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Molecular Computation with Automated Microfluidic Sensors (MCAMS) Laura Landweber * Princeton University L. L. Sohn† M. Singh A. Sahai R. Weiss Stanford.

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Presentation on theme: "Molecular Computation with Automated Microfluidic Sensors (MCAMS) Laura Landweber * Princeton University L. L. Sohn† M. Singh A. Sahai R. Weiss Stanford."— Presentation transcript:

1 Molecular Computation with Automated Microfluidic Sensors (MCAMS) Laura Landweber * Princeton University L. L. Sohn† M. Singh A. Sahai R. Weiss Stanford University C. Webb R. Davis UC Berkeley A. P. Alivisatos *DARPA Biocomp PI †NSF ITR PI

2 Molecular Computation with Automated Microfluidic Sensors (MCAMS) Accelerate the field of molecular-based computing by increasing sensitivity and throughput and enabling “hands-free” molecular computation. Combine microfluidic technology and biophysical methods for detecting nucleic acids with recently-developed algorithms of RNA-based computing to create a compact, automated, scalable, nucleotide-based computational device capable of rapidly and directly detecting the computational output. New Ideas Impact/Relevance

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7 Sensing Single Molecules of DNA Saleh & Sohn

8 The sensing device EBL on quartz is time consuming Etched samples have finite lifetimes- after ~5 msmts., too dirty to clean and reuse Solution: Embed pore in PDMS- make one master, cast from it forever… Seal PDMS to glass slip that holds electrodes

9 The master Negative pore: Electron-beam-defined polystyrene line (height, width adjustable 450 nm to <100 nm), or photolith defined etched quartz (1x1  m and bigger) Negative reservoirs: SU-8 photoresist (5  m thick)

10 Saleh and Sohn’s device in PDMS AFM on PDMS shows successful casting down to 200 nm line width Optical image of sealed devices a small and quickly fabricated pore! PDMS Reservoir Pore Reservoir

11 Measuring DNA Each downward spike=single DNA molecule Pore: diameter~300 nm, 4  m long Why does peak size vary? Varying DNA conformation?

12 selection principle: DNA input and transport principle

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14 negative selection: DNA input and transport principle

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16 selection principle: logical operations

17 selection principle: logical NOT operation

18 selection principle: logical AND operation a  ba  b

19 selection principle: logical OR operation a  ba  b

20 (  h   f)   a 3x3 knight problem

21 Immobilization principle: surface enlargement with streptavidin coated beads, NHS-LC-biotin to aminolated silicon surface silicon surface NHS-LC-biotin streptavidin

22 Immobilization principle: surface enlargement with streptavidin coated beads, NHS-LC-biotin to aminolated silicon surface biotinylated single DNA strands silicon surface NHS-LC-biotin streptavidin

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24 Nanocrystal-labeled DNA Alivisatos and Schultz, Nature 382 p ; Angew. Chemie 38 p related work by (Mirkin and Letsinger,) (Silvan and Braun)

25 Isolating gold nanocrystals bearing discrete numbers of oligonucleotides

26 read-out: gold particles electrodes Au nanocrystals library strand

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28 Scheduled Milestones/Success Metrics Design prototype microfluidic system Identify methods for sizing and detection of RNA-based computation outputs, such as electrical detection. Develop microfluidic chip to perform RNA-based computation Explore ways to make the chip versatile for different computing algorithms Design microfluidic chip with reaction wells and switching valves Identify methods to detect 15-nt bits in RNA computation Solve an instance of a SAT problem using microfluidic device Year 1Year 2

29 Molecular Computation with Automated Microfluidic Sensors Princeton University L. F. Landweber (15%)* L. L. Sohn (10%) † M. Singh A. Sahai (5%) R. Weiss Danny van Noort (100%) Omar A. Saleh (30%) Zhao Huang (50%) Stanford University C. Webb (10%) R. Davis (1%) W. Tongparsit (50%) UC Berkeley A. P. Alivisatos (10%) Christine Micheel (40%) Teresa Pelelgrino (20%) *DARPA Biocomp PI †NSF ITR PI


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