1. Synthetic Biology:
The next generation of biotechnology
Christina D. Smolke
Department of Bioengineering
Stanford University
July 9, 2012
LASER Talk, University of San Francisco

2. Engineering systems
Circuitry
Sensors (Inputs) Circuitry
signal processing remote control Signal Processing
Temperature Chemicals memory
automated response Automated Response
Sensors (Inputs) Light
dynamic controlTouch communication Dynamic Control Memor
chemicals Remote control Communication
biomolecules
temperature
light
Actuators (Outputs)
Actuators (Outputs) Motility Reporting
reporting phenotype Delivery Self-Organization
delivery self-organization Modify Environment
motility synthesis

3. Synthetic biology
ensors (Inputs) Circuitry
Circuitry
Temperature Chemicals signalProcessing
Signal processing remote control
Light Automated Response
automated response memory
Touch
Sensors (Inputs)
Communication dynamicControl
Dynamic control Memory
communication
Remote control
chemicals
biomolecules
temperature
light
Actuators (Outputs)
Actuators (Outputs)
Motility Reporting
reporting phenotype
Delivery self-organization
delivery Self-Organization
motility synthesis
Modify Environment

4. Tools driving genetic engineering
Recombinant DNA Polymerase Chain Reaction DNA Sequencing
First
Gen.
Biotech
=
Basic “Cut” & “Paste” Amplify & Make Simple Changes Read Out the Genetic Code
Insulin Production Erythropoietin Production
?
10-1000’s genes
complex chemicals
& materials
organisms as
products
1973 1985
….
bacterial cell culture, 1 gene mammalian cell culture, 1 gene

5. Ongoing tools revolution
Recombinant DNA Polymerase Chain Reaction DNA Sequencing
First
Gen.
Biotech
=
Basic “Cut” & “Paste” Amplify & Make Simple Changes Read Out the Genetic Code
Next
Gen.
Biotech
Adds = ...
New
Tools
c/o D. Endy (Stanford University)

6. Transformative advances in fabrication platforms
DNA Construction = #1 Tech. of 21st Ctry.
From absract
information to
physical, living
DNA designs.
Organic chemistry Biochemistry
nstruction = #1 Tech. of 21st Ctry.
TAATACGACTCACTATAGGGAGA
…TAATGCAGCTTATTACA…(<200 nt)
enzymes
2004: 10,000 bp
2010: 1,000,000 bp
2016: 100 million?
26 (~1000 –
ATACGACTCACTATAGGGAGA 10000 bp)
A T G C

7. Transformative advances in fabrication platforms
Carr PA, Church GM. 2009. Nat Biotech. 27: 1151-1162
Cellular (in vivo) assembly
1 Mb+
engineered
natural
genome
transplantation Gibson DG, et al. 2010. Science. 329: 52-6

8. Now that we can
write in DNA, what
do we say?
Challenge: Design Gap

9. Buffe
Inverter1+Invert
Buff
Inver
2xInvert
Invert
2xInver
Buffer1+Buff
2xBuf
2xBuf
Inver
Buf
2xInver
2xBuf
Buf
2xBuf
Buf
2xBuff
D
Buff
2xBuff
output protein (high when AA)
Biomanufacturing Platforms Next-Generation Therapies
C AND gate D16 14.0
Device response in unit expression
14
A input A input B
AB output
Apps
B 12
[SI 1.2]
A B output 10
theo tc GFP
8
0 0 0
0 1 0 6
1 0 0 4 3.1
AAAAA
AAA
1 1 1 2.0
2
0
0
GFP low low low high
output protein (high when AB) theo + +
tc + +
Scalable Biological Computation
E NOR gate F 10
9 8.1
Device response in unit expression
input A input B
Circuits
A 8
A+B output
B 7
[SI 1.3]
A B output 6
theo tc GFP 5
0 0 1 4
0 1 0
3 2.0
1 0 0
AAAAA 2 1.1
1 1 0
1 0
0
GFP high low low low
output protein (high when A+B)
theo + +
tc + +
input module actuator module output module assembled RNA device
I/O Tools Spatial Engineering Figure 2 F Win and Smolke
protein sensor alternative exon inclusion gene of interest
Tools
Golgi
Vacuole
+ + GOI GOI
output
3’ss 5’ss
ER
The identity of the RNA sensor and
Mitochondrion
Nucleus
its location in the intronic space will
determine the effect of input binding
exon inclusion exon exclusion input A
on the device output.
(low device output) (high device output)
The Smolke Laboratory

10. Molecular computers enable programming of function
input
sensor1 / transmitter1 / actuator
output
input A
sensor1 / transmitter2 / actuator
output
input A
sensor2 / transmitter1 / actuator
output
input B
output
input output
Win MN, Smolke CD. 2007. PNAS. 104: 14283-8

11. Molecular computers enable programming of function
input
sensor1 / transmitter1 / actuator
output
input A
sensor1 / transmitter2 / actuator
output
input A
sensor2 / transmitter1 / actuator
output
input B
output
n sensors / n transmitters / actuator
A B output
0 0 1
0 1 1
1 0 1
1 1 0
Win MN, Smolke CD. 2008. Science. 322: 456-60

12. Fluorescence low
GFP AAA Atropine
Cellular biosynthesis Muscarinic blocker
platforms
GFP AAA
Paclitaxel
Cancer
Ligand present
Ribozyme inactive GFP AAA
Fluorescence high
Digoxin
Arrhythmias
Ceftaroline
Antibiotic
Prostratin
HIV
1
2

13. H 3 CO H 3CO
NCH3 NCH 3
HO HO
O
OH
antimicrobial
Norcoclaurine Coclaurine N-Methylcoclaurine anticancer
Berbamunine
Tyrosine
BisBIAs
3’-Hydroxy-N-
methylcoclaurine
antiviral Norlaudanosoline 6-O-Methyl-
norlaudanosoline
Reticuline hair growth
Salutaridinol-7- Salutaridinol Salutaridine
O-acetate
Scoulerine Tetrahydro- Canadine Berberine
columbamine
Thebaine Neopinone Codeinone
antimicrobial
Berberine branch
analgesic antioxidant
antitussive Cheilanthifoline
Codeine
Cis-N- Dihydro- Sanguinarine
Stylopine Methylstylopine sanguinarine
Morphine branch Morphine Sanguinarine branch
Hawkins KM, Smolke CD. 2008. Nat Chem Biol. 4: 564

14. Building a microbial drug factory
Enzymes catalyze reactions:

15. Building a microbial drug factory
Bis-BIAs Sanguinarine / Morphinan
Berberine Alkaloids Alkaloids
O
N
O
OMe
OMe

16. Building a microbial drug factory
DNA
protein
metabolite
Bis-BIAs Sanguinarine / Morphinan
Berberine Alkaloids Alkaloids
O
N
O
OMe
OMe

17. Building a microbial drug factory
Bis-BIAs Sanguinarine / Morphinan
Berberine Alkaloids Alkaloids
O
N
O
OMe
OMe

18. Building a microbial drug factory
Bis-BIAs Sanguinarine / Morphinan
Berberine Alkaloids Alkaloids
O
N
O
OMe
OMe

19. Building a microbial drug factory
Bis-BIAs Sanguinarine / Morphinan
Berberine Alkaloids Alkaloids
O
N
O
OMe
OMe

20. Molecular tools for optimizing metabolite production
Challenge: Often need to screen through large libraries of pathway/enzyme
variants – invasive / analytical procedures are too time and resource intensive
Solution: Engineer scalable platforms for noninvasive sensors of metabolite
concentrations in single living cells
AAA AAA

21. I/O tools as noninvasive, real-time metabolite sensors
AAAA
GFP
Ligand absent
Ribozyme active GFP AAA
Fluorescence low
Ligand absent
Ribozyme active GFP AAAA AAA
GFP
Fluorescence low
AAA
Ligand absent GFP
Ribozyme active GFP AAA
Fluorescence low
GFP AAA
GFP
GFP AAA
AAAA
GFP AAA
GFP AAA
Ligand present
Ribozyme inactive GFP
GFP AAA
AAA
Fluorescence high GFP AAAA
Ligand present
Ribozyme inactive GFP AAA
Michener JK, Smolke CD. 2012. Metab Eng. In press Fluorescence high
Ligand present
Ribozyme inactive GFP AAA

22. Evolution… in the laboratory
mutagenesis
…
identify best variant
X

23. High-throughput enzyme library screening methods
plate-based assay
(library size: ~103)
FACS-based screen
(library size: ~106-107)
Molecular computer links enzyme
activity to fluorescence
Michener JK, Smolke CD. 2012. Metab Eng. In press

24. Li
2 0.1
a yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 (-) Control b 1.8 300
α-actin α-V5
KM, app
0 1.6 0.0 Selectivity
250
0% 20% 40% 60% 80% 100%1.4 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
Rapid Fluorescence Change of enzymes with improved activities
identification Relative Activity
KM, app (mM)
200
Selectivity
1.2
35
c 0.8
d1.0
vmax, app/KM, app 150
0.8 30
Library Frequency
Theophylline production
Fold Improvement
0.7
Relative
Enzyme model1.0x
1.0x
Expression
0.6
1.0x 1.2x 0.9x 0.7x 0.0x 0.6
0.4
25
100
0.5 50
c 0.2 20
0.4 0.0 0
yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8
0.3 15
0.2 56
d 10
0.1 54
0.0 5
Post-sort #3 52
0.0 0.2 Post-sort #1
T50 (oC)
0.4 0.6 Pre-sort #1 50 0
0.8 1.0 yCDM1 yCDM2 yCDM3 yCDM4 yCDM5 yCDM6 yCDM7 yCDM8
1.2 48
Relative Activity Plate Based FACS
46
a yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 (-) Control
44b 1.8 300
α-actin α-V5
KM, app
1.6 Selectivity
42 250
1.4
40
Wildtype yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8
KM, app (mM)
200
Selectivity
1.2
1.0
150
0.8
Relative 0.6 100
Catalytic 1.0x 1.2x and selectivity
1.0x
Expression activity 1.0x 0.9x 0.7x 0.0x
0.4
increases through evolutionary 50
c 0.2
trajectory 0.0
yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8
0
Michener JK, Smolke CD. 2012. Metab Eng. In press 56
d
54

25. Cellular therapies: engineering the immune system
transfer engineered
cells into patient
harvest lymphocytes
recover and engineer from patient
desired cell type
http://www.discoverymedicine.com/Leslie-E-Huye/files/2010/03/

26. A molecular computer controlling immune response
proliferation
apoptosis /
death
Chen YY, Jensen MC, Smolke CD. 2010. PNAS. 107: 8531-6

27. A molecular computer controlling immune response
1.2E+09
L2bulge9(3x) No Theo
L2bulge9(3x) 0 uM Theo
Flux (photons/sec)
1.0E+09
No drug L2bulge9(3x) 500 μM Theo
L2bulge9(3x) 500 uM Theo
8.0E+08
6.0E+08
4.0E+08
2.0E+08
0.0E+00
0 2 4 6 8 10 12 14 16
Days Post Injection
With drug
Chen YY, Jensen MC, Smolke CD. 2010. PNAS. 107: 8531-6

28. Apps
Standardization Abstraction Synthesis Device Design Languages & Grammar
AGGTACAGTTATCA
CCCATTGCATGGTA
TTCAAAGAAGTCGT
output
GGCCCAGATTCGAC
AAATCGTGTAGTAA
TGGTCCAGCTGATT input
Tools GGTTCAAATAACGG

29. Postdoctoral Researchers The Smolke Laboratory
Kate Thodey
Eric Hayden
Graduate Researchers
Ryan Bloom
Andy Chang
Leo d’Espaux
Stephanie Galanie
Katie Galloway
Drew Kennedy
Joe Liang
Melina Mathur
Josh Michener
Michael Siddiqui
Isis Trenchard
Jay Vowles Alumni Collaborators
Yen-Hsiang Wang Andrew Babiskin Michael Jensen (SCRI, FHCRC)
Kathy Wei Travis Bayer
Remus Wong Chase Beisel Funding Sources
Yvonne Chen Defense Advanced Research Projects
Stephanie Culler Agency
Kristy Hawkins Bill and Melinda Gates Foundation
Kevin Hoff National Institutes of Health (NIGMS, NCI)
Maung Nyan Win National Science Foundation (CBET, CCF)