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Computing with Life: Digitally Programmed Cells Tom Knight MIT Artificial Intelligence Laboratory.

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Presentation on theme: "Computing with Life: Digitally Programmed Cells Tom Knight MIT Artificial Intelligence Laboratory."— Presentation transcript:

1 Computing with Life: Digitally Programmed Cells Tom Knight MIT Artificial Intelligence Laboratory

2 1 micron ---------------------------

3 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

4 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

5 Biochemical Inverters

6 Digital Circuits Combining properly designed inverters, any digital circuit can be built A B CD C C A B D = gene proteins are the wires, genes are the gates NAND gate = wire-OR of two genes

7 Proof of Concept Circuits They work in simulation They work in vivo Models poorly track their behavior time (x100 sec) [A][A] [C][C] [B][B] B _S_S _R_R A _[R]_[R] [B][B] _[S]_[S] [A][A] RS-Latch (flip-flop)Ring oscillator Gardner & Collins 2000Elowitz & Leibler 2000

8 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 q from black magic to you can do it too

9 Biological Inverter Transfer 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

10 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

11 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

12 Cloning the lux Operon into E. coli 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]

13 Split -- engineer components and 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

14 Experiment III: Controlled Sender pLuxI-Tet-8 VAI LuxRGFP pRCV-3 Genetic networks for controlled sender & receiver: Logic circuit diagrams for controlled sender & receiver: VAI aTc TetRVAI * E. coli strain expresses TetR (not shown) *

15 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] positive control negative control

16 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 way The chassis and power supply for our computing

17 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

18 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

19 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

20 Mesoplasma florum

21 PFGE of Mesoplasma genomic DNA 1.2% agarose 9C 6V/cm Ramped 90 - 120 sec 48 hours y yeast marker lambda marker me Mesoplasma entomophilum mf Mesoplasma florum ml Mesoplasma lactucae

22 Mesoplasma SDS-PAGE ME Mesoplasma entomophilum MF Mesoplasma florum ML Mesoplasma lactucae whole cell 10% Tris-HCl gel

23 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 arent 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 Im 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

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

25 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)

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