computational synthetic biology - dtc · 2012. 8. 16. · computational synthetic biology yiannis...
TRANSCRIPT
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ComputationalComputational
Synthetic BiologySynthetic Biology
Yiannis N. Kaznessis
University of Minnesota
Department of Chemical Engineering and Materials Science
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gene
RNAp
operator
repressor
genepromoter
RNAp
operator
activator
mRNA ribosome
Protein product
mRNA
genepromoter
RNAp
promoter
!"#"$%"&'()*+
Inducer Binding
Gene expressionGene expression
• Transcription
• Translation
Gene RegulationGene Regulation
• Activation
• Repression
InductionInduction
Gene NetworksGene Networks
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!"#"$#"&'()*$"#, -#"")-#,!"#"$#"&'()*$"#, -#"")-#,! . -&/$!"#(0"$1)(2"3&+$&((45(6"+$(7
! 8",94:&();$
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H6:0
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H6:0
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H#, -#"")I+$-"'! K)(0 $:#$"#, -#"")-#, $-"' L $+;#&/"&-3
5 -( 4(, ;$-+$7 -)+& $:#?$7()"0(+&$:$':;$(7$:++"05 4"
3(0
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J-(J)-3*+
= /&&< GMM
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Light responsive system, dual regulation
Repressilator
Toggle Switch
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gene
RNAp
operator
repressor
genepromoter
RNAp
operator
activator
mRNA ribosome
Protein product
mRNA
genepromoter
RNAp
promoter
O(?"4-#, $!"#"$%"&'()*+O(?"4-#, $!"#"$%"&'()*+Protein Dimer Inducer BindingModeling all interactions at the
molecular level
• Protein Interactions
• Transcription
• Translation
• Regulation
Detailed modeling allows for
rational engineering
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B&(3/:+&-3$P-#"&-3+B&(3/:+&-3$P-#"&-3+Modeling cell functions
• Many rare distinct events
• Some participating species are sparse and diluted
• Intrinsic fluctuations important
Far from the thermodynamic limit: Stochastic chemical
kinetics (McQuarrie, 1949; Oppenheim, 1965; Fredrickson,
1963)
Stochastic multiple time scale algorithms (Gillespie DT ,
Journal of Computational Physics (1976))
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O94& -+3:4"O94& -+3:4" $O(?"4-#,$O(?"4-#,
K):0"'()*K):0"'()*! QG $$$$C-+3)"&"$M$B&(3/:+&-3
! R90$
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/&&< GMM+;#5 -(++F+(9)3"7(),"F#"&M! B(7&':)"$+9-&"$7()$, "#"):&-(#L+&()-#, L $)"&)-">:4$:#?$N9:#&-&:& ->"+-0 94:& -(#$(7$+;#&/"&-3$5 -( 4(, -3:4#"&'()*+F
! S/"$B;#J-(BB$CB$-+$:$+"&$(70 94& -+3:4"$:4, ()-&/0 +$7()$0 (?"4-#,:#?$+-0 94:& -#, $)":3&-(#$#"&'()*+L
! B;#J-(BB$/:+$:$9+")$7)-"#? 4;, ):
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Z O94& -T+3:4"$+&(3/:+&-3T? -+3)"&"$:#?$+&(3/:+&-3T
3(#&-#9(9+$:4, ()-&/0 $"#:5 4"+$+-0 94:& -(#+$(7
/9#?)"?+$(7$+(4>"?$-#$&/(9+:#?+$(7
)":3&-(#+$' -&/$? -+
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W(#+&)93&-(#$(7$:$J-(TU(, -3:4$D%CW(#+&)93&-(#$(7$:$J-(TU(, -3:4$D%C
!:&"!:&"
Our Molecular Toolbox
LacI and TetR repressors
DNA sites: lac operators (lacO1, lacO2, lacO3), tet operators (tetO1, tetO2)
Promoter sequences (-35 and -10 !70 dependent hexamers)
RBS sequences (hairpin secondary structures, RNAse binding sites)
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LTT
TLT
TTL
W(0
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P
LTT
TLT
TTL
Wet-lab experimentsWet-lab experiments
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Computer-Aided
Design of Bio-
Logical AND Gates
• Models capture experimental
phenotype.
•TTL is the highest-fidelity And
gate.
• Leakage of lacO can explain
the variable phenotypic
behavior. Biological insight.
•Double-L systems not
expressing enough GFP. Too
much LacI in E.coli strain.
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B900:);B900:);
! D>:-4:5 4"$&((45(6$(7$C%D$+"N9"#3"+$:#?$)", 94:&();"4$, "#"$#"&'()*+$&($3(#&)(4