ComputationalComputational
Synthetic BiologySynthetic Biology
Yiannis N. Kaznessis
University of Minnesota
Department of Chemical Engineering and Materials Science
gene
RNAp
operator
repressor
genepromoter
RNAp
operator
activator
mRNA ribosome
Protein product
mRNA
genepromoter
RNAp
promoter
!"#"$%"&'()*+
Inducer Binding
Gene expressionGene expression
• Transcription
• Translation
Gene RegulationGene Regulation
• Activation
• Repression
InductionInduction
Gene NetworksGene Networks
!"#"$#"&'()*$"#, -#"")-#,!"#"$#"&'()*$"#, -#"")-#,! . -&/$!"#(0"$1)(2"3&+$&((45(6"+$(7
! 8",94:&();$
H6:0
H6:0
H#, -#"")I+$-"'! K)(0 $:#$"#, -#"")-#, $-"' L $+;#&/"&-3
5 -( 4(, ;$-+$7 -)+& $:#?$7()"0(+&$:$':;$(7$:++"05 4"
3(0
J-(J)-3*+
= /&&< GMM
Light responsive system, dual regulation
Repressilator
Toggle Switch
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
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))
O94& -+3:4"O94& -+3:4" $O(?"4-#,$O(?"4-#,
K):0"'()*K):0"'()*! QG $$$$C-+3)"&"$M$B&(3/:+&-3
! R90$
/&&< 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+")$7)-"#? 4;, ):
Z O94& -T+3:4"$+&(3/:+&-3T? -+3)"&"$:#?$+&(3/:+&-3T
3(#&-#9(9+$:4, ()-&/0 $"#:5 4"+$+-0 94:& -(#+$(7
/9#?)"?+$(7$+(4>"?$-#$&/(9+:#?+$(7
)":3&-(#+$' -&/$? -+
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)
LTT
TLT
TTL
W(0
P
LTT
TLT
TTL
Wet-lab experimentsWet-lab experiments
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.
B900:);B900:);
! D>:-4:5 4"$&((45(6$(7$C%D$+"N9"#3"+$:#?$)", 94:&();"4$, "#"$#"&'()*+$&($3(#&)(4
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