Computing with Life:Digitally Programmed Cells

Tom Knight

MIT Artificial Intelligence Laboratory

1 micron---------------------------

A New Engineering Discipline• Living Systems are Special

– They self replicate

– They are self powered from common chemicals

– small

– They clean up after themselves

• They provide an interface to the chemical world– precision chemical construction

– antibody sensors and catalytic effectors

• They Compute– But we need to engineer and control their computation

– We need to standardize their I/O interfaces

• They cooperate to fabricate complex structure– Information Rich Materials

Implementing the Digital Abstraction

• In-vivo digital circuits:– signal = concentration of specific protein

– computation = regulated protein synthesis + decay

• The basic computational element is an inverter– Logical operation

– More importantly signal restoration

• The output must be better than the input– low gain in the high and low states– high gain in intermediate states– nonlinear transfer curve

Biochemical Inverters

Digital Circuits• Combining properly designed inverters, any

digital circuit can be built

A

B

C D

C

CA

B

D= gene

gene

gene

• proteins are the wires, genes are the gates• NAND gate = “wire-OR” of two genes

“Proof of Concept” Circuits• They work in simulation

• They work in vivo

• Models poorly track their behavior

time (x100 sec)

[A]

[C]

[B]

B_S

_R

A

_[R]

[B]

_[S]

[A]

time (x100 sec)

time (x100 sec)

RS-Latch (“flip-flop”) Ring oscillator

Gardner & Collins 2000 Elowitz & Leibler 2000

Bio Circuit Design (“TTL Data Book”)

• Data sheets for components– imitate existing silicon logic gates– new primitives from cellular regulatory elements

• e.g. an inverter that can be “induced”

• Assembling a large library of components– modifications that yield desired behaviors

• Constructing complex circuits– matching gates is hard– need standard interfaces for parts

from black magic to “you can do it too”

Biological InverterTransfer Curves

• Dramatic differences– Lambda cI vs. lacI

• Difficult measurements

• Single cell expression necessary

• Cell type and cell state has a huge effect

• Dual labelling with CFP / GFP / YFP

• Interchange approach to measurement

• Techniques out of VLSI design– dual rail signalling– self biased circuits

• Engineered binding sites and proteins

Naturally Occurring Sensor and Actuator Parts Catalog

Sensors• Light (various wavelengths)• Magnetic and electric fields• pH• Molecules

– Autoinducers

– H2S

– maltose / arabinose / lactose

– serine

– ribose

– cAMP

– NO

• Internal State– Cell Cycle

– Heat Shock

• Chemical and ionic membrane potentials

Actuators• Motors

Flagellar Gliding motion

• Light (various wavelengths)• Fluorescence• Autoinducers (intercellular

communications)• Sporulation• Cell Cycle control• Membrane transport• Exported protein product

(enzymes)• Exported small molecules• Cell pressure / osmolarity• Cell death

Engineering the Lux Operon• Starting point:

– Vibrio fischeri strain MJ-1• infects the light organ of Monocentris japonicus “Japanese Pinecone

fish”• Also free ocean swimming• Dark when free swimming• Emits light when in the light organ

– Light production cascade

– Autoinducers• small communications molecules (homo-serine lactones)• both emitters and detectors

Cloning the lux Operon into E. coli

pTK111334 bp

AP r

LuxI

LuxC

LuxD

LuxB

LuxE

LuxG

LuxA

LuxR

ORF-V

lux P(L) transcription start

lux P(R) transcription start

ColE1 ORI

• First, we shotgun cloned the lux Operon from Vibrio fischeri to form plasmid pTK1

• Expressed in E. coli DH5α showed bioluminescence• Sequenced the operon [Genbank entry AF170104]

Split -- engineer componentsand interfaces

• Autoinducer fabrication (LuxI)

• Autoinducer response (Pr promoter)

• Luciferase genes (Lux A, B)– Better ones from Xenorhabdus luminescens

• Aldehyde production (Lux C, D, E, G)

• Bidirectional terminator

Experiment III: Controlled Sender

pLuxI-Tet-8

VAI

LuxR GFP

pRCV-3

Fragment of pRCV-32038 bp (molecule 4149 bp)

GFP(LVA)

LuxR lux P(L)

lux P(R)

rrnB T1 rrnB T1

• Genetic networks for controlled sender & receiver:

• Logic circuit diagrams for controlled sender & receiver:

VAI VAI

Fragment of pLuxI-Tet-81052 bp (molecule 2801 bp)

LuxIP(LtetO-1) T1

aTcaTc

TetR VAI

* E. coli strain expresses TetR (not shown)

*

Experiment III: Controlling Sender• Figure shows ability to induce stronger signals with aTc

– Non-induced sender (pLux8-Tet-8) & receiver cells grown seperately @37°C to late log phase

– Cells were combined in FL600, and sender cells were induced with aTc– Data shows max fluorescence after 4 hours @37 °C for 5 separate cultures

plus control [positive cultures have same DNA variance due to OD]

Controlled Cell to Cell Signaling

0

50,000

100,000

aTc concentration ng / ml

Rel

ativ

e R

ecei

ver

Flu

ore

scen

ce

LuxTet4B9LuxTet4B8LuxTet4B7LuxTet8D4LuxTet4D3RCV Only

positive

control

negative

control

Engineered Minimal Organisms• simple

• understood

• malleable

• controlled

• Start with a simple existing organism

• Remove structure until failure

• Rationalize the infrastructure

• Learn new biology along the wayThe chassis and power supply for our computing

Hutchison experiment• Randomly insert transposons into locations in the

Mycoplasma genitalium genome

• Select for cells with an insert

• Cells which grow survived the destruction of the interrupted gene

• Sequence from the transposon, report which genes were not needed

• Only 293 out of 453 genes were essential

Nature, Dec. 1999

Plan• Locate a non pathogenic simple cell– plant and insect mycoplasma species

• Mesoplasma florum, M. lactucae, M. entomophilum

• Sequence• Develop plasmids • Eliminate restriction systems• Locate and remove unnecessary genes• Rationalize promoters, codon usage• Reclaim amino acid coding space• Understand the cell metabolism and control• Add debugging and control hooks

Recent progress• Chose Mesoplasma florum

• Easily grown -- saturation in 36 hours

• Sequenced 3% to know what we are up against

• Genome is approximately 870 Kb

• We are developing tools to examine protein expression

• We have discovered that two supposedly distinct species are essentially identical– exact identity of 16S ribosomal RNA sequence– near exact identity of protein expression patterns

• Engineering replicative plasmids

Mesoplasma florum

PFGE of Mesoplasmagenomic DNA

1.2% agarose 9C 6V/cm Ramped 90 - 120 sec 48 hours

yyeast marker

lambda marker

meMesoplasma entomophilum

mfMesoplasma florum

mlMesoplasma lactucae

Mesoplasma SDS-PAGEMEMesoplasmaentomophilum

MFMesoplasmaflorum

MLMesoplasmalactucae

whole cell10% Tris-HCl gel

I think in order to get to a petaflop we have to somehow reduce the size of our components from the micron size to the nanometer size. But there are lots of interesting things happening at the nanometer scale. During the past year I have read articles which make my jaw drop. They aren’t from our community. They are from the molecular biology community, and I can imagine two ways of riding on the coattails of a much bigger revolution than we have.

One way to do that is to make computing devices out of biological elements -- but I’m not comfortable about that because I am one and I feel threatened.

We can also use biological tools to manufacture non-biological devices: to manufacture the things that are more familiar to us and are more stable, in the sense that we understand them better…

Seymour Cray, January 1994, Petaflops workshop

Molecular Electronics• Semiconductor technology ends < .05 microns– Statistical placement of atoms– poor matching between devices

• Alternative is precise atomic placement and identical devices

• Path to continued performance enhancement of electronics and extension of Moore’s Law

• Every reason to believe that this technology will be as important in the next century as silicon is today.

Molecular Fabrication• Nanoscale molecular electronics requires control over highly structured information-rich placement of individual atoms

• Biochemistry is our best hope of achieving this control• We can isolate structure from function

– an information rich substrate, biochemically computed• Collagen?

– a binding mechanism (antibodies?)– high performance molecular devices

• carbon nanotubes

• conjugated polymers (Carotenes)