Programming DNA sequences to engineer multi-component...

Programming DNA sequences to engineer multi-component chemical systems

Neil Dalchau

Computational Science Laboratory

Biological Computation Group

Synthetic Biology and Control WorkshopWorcester College, University of Oxford

10 - 12th September 2014

2050: Doctor in a cell (Udi Shapiro)

Programmable molecular computer

computational

science

laboratory

Programming biological systems

Molecules Cells Colonies

Computer Aided Design software

computational

science

laboratory

Computational DNA Drugs

• Perform logical computation before releasing drug

• Uses restriction enzymes

Simplified (omitting the “no” pathway)

An automaton sequentially reading the string PPAP2B, GSTP1, PIM1, HPS (known cancer indicators) and sequentially cutting the DNA hairpin until a ssDNA drug (Vitravene) is released.

Vitravene (GCGTTTGCTCTTCTTCTTGCG)

(Restriction Enzymes)

Building stuff with DNA…

computational

science

laboratory

Using DNA to implement algorithms

DNA Strand Displacement

Microsoft Research Outreach

computational

science

laboratory

VIDEO

DNA structure abstraction

8

computational

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laboratory

DNA domains

Short complementary domains bind reversibly

Long complementary domains bind irreversibly

9

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

10

Input 1 Input 2

Output

Substrate

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

11

Input 2

Output

Substrate

Input 1

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

12

Input 2

Output

Substrate

Input 1

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

13

Input 2

Output

Substrate

Input 1

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

14

Output

Substrate

Input 1

Input 2

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

15

Substrate

Input 1 Input 2

Output

computational

science

laboratory

Strand displacement logic circuit

Output = Input1 AND Input2

16

Substrate

Input 1 Input 2

Output

computational

science

laboratory

Domain abstraction

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tb --> (5') TACCAA (3')tx --> (5') TATTCC (3')to --> (5') GTCA (3')b --> (5') CCCTTTTCTAAACTAAACAA (3')x --> (5') CCCAAAACAAAACAAAACAA (3')

computational

science

laboratory

DNA strand displacement (DSD) language

Programming DNA circuits

Step 1: Program circuit design Step 2: Compile circuit behaviour Step 3: Simulate circuit

computational

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Approximate Majority algorithm

X + Y 2BX + B 2XY + B 2Y

Y (Minority)

X (Majority)

X (Majority)

computational

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Autocatalytic component

X + B 2X + PX

BX

N.B. Two-Domain DNA Strand Displacement was proposed by Luca Cardelli (DCM 2010)

X XPX

High-level specification

DNA-level implementation

computational

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Join gate Fork gate

Autocatalytic component

X + B 2X + PXHigh-level specification

DNA-level implementation

computational

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Component characterisation

X + B 2X + PX

computational

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Data - - - Model fitting+ + Model fitting

Component characterisation

X + B 2X + PX

Data - - - Model fitting+ + Model fitting

computational

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X + Y 2B + PB

Y + B 2Y + PY

Predicting dynamics of the full circuit

X + Y 2B X + B 2X Y + B 2Y

Experimental Data - - - Model prediction

computational

science

laboratory

Predicting dynamics of the full circuit

X + Y 2B X + B 2X Y + B 2Y

Experimental Data - - - Model prediction

computational

science

laboratory

Speeding up with higher concentrations

X + Y 2B X + B 2X Y + B 2Y

Experimental Data - - - Model prediction

computational

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Consensus over the initial majority

computational

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Summary

• DNA is an excellent programmable material

• CRNs offer a rich set of behaviours that can be recapitulated using DNA-based strategies

• Detailed molecular models can be programmed using Visual DSD

– Bayesian parameter inference enables characterisation of the detailed models

– Predictions can be very good

computational

science

laboratory

Consensus in space

Dalchau, Seelig, Phillips (DNA 2014)

computational

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Conclusions

• Manipulating DNA sequences enables the creation of complex circuits

– Implement algorithms using DNA strand displacement

– Fine-tune and sometimes completely change sensitivity to input signals

• Detailed mathematical modelling, in combination with parameter inference, enables quantification of specific biochemical processes

– Where are the bottlenecks? Where is there interference?

– How do we optimally modify the system to improve performance?

Acknowledgements

Yuan-Jyue ChenGeorg Seelig

David Soloveichik

Andrew PhillipsBoyan YordanovLuca CardelliStephen EmmottMatt LakinFilippo PoloColin GravillRasmus Petersen

University of Washington

UCSF

Microsoft Research Cambridge

Paul GrantJames BrownJim HaseloffJim Ajioka

University of Cambridge

computational

science

laboratory

http://research.microsoft.com/science/tools