Developing designable genetic control systems for applied ......Developing designable genetic...
Transcript of Developing designable genetic control systems for applied ......Developing designable genetic...
Developing designable genetic control systems for applied synthetic biology
James M. Carothers
December 9, 2013
BIO Pacific Rim Summit on Industrial Biotechnology & Bioenergy
San Diego, CA
Chemical Engineering and BioengineeringCenter for Synthetic Biology
University of Washington, Seattle, WA
Control functions balance the supplies and demands for cellular resources
After Carothers, Goler, Keasling. Curr. Opin. Biotechnol. 2009.
DNA
Enzymes
Reaction1
Reaction2
ReactionN
Product
Enzyme1
E1
E1
E2
EN
EN
EN
EN
s1
Enzyme2
EnzymeN
s2
sN P
Control functions balance the supplies and demands for cellular resources
After Carothers, Goler, Keasling. Curr. Opin. Biotechnol. 2009.
DNA
Enzymes
Reaction1
Reaction2
ReactionN
Product
Enzyme1
E1
E1
E2
EN
EN
EN
EN
s1
Enzyme2
EnzymeN
s2
sN P
Targeted device outputs
Genetic devicedesign goals
Engineercomponents
mechanisticmodels
Systems-levelmodels
0...1
Biochemicalmodels for identifying
component specifications
Functionaldesign
Physical implementation
Kinetic foldingmodels
Biophysical models for designing
transcripts
Assembly & verification
mechanisticmodels
Biochemical models for predicting
device outputs
StaticRNA devices
DynamicRNA devices
Devices generating
targeted outputs
Systems-levelfunctions
Refine Systems-level models
Approach: Develop RNA-based systems to solve regulatory control problems and enable high levels of chemical production from engineered cells
Goal: Designable RNA-based genetic control systems that dramatically increase the sizes and complexities of systems that can be engineered
Computational modeling
In vitro selection/evolution
Genetic engineering
E. coli platform for p-aminostyrene (p-AS) production 14 enzymatic transformations
Goal: Engineer RNA-based genetic systems to solve pathway control problems and enable production of p-AS for advanced polymer composites
DHA
DHQ
Shikimate
Glucose
DAHP
PEPE4P
Chorismate
p-AF
PpsATktA
AroG*
aroB
aroD
aroE
EPSC
S3P
p-ADC
p-AP
p-APA
aroL
aroA
aroC
papA
papB
papC
deaminase
p-ACA
deaminase
decarboxylase
Opiate analgesics
p-aminostyrene(p-AS)
NH2
COO-
NH3
+
NH2
COO-
OHOH
HO
Shikimate p-AF
Dipeptidylpeptidase IV
inhibitors
Pristinamycinantibiotics
After Carothers. Syst. Synth. Biol. 2013.Sariaslani. Ann. Rev. Microbiol. 2007.
E. coli platform for p-aminostyrene productionLast 6 of 14 steps shown
Goal: Engineer RNA-based genetic systems to solve pathway control problems and enable production of p-AS for advanced polymer composites
Functional RNAs regulate gene expression and control cellular activities
S. mansoni hammerhead ribozyme B. subtilis purine riboswitch aptamer domain
Phosphodiester bond cleavage
5'
3' Purine ligand
Dynamic, ligand-responsive
5' 3'
5'
3'
Scissilephosphate
Functional RNAs can be evolved in the laboratory with in vitro selection
Aptamers GTP aptamer
Ribozymeshammerhead rbz
A G U
C
G
C
U
AG
C
U G
AA A
C
G
C
C
UU
AG
A
GAA
GA
U
C
G
G
C
GC
UU A
UA
GG
C G
G C
G C
G UG
A A
AAptamer
Ribozyme
Bridge(evolved)
5'3'
AptazymesAptamer-controlled ribozyme
Scissilephosphate
kd range: mM to nM k
cat (+/-) up to 1000
5' 3'
5'
3'
Scissilephosphate
kcat
: up to ~10 min-1
GTP ligand
3'
5'
Carothers & Szostak. The Aptamer Handbook. 2006.Carothers, Goler et al. & Keasling. Nucl. Acids Res.2010.
Formalized framework for designing static and dynamic genetic devices with quantitatively-predictable outputs
Targeted device outputs
Genetic devicedesign goals
Engineercomponents
mechanisticmodels
Systems-levelmodels
0...1
Biochemicalmodels for identifying
component specifications
Functionaldesign
Physical implementation
Kinetic foldingmodels
Biophysical models for designing
transcripts
Assembly & verification
mechanisticmodels
Biochemical models for predicting
device outputs
StaticRNA devices
DynamicRNA devices
Devices generating
targeted outputs
Systems-levelfunctions
Refine Systems-level models
Ribozyme (rREDs)
Aptazyme (rREDs)
Predicted and observed functions for 25 static rREDs and dynamic aREDs
r = 0.94, P = 2 x 10-7
For devices w/ kfold
> 0
rel. rms-d = 0.19
For devices w/ kfold
< 0
rel. rms-d = 0.60
Excellent correspondence between predictions and observations validates underlying models and overall approach
Carothers, Goler, Juminaga & Keasling. Science, 2011.
Design of FadR-based dynamic sensor regulator system to improve fatty acid ethyl ester (FAEE) biodiesel production in E. coli
Approach: Interrogate system designs by performing design variable global sensitivity analysis with biochemical parameter uncertainty
Renewable diesel
Original system: Steen et al. & Keasling. Nature. 2010.
Design of FadR-based dynamic sensor regulator system to improve fatty acid ethyl ester (FAEE) biodiesel production in E. coli
Zhang, Carothers & Keasling, Nature Biotech. 2012.
Broad range of dynamic regulatory topologies gave improved production compared to pathway gene expression from constitutive promoters
Modeled parameter ranges where dynamic sensor regulator system increases FAEE production
Experimentally measured 3 fold greater yields with FadR-dynamic sensor regulator system (28% theoretical maximum from glucose)
E. coli platform for p-aminostyrene productionLast 6 of 14 steps shown
Goal: Engineer RNA-based genetic systems to solve pathway control problems and enable production of p-AS for advanced polymer composites
Approach: Generate 728 unique pathway models to map space of dynamic control topology inputs to production outputs
E. coli platform for p-aminostyrene productionPotential genetic control mechanisms
PapC
PapB
PapC
chorismatep-amino-
phenylalanine(p-AF)
Deam.
txn
trans.
LAAO
p-amino-cinnamic acid
Transport out of cell
p-amino-styrene(p-AS)
CO2
p-AF is excreted from cell
p-ACA is a toxicintermediate
LAAO Overexpressiondepletes
metabolites
Pump Overexpression
reducesgrowthtrans. trans.
txn txn
mRNA mRNA mRNA
Finalproduct
From shikimate/chorismate
modules
L-amino Acid Oxidase CDSPap Operon CDS
pump
pump
pump
deg.deg.
deg.
trans.trans.
ControlProblem
ControlNode
Enzyme
Efflux Pump CDS
blue text
NH2
NH3
NH2
COO-
+
COO-COO-
NH2
NH2
OH
COO-
COO-O
9-fold production increases (20% theoretical maximum) readily achievable with dynamic multi-device, RNA-based genetic control systems
E. coli platform for p-aminostyrene productionPredicted production outputs for 728 core regulatory topologies
Co
re T
op
olo
gy
Nu
mb
er
1
26
aRED aRED aRED only + + sRNA TF
0.1
1
0
1
000
Co
re T
op
olo
gy
Nu
mb
er
1
3
5
7
9
1
1
13
1
5
17
1
9
21
23
2
5
1e-10 1e-5 1 1e5p-ACA Output Ratio (αrel)
P L E
Rank 1: Core Topology 4
αrel: 3.23 (2.68)βrel:1.34 (0.20)titer:12.1(15.7)
P L E
Rank 3: Core Topology 20
αrel: 2.37 (1.55)βrel:1.2 (0.13)titer: 8.39 (11.2)
P L E
Rank 5: Core Topology 22
αrel: 1.04 (0.19)βrel:1.04 (0.03)titer: 3.84 (5.34)
P L E
Rank 15: Core Topology 23
αrel: 0.037 (0.054)βrel:1.44 (0.24)titer: 0.098 (0.14)
P L E
Rank 25: Core Topology 6
αrel: 9.1e-7 (1.4e-6)βrel:1.3 (0.15)titer: 2.6e-6 (5.4e-6)
P L E
Rank 2: Core Topology 5
αrel: 2.83 (1.62)βrel:1.27 (0.15)titer: 11.9 (15.4)
P L E
Rank 4: Core Topology 13
αrel: 2.21 (1.06)βrel:1.2 (0.10)titer: 9.05 (12.1)
P L E
Rank 10: Core Topology 2
αrel: 0.18 (0.20)βrel:1.2 (0.13)titer: 0.61 (0.86)
P L E
Rank 20: Core Topology 26
αrel: 3.2e-6 (4.8e-6)βrel:1.27 (0.145)titer: 1.6e-5 (2.35e-5)
P L E
Rank 26: Core Topology 21
αrel: 3.6e-7 (5.3e-7)βrel:1.05 (0.03)titer: 1.4e-6 (2.1e-6)
p-ACA Output Ratio (αrel
)
= dynamic / static production
Ou
tpu
t P
rod
uct
ion
Rat
io
1 1e51e-51e-10
9-fold production increases (20% theoretical maximum) readily achievable with dynamic multi-device, RNA-based genetic control systems
E. coli platform for p-aminostyrene productionPredicted production outputs for 728 core regulatory topologies
Co
re T
op
olo
gy
Nu
mb
er
1
26
aRED aRED aRED only + + sRNA TF
0.1
1
0
1
000
Co
re T
op
olo
gy
Nu
mb
er
1
3
5
7
9
1
1
13
1
5
17
1
9
21
23
2
5
1e-10 1e-5 1 1e5p-ACA Output Ratio (αrel)
P L E
Rank 1: Core Topology 4
αrel: 3.23 (2.68)βrel:1.34 (0.20)titer:12.1(15.7)
P L E
Rank 3: Core Topology 20
αrel: 2.37 (1.55)βrel:1.2 (0.13)titer: 8.39 (11.2)
P L E
Rank 5: Core Topology 22
αrel: 1.04 (0.19)βrel:1.04 (0.03)titer: 3.84 (5.34)
P L E
Rank 15: Core Topology 23
αrel: 0.037 (0.054)βrel:1.44 (0.24)titer: 0.098 (0.14)
P L E
Rank 25: Core Topology 6
αrel: 9.1e-7 (1.4e-6)βrel:1.3 (0.15)titer: 2.6e-6 (5.4e-6)
P L E
Rank 2: Core Topology 5
αrel: 2.83 (1.62)βrel:1.27 (0.15)titer: 11.9 (15.4)
P L E
Rank 4: Core Topology 13
αrel: 2.21 (1.06)βrel:1.2 (0.10)titer: 9.05 (12.1)
P L E
Rank 10: Core Topology 2
αrel: 0.18 (0.20)βrel:1.2 (0.13)titer: 0.61 (0.86)
P L E
Rank 20: Core Topology 26
αrel: 3.2e-6 (4.8e-6)βrel:1.27 (0.145)titer: 1.6e-5 (2.35e-5)
P L E
Rank 26: Core Topology 21
αrel: 3.6e-7 (5.3e-7)βrel:1.05 (0.03)titer: 1.4e-6 (2.1e-6)
p-ACA Output Ratio (αrel
)
= dynamic / static production
Ou
tpu
t P
rod
uct
ion
Rat
io
1 1e51e-51e-10
9-fold production increases (20% theoretical maximum) readily achievable with dynamic multi-device, RNA-based genetic control systems
E. coli platform for p-aminostyrene productionPredicted production outputs for 728 core regulatory topologies
Co
re T
op
olo
gy
Nu
mb
er
1
26
aRED aRED aRED only + + sRNA TF
0.1
1
0
1
000
Co
re T
op
olo
gy
Nu
mb
er
1
3
5
7
9
1
1
13
1
5
17
1
9
21
23
2
5
1e-10 1e-5 1 1e5p-ACA Output Ratio (αrel)
P L E
Rank 1: Core Topology 4
αrel: 3.23 (2.68)βrel:1.34 (0.20)titer:12.1(15.7)
P L E
Rank 3: Core Topology 20
αrel: 2.37 (1.55)βrel:1.2 (0.13)titer: 8.39 (11.2)
P L E
Rank 5: Core Topology 22
αrel: 1.04 (0.19)βrel:1.04 (0.03)titer: 3.84 (5.34)
P L E
Rank 15: Core Topology 23
αrel: 0.037 (0.054)βrel:1.44 (0.24)titer: 0.098 (0.14)
P L E
Rank 25: Core Topology 6
αrel: 9.1e-7 (1.4e-6)βrel:1.3 (0.15)titer: 2.6e-6 (5.4e-6)
P L E
Rank 2: Core Topology 5
αrel: 2.83 (1.62)βrel:1.27 (0.15)titer: 11.9 (15.4)
P L E
Rank 4: Core Topology 13
αrel: 2.21 (1.06)βrel:1.2 (0.10)titer: 9.05 (12.1)
P L E
Rank 10: Core Topology 2
αrel: 0.18 (0.20)βrel:1.2 (0.13)titer: 0.61 (0.86)
P L E
Rank 20: Core Topology 26
αrel: 3.2e-6 (4.8e-6)βrel:1.27 (0.145)titer: 1.6e-5 (2.35e-5)
P L E
Rank 26: Core Topology 21
αrel: 3.6e-7 (5.3e-7)βrel:1.05 (0.03)titer: 1.4e-6 (2.1e-6)
p-ACA Output Ratio (αrel
)
= dynamic / static production
Ou
tpu
t P
rod
uct
ion
Rat
io
1 1e51e-51e-10
1. RNA devices with quantitatively-programmable genetic outputs can be assembled from component parts generated and characterized in vitro, in vivo, and in silico.
2. Simulation analysis can inform the design of Dynamic Sensor-Regulator Systems that increase production of FAEEs.
3. Multi-device, RNA-based genetic systems can be designed to solve control problems and potentially enable high levels of production.
Summary:
Acknowledgements
University of California Berkeley and DOE Joint BioEnergy Institute
Email: [email protected] http://carothersresearch.com
Jay Keasling Jonathan GolerAlex Juminaga
Tim ThimmaiahFuzhong Zhang
Guillaume CambrayRichard Schwartz
Jason Stevens – BioE PhD Student Rodrigo Correa – PostdocDavid Sparkman-Yager – Research Assistant
Willy Voje – ChemE PhD studentMichael Newton - ChemE PhD studentMisha Yatsevitch – ChemE Undergrad
University of Washington
CENTER for SYNTHETIC BIOLOGY