Accurate Predictions of Genetic Circuit
Behavior from Part Characterization
and Modular Composition
Jacob Beal, Noah Davidsohn, Aaron Adler, Fusun Yaman,
Yinqing Li, Zhen Xie, Ron Weiss
5th IWBDA
July, 2013
EQuIP: Realizing the Dream
TAL14
8
TAL14-TAL21
10
7
Active pop. mean MEFL vs time
OFP MEFL
10
6
10
5
10
4
10
5
10
6
10
IFP MEFL
7
10
8
10
Outline
• Calibrating Flow Cytometry
• Building EQuIP Models
• Prediction & Validation
TASBE Method
[Beal et al., Technical Report: MIT-CSAIL-TR-2012-008, 2012]
R1
Arbitrary
[R1]Red
[Output]
Blue
Arbitrary
[R2]Blue
Arbitrary
First, some metrology…
R2
Arbitrary
[R2]Red
Unit mismatch!
Output
How Flow Cytometry Works
Challenges:
• Autofluorescence
• Variation in measurements
• Spectral overlap
• Time Contamination
•
•
•
Lots of data points!
Different protein fluorescence
Individual cells behave (very)
differently
Fluorescent Beads  Absolute Units
SpheroTech RCP-30-5A
Run beads every time: flow cytometers drift up to 20 percent!
Also can detect instrument problems, mistakes in settings
Compensating for Autofluorescence
Negative control used for this
Compensating for Spectral Overlap
Strong positive control used for each color
Note: only linear when autofluorescence subtracted
Translating Fluorescence to MEFL
• Only FITC channel (e.g. GFP) goes directly
• Others obtained from triple/dual constitutive controls
• Must have exact same constitutive promoter!
• Must have a FITC control protein!
Outline
• Calibrating Flow Cytometry
• Building EQuIP Models
• Prediction & Validation
EQuIP = Empirical Quantitative Incremental Prediction
TASBE Characterization Method
Dox
R1
Output
Dox
pCAG
pCAG
mkate
pCAG
rtTA3
T2A
VP16Gal4
pTRE
EBFP2
pTRE
R1
pUAS-Rep1
Transient cotransfection of 5 plasmids
Calibrated flow cytometry
Analysis by copy-count subpopulations
EYFP
Multi-plasmid cotransfection!?!
• Avoids all problems with adjacency, plasmid
size, sequence validations
• Variation appears to be independent
Result: Input/Output Relations
Transfer curve for TAL 14
Transfer curve for TAL 21
R1 = TAL14
R1 = TAL21
13
Expression Dynamics
Fraction Active
Mean Expression
Results  division rate, mean expression time, production scaling factor
EQuIP model
Dilution
& decay
I(t)
P(I,t)
+
Delay
O(t)
Model = first-order discrete-time approximation
Outline
• Calibrating Flow Cytometry
• Building EQuIP Models
• Prediction & Validation
EQuIP Prediction
+
Dox
R1
R2
Output
Dox
pCAG
pCAG
rtTA3
T2A
VP16Gal4
pTRE
EBFP2
pCAG
mkate
pTRE
R1 Device
P(I,t)
R2
pUAS-Rep1
pUAS-Rep2
EYFP
R2 Device
Dilution
& decay
I(t)
R1
+
Dilution
& Decay
Delay
P(I,t)
+
Delay
O(t)
Incremental Discrete Simulation
3
4
7
6
3
8
+
4
3
3
4
4
5
6 7
8
TAL21 (log10 MEFL)
7
6
3
3
3
4
5
6
7
8
Plasmids (log 10 MEFL)
4
5
6
7
8
TAL21 (log10 MEFL)
8
5
+
4
3
4
5
6 7
8
TAL14 (log10 MEFL)
7
6
hour 2
5
4
3
3
4
5
6
7
8
TAL21 (log10 MEFL)
D OFP (log10 MEFL)
8
7
6
8
5
+
4
3
4
5
6 7
8
TAL21 (log10 MEFL)
TAL14 (log10 MEFL)
D OFP (log10 MEFL)
TAL21 (log10 MEFL)
4
6
-
3
5
3
…
6
4
…
production
hour 1
5
7
4
5
6
7
8
TAL21 (log10 MEFL)
8
7
4
5
6 7
8
TAL14 (log10 MEFL)
6
-
4
8
3
7
8
3
…
loss
+
4
3
5
3
3
4
5
6
7
8
Plasmids (log 10 MEFL)
8
5
4
5
6
7
8
TAL21 (log10 MEFL)
7
5
6
4
-
6
7
3
5
8
5
8
7
6
6
4
3
4
5
6
7
8
TAL21 (log10 MEFL)
8
5
+
4
3
5
3
7
3
4
5
6 7
8
TAL14 (log10 MEFL)
OFP (log10 MEFL)
8
6
3
3
4
5
6
7
8
Plasmids (log 10 MEFL)
-
3
4
5
6 7
8
TAL21 (log10 MEFL)
7
OFP
stat
e
OFP (log10 MEFL)
+
4
OFP
production
D OFP (log10 MEFL)
8
5
3
5
loss
TAL21 (log10 MEFL)
6
[OFP]
OFP
D OFP (log10 MEFL)
6
7
TAL14 (log10 MEFL)
7
3
production
8
D OFP (log10 MEFL)
production
TAL21 (log10 MEFL)
8
TAL14
state
TAL14 (log10 MEFL)
time
TAL14
production
D OFP (log10 MEFL)
TAL21
state
[TAL14
]
TAL14
OFP (log10 MEFL)
[TAL21
]
7
6
hour 46
5
4
3
3
4
5
6
7
8
TAL21 (log10 MEFL)
High Quality Cascade Predictions
1.6x mean error on 1000x range!
8
10
TAL14  TAL21
8
10
7
TAL21  TAL14
7
OFP MEFL
10
OFP MEFL
10
6
6
10
10
5
10
4
10
5
5
10
6
10
IFP MEFL
7
10
8
10
Circles = EQuIP predictions
Crosses = Experimental Data
10
4
10
5
10
6
10
IFP MEFL
7
10
8
10
Contributions
• TASBE method calibrates flow cytometry data
• Cotransfected test circuits give good models
• EQuIP accurately predicts cascade behavior
from models of individual repressors
Now for bigger, better circuits on more platforms…
Acknowledgements:
Aaron Adler
Fusun Yaman
Ron Weiss
Noah Davidsohn
Yinqing Li
Zhen Xie
21
Characterization Tools Online!
https://synbiotools.bbn.com/