microfluidic biochips: design, programming, and optimization bill thies joint work with vaishnavi...

Microfluidic Biochips: Design, Programming, and Optimization

Bill Thies

Joint work with Vaishnavi Ananthanarayanan, J.P. Urbanski, Nada Amin, David Craig, Jeremy Gunawardena,

Todd Thorsen, and Saman Amarasinghe

Indian Statistical InstituteMarch 2, 2011

Microfluidic Chips• Idea: a whole biology lab on a single chip

– Input/output– Sensors: pH, glucose,

temperature, etc.– Actuators: mixing, PCR,

electrophoresis, cell lysis, etc.

• Benefits:– Small sample volumes– High throughput– Geometrical manipulation– Portability

• Applications:– Biochemistry - Cell biology– Biological computing

1 mm 10x real-time

Application to Rural Diagnostics

DisposableEnteric Card

PATH,Washington U.Micronics, Inc.,U. Washington

Targets: - E. coli, Shigella, Salmonella, C. jejuni

DxBox

U. Washington,Micronics, Inc.,Nanogen, Inc.

Targets: - malaria (done) - dengue, influenza, Rickettsial diseases, typhoid, measles (under development)

CARD

Rheonix, Inc.

Targets: - HPV diagnosis - Detection of specific gene sequences

Moore’s Law of Microfluidics:Valve Density Doubles Every 4 Months

Source: Fluidigm Corporation (http://www.fluidigm.com/images/mlaw_lg.jpg)

Moore’s Law of Microfluidics:Valve Density Doubles Every 4 Months

Source: Fluidigm Corporation (http://www.fluidigm.com/didIFC.htm)

Current Practice: Manage Gate-Level Details from Design to Operation

• For every change in the experiment or the chip design:

1. Manually draw in AutoCAD 2. Operate each gate from LabView

fabricatechip

Abstraction Layers for Microfluidics

C

x86

Pentium III,Pentium IV

Silicon Analog

transistors, registers, …

Fluidic Instruction Set Architecture (ISA) - primitives for I/O, storage, transport, mixing

Protocol Description Language - architecture-independent protocol description

Fluidic Hardware Primitives - valves, multiplexers, mixers, latches

chip 1 chip 2 chip 3






BioCoder Language[J.Bio.Eng. 2010]

Contributions

Optimized Compilation[Natural Computing 2007]

Demonstrate Portability[DNA 2006]

Micado AutoCAD Plugin[MIT 2008, ICCD 2009]

Digital Sample Control Using Soft Lithography

[Lab on a Chip ‘06]

• Continuous flow of fluids (or droplets) through fixed channels [Whitesides, Quake, Thorsen, …]

• Pro:– Smaller, more precise sample sizes– Made-to-order availability [Stanford]– More traction in biology community

Droplets vs. Continuous Flow• Digital manipulation of droplets

on an electrode array [Chakrabarty, Fair, Gascoyne, Kim, …]

• Pro:– Reconfigurable routing– Electrical control– More traction in CAD community

Source: Chakrabarty et al, Duke University

Primitive 1: A Valve (Quake et al.)

Control Layer

Flow Layer

0. Start with mask of channels


Control Layer

Flow Layer

1. Deposit pattern on silicon wafer


Control Layer

Flow Layer

2. Pour PDMS over mold - polydimexylsiloxane: “soft lithography”

Thick layer (poured)

Thin layer (spin-coated)


Control Layer

Flow Layer

3. Bake at 80° C (primary cure), then release PDMS from mold


Control Layer

Flow Layer

4a. Punch hole in control channel4b. Attach flow layer to glass slide


Control Layer

Flow Layer

5. Align flow layer over control layer


Control Layer

Flow Layer

6. Bake at 80° C (secondary cure)


Control Layer

Flow Layer

pressure

actuator

7. When pressure is high, controlchannel pinches flow channel toform a valve

Primitive 2: A Multiplexer (Thorsen et al.)

flow layercontrol layer

Bit 2 Bit 1 Bit 00 1 0 1 0 1

Input

Output 0

Output 7

Output 6

Output 5

Output 4

Output 3

Output 2

Output 1

• Control lines can cross flow lines

- Only thick parts make valves

• Logic is not complimentary

• To control n flow lines, need 2 log2 n control lines

Primitive 2: A Multiplexer (Thorsen et al.)

Bit 2 Bit 1 Bit 00 1 0 1 0 1

Input

Output 0

Output 7

Output 6

Output 5

Output 4

Output 3

Output 2

Output 1

Example: select 3 = 011

flow layercontrol layer

• Control lines can cross flow lines

- Only thick parts make valves

• Logic is not complimentary

• To control n flow lines, need 2 log2 n control lines

Primitive 3: A Mixer (Quake et al.)

1. Load sample on bottom2. Load sample on top

3. Peristaltic pumping

Rotary Mixing

Primitive 4: A Latch (Our contribution)

• Purpose: align sample with specific location on device– Examples: end of storage cell, end of mixer, middle of sensor

• Latches are implemented as a partially closed valve– Background flow passes freely– Aqueous samples are caught

Sample Latch

Primitive 5: Cell Trap• Several methods for confining cells in microfluidic chips

– U-shaped weirs - C-shaped rings / microseives– Holographic optical traps - Dialectrophoresis

• In our chips: U-Shaped Microseives in PDMS Chambers

Source: Wang, Kim, Marquez, and Thorsen, Lab on a Chip 2007

Primitive 6: Imaging and Detection• As PDMS chips are translucent,

contents can be imaged directly– Fluorescence, color, opacity, etc.

• Feedback can be used todrive the experiment

Driving Applications

1. What are the best indicators for oocyte viability?- With Mark Johnson’s and

Todd Thorsen’s groups- During in-vitro fertilization,

monitor cell metabolites and select healthiest embryo for implantation

2. How do mammalian signal transduction pathways respond to complex inputs?- With Jeremy Gunawardena’s

and Todd Thorsen’s groups- Isolate cells and stimulate with

square wave, sine wave, etc.

Driving Applications

1. What are the best indicators for oocyte viability?- With Mark Johnson’s and

Todd Thorsen’s groups- During in-vitro fertilization,

monitor cell metabolites and select healthiest embryo for implantation

2. How do mammalian signal transduction pathways respond to complex inputs?- With Jeremy Gunawardena’s

and Todd Thorsen’s groups- Isolate cells and stimulate with

square wave, sine wave, etc.

Video courtesy David Craig

CAD Tools for Microfluidic Chips• Goal: automate placement, routing, control of

microfluidic features

• Why is this different than electronic CAD?

CAD Tools for Microfluidic Chips• Goal: automate placement, routing, control of

microfluidic features

• Why is this different than electronic CAD?

1. Control ports (I/O pins) are bottleneck to scalability– Pressurized control signals cannot yet be generated on-chip– Thus, each logical set of valves requires its own I/O port

2. Control signals correlated due to continuous flows

Demand & opportunity for minimizing control logicpipelined flow continuous flow

Our Technique:Automatic Generation of Control Layer


1. Describe Fluidic ISA


1. Describe Fluidic ISA2. Infer control valves


1. Describe Fluidic ISA2. Infer control valves3. Infer control sharing


1. Describe Fluidic ISA2. Infer control valves3. Infer control sharing4. Route valves to control ports


1. Describe Fluidic ISA2. Infer control valves3. Infer control sharing4. Route valves to control ports5. Generate an interactive GUI

1. Describe a Fluidic ISA• Hierarchical and composable flow declarations

Sequential flow P1 P2

AND-flow F1 Λ F2

OR-flow F1 \/ F2

Mixing mix(F)

Pumped flow pump(F)

P1 P2

F1

F2

F1

F2or

F

F

F1

F2

1. Describe a Fluidic ISA

1. Describe a Fluidic ISA

mix-and-store (S1, S2, D) {

1. in1 top out

2. in2 bot out

3. mix(top bot-left bot-right top)

4. wash bot-right

top bot-left store

}

50x real-time

2. Infer Control Valves

3. Infer Control Sharing

3. Infer Control Sharing

Column Compatibility Problem - NP-hard - Reducible to graph coloring

4. Route Valves to Control Ports

• Build on recent algorithm for simultaneous pin

assignment & routing [Xiang et al., 2001]

• Idea: min cost - max flow from valves to ports

• Our contribution: extend algorithm to allow sharing

– Previous capacity constraint on each edge:

– Modified capacity constraint on each edge:

Solve with linear programming, allowing sharing where beneficial

f1 + f2 + f3 + f4 + f5 + f6 ≤ 1

max(f1, f4) + max(f2 , f3) + f5 + f6 ≤ 1

Micado: An AutoCAD Plugin• Implements ISA, control inference, routing, GUI export

– Using slightly older algorithms than presented here [Amin ‘08]

– Parameterized design rules– Incremental construction of chips

• Realistic use by at least 3 microfluidic researchers

• Freely available at:http://groups.csail.mit.edu/cag/micado/

Cell Culture with Waveform Generator

Courtesy David Craig

Embryonic Cell Culture

Courtesy J.P. Urbanski

Metabolite Detector


Open Problems• Automate the design of the flow layer

– Hardware description language for microfluidics– Define parameterized and reusable modules

• Replicate and pack a primitive as densely as possible– How many cell cultures can you fit on a chip?

• Support additional primitives and functionality– Metering volumes– Sieve valves– Alternate mixers– Separation primitives– …

Conclusions: Microfludics CAD• Microfluidics represents a rich new

playground for CAD researchers

• Two immediate goals:– Enable designs to scale– Enable non-experts to design own chips

• Micado is a first step towards these goals– Hierarchical ISA for microfluidics– Inference and minimization of control logic– Routing shared channels to control ports– Generation of an interactive GUI

http://groups.csail.mit.edu/cag/micado/


Toward “General Purpose” Microfluidic Chips

Inpu

ts

Out

puts

FluidicStorage(RAM)

In

Out

MixingChambers

Inpu

ts

Out

puts

FluidicStorage(RAM)

In

Out

MixingChambers

Sensorsand

Actuators

Inpu

ts

Out

puts

FluidicStorage(RAM)

In

Out

MixingChambers

Inpu

ts

Out

puts

FluidicStorage(RAM)

In

Out

MixingChambers

Sensorsand

Actuators

A Digital Architecture• Recent techniques can control independent samples

– Droplet-based samples– Continuous-flow samples– Microfluidic latches

• In abstract machine, allsamples have unit volume– Input/output a sample– Store a sample– Operate on a sample

• Challenge for a digital architecture: fluid loss– No chip is perfect – will lose some volume over time– Causes: imprecise valves, adhesion to channels, evaporation, ...– How to maintain digital abstraction?

[Fair et al.]

[Our contribution]

[Our contribution]

Inputs

Outp

uts

FluidicStorage(RAM)

In

Out

MixingChambers

Inputs

Outp

uts

FluidicStorage(RAM)

In

Out

MixingChambers

Sensorsand

Actuators

Inputs

Outp

uts

FluidicStorage(RAM)

In

Out

MixingChambers

Inputs

Outp

uts

FluidicStorage(RAM)

In

Out

MixingChambers

Sensorsand

Actuators

Maintaining a Digital Abstraction

Maintaining a Digital Abstraction

Instruction SetArchitecture (ISA)

High-LevelLanguage

Hardware

Electronics Microfluidics

Replenish charge(GAIN)

Loss of charge

RandomizedGates [Palem]

Soft errorHandling?

Replenish fluids? - Maybe (e.g., with water) - But may affect chemistry

Loss of fluids

? ? ?

? ? ?

Expose loss in ISA - Compiler deals with it

Expose loss in language- User deals with it

Towards a Fluidic ISA• Microfluidic chips have various mixing technologies

– Electrokinetic mixing– Droplet mixing– Rotary mixing

• Common attributes:– Ability to mix two samples in equal proportions, store result

• Fluidic ISA: mix (int src1, int src2, int dst)– Ex: mix(1, 2, 3)

– To allow for lossy transport, only 1 unit of mixture retained

[Quake et al.]

[Fair et al.]

[Levitan et al.]

Storage Cells

1234

Mixer

Implementation: Oil-Driven Chip

Inputs Storage CellsBackground

Phase Wash Phase Mixing

Chip 1 2 8 Oil — Rotary

Implementation: Oil-Driven Chip




mix (S1, S2, D) {

1. Load S1

2. Load S2

3. Rotary mixing

4. Store into D

}

50x real-time

Implementation 2: Air-Driven Chip




Chip 2 4 32 Air Water In channels

Implementation 2: Air-Driven Chip

mix (S1, S2, D) {

1. Load S1

2. Load S2

3. Mix / Store into D

4. Wash S1

5. Wash S2

}




Chip 2 4 32 Air Water In channels

50x real-time

“Write Once, Run Anywhere”

• Example: Gradient generation

• Hidden from programmer:– Location of fluids– Details of mixing, I/O– Logic of valve control– Timing of chip operations

450 Valve Operations

Fluid yellow = input (0);Fluid blue = input(1);for (int i=0; i<=4; i++) { mix(yellow, 1-i/4, blue, i/4);}




setValve(0, HIGH); setValve(1, HIGH);setValve(2, LOW); setValve(3, HIGH);setValve(4, LOW); setValve(5, LOW);setValve(6, HIGH); setValve(7, LOW);setValve(8, LOW); setValve(9, HIGH);setValve(10, LOW); setValve(11, HIGH);setValve(12, LOW); setValve(13, HIGH);setValve(14, LOW); setValve(15, HIGH);setValve(16, LOW); setValve(17, LOW);setValve(18, LOW); setValve(19, LOW);wait(2000);setValve(14, HIGH); setValve(2, LOW);wait(1000);setValve(4, HIGH); setValve(12, LOW);setValve(16, HIGH); setValve(18, HIGH);setValve(19, LOW);wait(2000);

wait(2000);setValve(14, HIGH); setValve(2, LOW);wait(1000);setValve(4, HIGH); setValve(12, LOW);setValve(16, HIGH); setValve(18, HIGH);setValve(19, LOW);wait(2000);setValve(0, LOW); setValve(1, LOW);setValve(2, LOW); setValve(3, HIGH);setValve(4, LOW); setValve(5, HIGH);setValve(6, HIGH); setValve(7, LOW);setValve(8, LOW); setValve(9, HIGH);setValve(10, HIGH); setValve(11, LOW);setValve(12, LOW); setValve(13, LOW);setValve(14, LOW); setValve(15, HIGH);setValve(16, HIGH); setValve(17, LOW);setValve(18, HIGH); setValve(19, LOW);



















Direct Control- 450 valve actuations- only works on 1 chip


















Gradient Generation in Fluidic ISA

input(0, 0);input(1, 1);input(0, 2);mix(1, 2, 3);input(0, 2);mix(2, 3, 1);input(1, 3);input(0, 4);mix(3, 4, 2);input(1, 3);input(0, 4);mix(3, 4, 5);input(1, 4);mix(4, 5, 3);mix(0, 4);

Direct Control- 450 valve actuations- only works on 1 chip

Fluidic ISA- 15 instructions- portable across chips

abstraction

















Gradient Generation in Fluidic ISA


Fluid[] out = new Fluid[8];Fluid yellow, blue, green;out[0] = input(0);yellow = input(0);blue = input(1);green = mix(yellow, blue);yellow = input(0);out[1] = mix(yellow, green);yellow = input(0);blue = input(1);out[2] = mix(yellow, blue);yellow = input(0);blue = input(1);green = mix(yellow, blue);blue = input(1);out[3] = mix(blue, green);out[4] = input(1);

Abstraction 1: Managing Fluidic Storage

• Programmer uses location-independent Fluid variables– Runtime system assigns & tracks location of each Fluid– Comparable to automatic memory management (e.g., Java)

FluidicISA

1. StorageManagement


Fluid[] out = new Fluid[8];Fluid yellow = input(0);Fluid blue = input(1);Fluid green = mix(yellow, blue);

out[0] = yellow;out[1] = mix(yellow, green);out[2] = green;out[3] = mix(blue, green);out[4] = blue;


Abstraction 2: Fluid Re-Generation

2. Fluid Re-Generation

• Programmer may use a Fluid variable multiple times– Each time, a physical Fluid is consumed on-chip– Runtime system re-generates Fluids from computation history

Abstraction 3: Arbitrary Mixing

• Allows mixing fluids in any proportion, not just 50/50– Fluid mix (Fluid F1, float p1, Fluid f2, float F2)

Returns Fluid that is p1 parts F1 and p2 parts F2

– Runtime system translates to 50/50 mixes in Fluidic ISA– Note: some mixtures only reachable within error tolerance

Fluid[] out = new Fluid[8];Fluid yellow = input (0);Fluid blue = input (1);

out[0] = yellow;out[1] = mix(yellow, 3/4, blue, 1/4);out[2] = mix(yellow, 1/2, blue, 1/2);out[3] = mix(yellow, 1/4, blue, 3/4);out[4] = blue;

3. Arbitrary Mixing




Abstraction 3: Arbitrary Mixing


for (int i=0; i<=4; i++) { out[i] = mix(yellow, 1-i/4, blue, i/4);}

• Allows mixing fluids in any proportion, not just 50/50– Fluid mix (Fluid F1, float p1, Fluid f2, float F2)

Returns Fluid that is p1 parts F1 and p2 parts F2

– Runtime system translates to 50/50 mixes in Fluidic ISA– Note: some mixtures only reachable within error tolerance

4. Parameterized Mixing


out[0] = yellow;out[1] = mix(yellow, 3/4, blue, 1/4);out[2] = mix(yellow, 1/2, blue, 1/2);out[3] = mix(yellow, 1/4, blue, 3/4);out[4] = blue;

3. Arbitrary Mixing

Algorithms for Efficient Mixing• Mixing is fundamental operation of microfluidics

– Prepare samples for analysis– Dilute concentrated substances– Control reagant volumes

• How to synthesize complex mixture using simple steps?– Many systems support only 50/50 mixers– Should minimize number of mixes, reagent usage– Note: some mixtures only reachable within error tolerance

• N

Analogous to ALU operations on microprocessors

Interesting scheduling and optimization problem

Why Not Binary Search?0 13/8

1/41/2

1/23/8 5 inputs, 4 mixes

Why Not Binary Search?0 13/8

3/41/2

3/8

4 inputs, 3 mixes

1/41/2

1/23/8 5 inputs, 4 mixes

Min-Mix Algorithm• Simple algorithm yields minimal number of mixes

– For any number of reagents, to any reachable concentration– Also minimizes reagent usage on certain chips

1. The mixing process can be represented by a tree.

Min-Mix Algorithm: Key Insights

BA

B

A

5/8 A, 3/8 B


2. The contribution of an input sample to the overall mixture is 2-d, where d is the depth of the sample in the tree


BA

B

A

5/8 A, 3/8 B

d

3

2

1

2-d

1/8

1/4

1/2


2. The contribution of an input sample to the overall mixture is 2-d, where d is the depth of the sample in the tree

3. In the optimal mixing tree, a reagent appears at depths corresponding to the binary representation of its overall concentration.


BA

B

A

5/8 A, 3/8 B

d

3

2

1

2-d

1/8

1/4

1/2

3 = 011

0

1

1

5 = 101

1

0

1

Min-Mix Algorithm• Example: mix 5/16 A, 7/16 B, 4/16 C

• To mix k fluids with precision 1/n:– Min-mix algorithm: O(k log n) mixes– Binary search: O(k n) mixes

4

3

2

1

B CA

B

A B

A

A

B

B

B

C

1/16

1/8

1/4

1/2

2-dd

A=5 B=7 C=4 =0101 =0111 =0100

[Natural Computing 2007]

Related Work• Algorithms for microfluidic sample preparation [Sudip Roy et al.]

• Aquacore – builds on our work, ISA + architecture [Amin et al.]

• Automatic scheduling of biology protocols– EDNAC computer for automatically solving 3-SAT [Johnson]– Compile SAT to microfluidic chips [Landweber et al.] [van Noort] – Mapping sequence graphs to grid-based chips [Su/Chakrabarty]

• Custom microfluidic chips for biological computation– DNA computing [Grover & Mathies] [van Noort et al.] [McCaskill] [Livstone,

Weiss, & Landweber] [Gehani & Reif] [Farfel & Stefanovic]– Self-assembly [Somei, Kaneda, Fujii, & Murata] [Whitesides et al.]

• General-purpose microfluidic chips– Using electrowetting, with flexible mixing [Fair et al.] – Using dialectrophoresis, with retargettable GUI [Gascoyne et al.]– Using Braille displays as programmable actuators [Gu et al.]

Conclusions: Fluidic ISA• Fluidic ISA gives flexibility to evolve underlying

hardware– Including various device technologies

• It remains to be seen to what degree the functionality of chips can be standardized– May need to reconsider abstractions for analog machines

• Opportunities for future work– Rich extensions to mixing algorithms– Schedule separate protocols onto shared hardware,

maximizing utilization of shared resource (e.g., thermocycler)– Parallelize single protocol across people or chips– Dynamic optimization of slow co-processors (lazy vectorization)?

“Immunological detection ... was carried out as described in the Boehringer digoxigenin-

nucleic acid detection kit with some modifications.”

- Main paper

- Ref. papers

- References

Papers from Nature, Spring 2010

Problems with ExistingDescriptions of Protocols

• Incomplete– Cascading references several levels deep– Some information missing completely

• Ambiguous– One word can refer to many things– E.g., “inoculate” a culture

• Non-uniform– Different words can refer to the same thing– E.g., “harvest”, “pellet down”, “centrifuge” are equivalent

• Not suitable for automation

BioCoder: A High-Level Programming Language for Biology Protocols

1. Enable automation via microfluidic chips

2. Improve reproducibility of manual experiments

In biology publications, can we replace the textual description of the methods used with a computer program?

Related Work• EXACT: EXperimental ACTions ontology as a formal

representation for biology protocols [Soldatova et al., 2009]

• Robot Scientist: functional genomics driven by macroscopic laboratory automation [King et al., 2004]

• PoBol: RDF-based data exchange standard for BioBricks

The BioCoder Language• BioCoder is a protocol language for reuse & automation

– Independent of chip or laboratory setup– Initial focus: molecular biology

• Implemented as a C library– Used to express 65 protocols, 5800 lines of code– Restricted backend: automatic execution on microfluidic chips– General backend: emit readable instructions for human

• Validation – Used to direct actions of scientists in the lab (IISc)– Used to control microfluidic chips (MIT)– Standardizing online Wiki; interest from Berkeley, UW, UMN…

• Declaration / measurement / disposal- declare_fluid- declare_column- measure_sample - measure_fluid- volume - discard- transfer- transfer_column- declare_tissue

• Combination / mixing- combine - mix

- combine_and_mix- addto_column- mixing_table

• Centrifugation- centrifuge_pellet

- centrifuge_phases - centrifuge_column

• Temperature - set_temp

- use_or_store - autoclave

• Timing - wait

- time_constraint - store_until- inoculation- invert_dry

• Detection- ce_detect

- gas_chromatography - nanodrop- electrophoresis- mount_observe_slide- sequencing

BioCoder Language Primitives

FluidSample f1 = measure_and_add(f0, lysis_buffer, 100*uL);FluidSample f2 = mix(f1, INVERT, 4, 6);time_constraint(f1, 2*MINUTES, next_step);

Example: Plasmid DNA Extraction

I. Original protocol (Source: Klavins Lab)

II. BioCoder code

III. Auto-generated text output

Add 100 ul of 7X Lysis Buffer (Blue) and mix by inverting the tube 4-6 times. Proceed to step 3 within 2 minutes.

Add 100 ul of 7X Lysis Buffer (Blue).Invert the tube 4-6 times.NOTE: Proceed to the next step within 2 mins.

Example: Plasmid DNA Extraction

Auto-GeneratedDependence Graph

1. Data Types

Sample

FluidSample SolidSample

Stock

Fluid Solid

conceptually infinite volume finite volume

measure instruction

Can measure symbolic volumewhich is resolved by compiler / runtime system depending on

eventual uses

declare_fluidinstruction

other instructions

2. Standardizing Ad-Hoc Language• Need to convert qualitative words to quantitative scale

• Example: a common scale for mixing– When a protocol says “mix”, it could mean many things– Level 1: tap– Level 2: stir– Level 3: invert– Level 4: vortex / resuspend / dissolve

• Similar issues with temperature, timing, opacity, …

3. Separating Instructions from Hints• How to translate abstract directions?

– “Remove the medium by aspiration, leaving the bacterial pellet as dry as possible.”

• Hints provide tutorial or self-check information– Can be ignored if rest of protocol is executed correctly

• Separating instructions and hints keeps language tractable– Small number of precise instructions– Extensible set of hints

Centrifuge(&medium, ...);hint(pellet_dry)

Aspirate and remove medium.Leave the pellet as dry as possible.

4. Generating Readable Instructions• In typical programming languages, aim to define the

minimal set of orthogonal primitives

• However, for generating readable protocols, separating primitives can detract from readabilityOriginal: “Mix the sample with 1uL restriction enzyme.”

BioStream with orthogonal primitives:

FluidSample s1 = measure(&restriction_enzyme, 1*uL); FluidSample s2 = combine(&sample, &s1); mix(s2, vortex);

Measure out 1uL of restriction enzyme.

Combine the sample with the restriction enzyme.

Mix the combined sample by vortexing.




BioStream with compound primitives:

combine_and_mix(&restriction_enzyme, 1*uL, &sample, vortex);

Add 1uL restriction enzyme and mix by vortexing.




BioStream with compound primitives:

combine_and_mix(&restriction_enzyme, 1*uL, &sample, vortex);

Add 1uL restriction enzyme and mix by vortexing.

• General approaches:– Detect combined operations with policies (peekhole optimizer)– Define a standard library that combines primitive operations

4. Generating Readable Instructions

mixing_table_pcr(7,20,array_pcr,initial_conc, final_conc,vol);

5. Timing Constraints• Precise timing is critical for many biology protocols

– Minimum delay: cell growth, enzyme digest, denaturing, etc.– Maximum delay: avoid precipitation, photobleaching, etc.– Exact delay: regular measurements, synchronized steps, etc.

• May require parallel execution– Fluid f1 = mix(…); useBetween(f1, 10, 10); – Fluid f2 = mix(…); useBetween(f2, 10, 10);– Fluid f3 = mix(f1, f2);

f1 f2

f3

1010

Benchmark Suite

65 protocols5800 LOC

Plasmid DNA Extraction

214 lines

PCR

73 lines

repeatthermocycling

Molecular Barcodes

200 lines

Preparation

+ PCR (2)

DNA Sequencing

144 linesPreparation

PCR

Analysis

PCR PCR PCR

Instruction Usage

Validating the Language

• Eventual validation: automatic execution– But BioCoder more capable than most chips today– Need to decouple language research from microfluidics research

• Initial validation: human execution– In collaboration with Prof. Utpal Nath’s lab at IISc– Target Plant DNA Isolation, common task for summer intern

Biologist is never exposed to original lab notes

• To the best of our knowledge, first execution of a real biology protocol from a portable programming language

Original Lab Notes

BioCoderCode

Auto-Generated Protocol

Executionin Lab

3. Add 1.5 vol. CTAB to each MCT and vortex. Incubate at 65° C for 10-30 mins

4. Add 1 vol. Phenol:chloroform:isoamylalcohol: 48:48:4 and vortex thoroughly

5. Centrifuge at 13000g at room temperature for 5 mins

6. Transfer aqueous (upper) layer to clean MCT and repeat the extraction using chloroform: Isoamyalcohol: 96:4

Exposing Ambiguity in Original Protocols

?

Coding protocols in precise language removes

ambiguity and enables consistency checking

Growing a Community

Future Work• Backends for BioStream

– Generate graphical protocol– Generate a microfluidic chip to perform protocol

• Reliability and troubleshooting– Verify that protocol is safe, correct, obeys timing constraints– If protocol fails, automatically suggest troubleshooting routine

• Building a knowledge base– Encode experimental results in addition to protocols– Search for a protocol based on input/output relationship– Revision control for biology protocols

Conclusions• Abstraction layers for

programmable microfluidics– Microfluidic CAD tools– General-purpose chips– Fluidic ISA– BioCoder language

• Vision for microfluidics:everyone uses standard chip

• Vision for software:a defacto language for experimental science– Download a colleague’s code, run it on your chip– Compose modules and libraries to enable complex experiments

that are impossible to perform today

microfluidic biochips: design, programming, and optimization bill thies joint work with vaishnavi...

Documents

chip slide

chip design

realtime slide

latches chip

control layer flow layer

silicon wafer slide

duke university slide

valve quake