ecolitaster: cellular biosensor valencia igem 2006
TRANSCRIPT
Outline
Introduction Parts Design Systems Design Experimental work Conclusions
“To have success in science, you need some luck. Without it, I would never have become interested in genetics”. J.D. Watson.
Introduction
Objectives: Design a genetic system consisting on few genes that is
able to give a graded response according to a concentration of an input.
Modular project. Different devices. Use a biological mechanism to connect the membrane
receptor with the genetic network, obtaining a cellular biosensor.
Use new synthetic parts.
Project Design
This project is formed by two devices: a sensor and an actuator.
We use OmpR-P as input in order to assemble them.
We use a vanillin receptor (design in silico) as a sensor.
Our genetic circuit (actuator) is based on Weiss’ group work (Basu, Nature 2005) in order to obtain a graded response versus the concentration of a given input.
Incoherent circuit. Semi-digital interpretation.
RFP & GFPVanillin
Actuator Behavior
CRP
cI
RFPOmpR
GFP
tetR
CRP
cI
RFPOmpR
GFP
tetR
CRP
cI
RFPOmpR
GFP
tetR
[Vanillin]
P
P
P
V
V
V
Vanillin Receptor: mechanism
OPEN CLOSED
SIGNAL
Specificitydesign
Independentof stimulus
90R
164E
105N
89K
15D16N
214D
235E
103N
pdb 2DRI271 res
V
PPBP GN
E. coli
Parts Design
Promoters are critical elements designing those networks.
We focus our interest in binary promoters, i.e., promoters regulated by two transcription factors.
Integrating two signals. Reduce the number of genes of the circuit. Small size device. Different implementations exhibiting logic behaviors, but
not necessarily. Computational protein design.
Vanillin Receptor: DESIGNER methodology
First pre-compute all possible pair interactions for later use
iiij
ji
ij
ij
ij
ijASA
r
r
b
r
aE
.612
foldedfoldedfolded
foldΔG
Folded: Fixed backbone + rotamerlibrary. Pairwise interactions, G ≈ E
unfoldedunfoldedunfolded
EG (AA)U
CHARMM22
Combinatorial problemAt each position we consider all rotamers R for each aminoacid a a1
a2a3R1
R2
R..
R3
82 4
53
9
Stability
Bin
din
g
Stabilityregion
Bindingregion
Pareto setnon-dominated
solutions
Stability
Bin
din
g
Stabilityregion
Bindingregion
Pareto setnon-dominated
solutions
vdw elec solbat
Systems Design
System and expected behavior. Model and simulations. Sensitivity analysis. Robustness analysis. Our biological system.
System and Expected Behavior
CRP
cI
RFPOmpR
GFP
tetR
CRP
cI
RFPOmpR
GFP
tetR
CRP
cI
RFPOmpR
GFP
tetR
[Vanillin]
P
P
P
V
V
V
Model and Simulations
We use an effective model, modeling only protein concentrations:
We consider generic parts to make these simulations. Thus, we take common values for the parameters from the literature. However we expect a similar behavior:
γ+β[Y]K[U]+
α=[Y]dtd
n -/1
1
Sensitivity Analysis
The well working of the circuit depends on the promoters upstream of the two branches: pOmpR and pOmpRm.
Robustness Analysis
We study the robustness of the gene circuit when there are oscillations in the sensing device. To perform that, we introduce a white noise in the input (OmpR-P).
OmpR-P OmpR-P OmpR-P
RFP
GFP
RFP
GFP
RFP
GFP
time time time
Experimental Work
Parts construction. Where are the parts?
Repositories. E. coli genome. Built from scratch.
Making our BioBricks. pAND. Vanillin receptor. Fusion protein.
FACS results. Our Registry.
Making our BioBricks (I)
pAND:
5’
5’
3’
3’
F0 F32 F71
R0R16R51R91
pAND
CRP BS cI BS
-93,5 -42XbaI
[Joung, Science 1994]
Making our BioBricks (I)
pAND:
5’
3’ 5’
3’
DNA ligase
5’
5’
3’
3’
F0 F32 F71
R0R16R51R91
pAND
CRP BS cI BS
-93,5 -42XbaI
[Joung, Science 1994]
Making our BioBricks (I)
pAND:
5’
3’ 5’
3’
DNA ligase
5’
5’
3’
3’
F0 F32 F71
R0R16R51R91
DNA polimerase & R91 + F71PCR
5’
3’ 5’
3’
5’
3’ 5’
3’
5’
3’ 5’
3’
pAND
CRP BS cI BS
-93,5 -42XbaI
[Joung, Science 1994]
Making our BioBricks (II)
Vanillin receptor:aa sequence:
KDTIALVVETLNKPDNVSLKDGAQKEADKLGYNLVVLDSQNNPAKELANVQDLTVRGTKILLIVPTDSDAVGNAVKMANQANIPVITLKRQATKGEVVSHIAADNVLGGKIAGDYIAKKAGEGAKVIELQGKAGTSAARELGEGFQQAVAAHKFNVLASQPADEDRIKGLNVMQNLLTAHPDVQAVFAQQDEMALGALRALQTAGKSDVMVVGDVGTPDGEKAVNDGKLAATIAELPDQIGAKGVETADKVLKGEKVQAKYPVDLKLVVKQ
pBSKValencia
$$ or €€
Computational design:Combinatory optimization
DESIGNER
Making our BioBricks (III)
NdeItrg
NdeItar envZ
Fusion protein Trz. [Baumgartner, J. Bact. 1993]. chemoreceptor Trg: periplasmic and transmembrane
domains. osmosensor EnvZ: cytoplasmic kinase/phosphatase
domain.
Making our BioBricks (III)
BioBrick PCR
Genomic PCR
NdeItrg
NdeItar envZ
Fusion protein Trz. [Baumgartner, J. Bact. 1993]. chemoreceptor Trg: periplasmic and transmembrane
domains. osmosensor EnvZ: cytoplasmic kinase/phosphatase
domain.
Making our BioBricks (III)
NdeI digestion
NdeI digestion &
dephosphorilation
BioBrick PCR
Genomic PCR
NdeItrg
NdeItar envZ
Fusion protein Trz. [Baumgartner, J. Bact. 1993]. chemoreceptor Trg: periplasmic and transmembrane
domains. osmosensor EnvZ: cytoplasmic kinase/phosphatase
domain.
Making our BioBricks (III)
NdeI digestion
NdeI digestion &
dephosphorilation
mix + ligate
BioBrick PCR
Genomic PCR
NdeItrg envZ
NdeItrg
NdeItar envZ
Fusion protein Trz. [Baumgartner, J. Bact. 1993]. chemoreceptor Trg: periplasmic and transmembrane
domains. osmosensor EnvZ: cytoplasmic kinase/phosphatase
domain.
Making our BioBricks (III)
NdeI digestion
NdeI digestion &
dephosphorilation
mix + ligate
BioBrick PCR
Genomic PCR
NdeItrg envZ
NdeItrg
NdeItar envZ
Fusion protein Trz. [Baumgartner, J. Bact. 1993]. chemoreceptor Trg: periplasmic and transmembrane
domains. osmosensor EnvZ: cytoplasmic kinase/phosphatase
domain.
Making our BioBricks (IV)
From wild type to BioBrick, a powerful screening method:
S PpTetR-RFP E X
Trg-envZ S P E X
Making our BioBricks (IV)
From wild type to BioBrick, a powerful screening method:
S PpTetR-RFP E X
Trg-envZ S P E X
EcoRI + PstI digestion & dephosphorilation
EcoRI + PstI digestion
Making our BioBricks (IV)
From wild type to BioBrick, a powerful screening method:
S PpTetR-RFP E X
Trg-envZ S P E X
EcoRI + PstI digestion & dephosphorilation
EcoRI + PstI digestion
mix & ligate &
transformation
Making our BioBricks (IV)
From wild type to BioBrick, a powerful screening method:
S PpTetR-RFP E X
Trg-envZ S P E X
EcoRI + PstI digestion & dephosphorilation
EcoRI + PstI digestion
mix & ligate &
transformation
pTetR-RFP
trg-envZ
FACS results (I)
Promoter pOmpR with GFP as reporter:
Negative control:XL1-Blue
Positive control:Green fluorophore
Set: pOmpR-RBS-GFP-T
FACS results (II)
Characterization of pOmpR and pOmpRm.
Negative control:XL1-Blue
Positive control:Green fluorophore
Set: pOmpR-RBS-GFP-T
Set: pOmpR-RBS-GFP-T
Our Registry
Parts submited by Valencia:New Parts:
pOmpR + RBS-GFP-T
(J 58102)
pOmpRm + RBS-GFP-T (J 58103)
pAND(J 58100)
pAND + RBS-RFP-T (J 58101)
PBP vanillin sensor (J 58105)
Fusion protein Trg-EnvZ(J 58104)
Conclusions
We have designed a genetic system consisting on 7 genes, expected to give a graded response according to vanillin concentration.
We use the phosphorilation mechanism to connect the membrane receptor with the genetic network, obtaining a cellular biosensor.
Our use of a two-regulator promoter allows to integrate signals and reduce the number of genes required for a device.
Computational design of a PBP-vanillin receptor.
Acknowledgements
EU FP6 NEST SYNBIOCOMM project (financial support).
Escuela Técnica Superior de Ingenieros Industriales (Universidad Politécnica de Valencia).
Instituto de Ciencia Molecular (Universitat de València).
E. O’Connor (FACS services). A. Moya and A. Latorre (Cavanilles).