNAP:RNA att-att:RNA

anti:RNA anti

DNA + RNAP + RNA att:RNA anti

DNA + RNAP + RNA att-att:RNA anti

DNA + RNAP + RNA att-att:RNA anti

RNA:RNase null

Translation

null

Model partial innards

37

Computational Prediction

An -1

A -1 An -2

A -2

A -2 A -2

RFP

GFP

38

The whole shebangDATA

An -1

A -1 An -2

A -2

A -2 A -2

RFP

GFP

VARY: 18,14 or 10nM

HOLD constant

both present

0

400

800

1200

1600

2000

419-004-18 419-004-14 419-004/10

RF

U

RFP - whole cascade

0

2000

4000

6000

8000

10000

12000

419-004-18 419-004-14 419-004/10

RF

U

GFP - whole cascade

OTHER THINGS I LEARNED

• Project management

• Different opinions on how to be a principle investigator

• Be a good story teller.

• How to choose a problem to solve.

• Aware of the things not discussed

• Very little talk about synthetic membranes/compartments.

• The evolution problem.

TRELLO: ONLINE PROJECT

MANAGEMENT SUITE

ALLOWS CHECKLISTS, SHARING OF

FILES, ASSIGN PEOPLE TO

TASKS, DEADLINES

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