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