Cellular Computation and Communications using Engineered Genetic Regulatory Networks

1. Cellular Computation and Communicationsusing Engineered Genetic Regulatory Networks Ron Weiss Advisors: Thomas F. Knight, Gerald Jay Sussman, Harold Abelson Artificial Intelligence Laboratory, MIT

2. Cellular Robotics A C C A D D gene B B C gene gene NAND NOT Environment Biochemical Logic circuit actuators sensors =

3. Vision • A new substrate for engineering: living cells • interface to the chemical world • cell as a factory / robot • Logic circuit = process description • extend/modify behavior of cells • Challenge: engineer complex, predictable behavior

4. Applications • “Real time” cellular debugger • detect conditions that satisfy logic statements • maintain history of cellular events • Engineered crops / farm animals • toggle switches control expression of growth hormones, pesticides • Biomedical • combinatorial gene regulation with few inputs • sense & recognize complex environmental conditions • Molecular-scale fabrication • cellular robots that manufacture complex scaffolds

5. “Programming” Cells plasmid = “user program”

6. Biochemical Inverters • signal = concentration of specific proteins • computation = regulated protein synthesis + decay

7. Engineering Challenges • Map logic circuits to biochemical reactions • Circuit design and implementation: • conventional interfaces • sensitivities to chemical concentrations • understand affinities of molecules to each other • process engineering to adjust trigger levels, gains • CAD tools (BioSpice)

8. Contributions • Experimental results: • Built and characterized a small library of logic gates • 4 different dna-binding proteins (lacI, tetR, cI, luxR) • 12 modifications of gates based on cI protein • transfer functions (input/output relationship) • Built and tested several logic circuits • combined 3 gates based on transfer functions • Engineered communication between cells • chemical diffusions carry message • CAD tools and program design: • BioSpice (circuit design/verification) • Microbial Colony Language

9. Outline • A Model for Programming Biological Substrates • Example: Pattern formation • Microbial Colony language • In-vivo digital circuits • Cellular gates: Inverter, Implies • BioSpice circuit simulations & design • Measuring and modifying “device physics” • Intercellular communications • Additional gate: AND • BioSpice simulations & design • Measuring “device physics”

10. Programming Biological Substrates • Constraints/Characteristics: • Simple, unreliable elements • Local, unreliable communication • Elements engineered to perform tasks • Example task: form cellular-scale patterns

11. Another Example: Differentiation Cells differentiate into bands of alternating C and D type segments.

12. Microbial Colony Language (MCL) A program for creating segments: message condition actions (start Crest ((send (make-seg C 1) 3))) ((make-seg seg-type seg-index) (and Tube (not C) (not D)) ((set seg-type) (set seg-index) (send created 3))) (((make-seg) (= 0)) Tube ((set Bottom))) (((make-seg) (> 0)) Tube ((unset Bottom))) (created (or C D) ((set Waiting 10))) (* (and Bottom C 1 (Waiting (= 0))) ((send (make-seg D 1) 3))) (* (and Bottom D 1 (Waiting (= 0))) ((send (make-seg C 2) 3))) (* (and Bottom C 2 (Waiting (= 0))) ((send (make-seg D 2) 3))) (* (and Bottom D 2 (Waiting (= 0))) ((send (make-seg C 3) 3)))

13. How can we accomplish this? • Boolean state variables • DNA binding proteins • Biochemical logic circuits • genetic regulatory networks • Intercellular signaling chemicals • enzymes that make small molecules biocompiler: MCL  genetic circuits

14. Outline • Programming Biological Substrates • Pattern Formation • Microbial Colony language • In-vivo digital circuits • Cellular gates: Inverter, Implies • BioSpice circuit simulations & design • Measuring and modifying “device physics” • Intercellular communications • Additional gate: AND • BioSpice simulations & design • Measuring “device physics”

15. Why Digital? • We know how to program with it • Signal restoration + modularity = robust complex circuits • Cells do it • Phage λ cI repressor: Lysis or Lysogeny?[Ptashne, A Genetic Switch, 1992] • Circuit simulation of phage λ[McAdams & Shapiro, Science, 1995] • Ultimately, combine analog &digital circuitry

16. Logic Circuits based on Inverters X R1 = X R1 Z Z gene Y R1 Y gene NAND NOT gene • Proteins are the wires/signals • Promoter + decay implement the gates • NAND gate is a universal logic element: • any (finite) digital circuit can be built!

17. Examples of Useful Circuits • Logic statements: • (x AND y AND z) OR (NOT u) • Decoders: • Turn ON 1 of 8 genes using only 3 inputs • Counters • Memory, Toggle switches • Clocks

18. BioCircuit Computer-Aided Design intercellular steady state dynamics SPICE BioSPICE • BioSpice: a prototype biocircuit CAD tool • simulates protein and chemical concentrations • intracellular circuits • intercellular communication

19. “Proof of Concept” Circuits • Work in BioSpice simulations [Weiss, Homsy, Nagpal, 1998] • They work in vivo • Flip-flop [Gardner & Collins, 2000], Ring oscillator[Elowitz & Leibler, 2000] • Models poorly predict their behavior RS-Latch (“flip-flop”) Ring oscillator _ [R] [A] _ R _ [S] A [B] time (x100 sec) [B] _ S B [C] [A] time (x100 sec) time (x100 sec)

20. Evaluation of the Ring Oscillator • [Elowitz & Leibler, 2000] Reliable long-term oscillation doesn’t work yet • Need to match gates

21. Measuring & Modifying “Device Physics” • Why? • Different elements have widely varying characteristics • Need to be matched • Assembled and characterized a library of components • Constructed and measured gates using 4 genetic candidates • lac, tet, cI, lux • Created 12 variations of cI in order to match with lac: • modified repressor/operator affinity • modified RBS efficiency • other mechanisms: protein decay, promoter strength, etc.. • Established component evaluation criteria • Initially, focused on steady state behavior

22. Steady-State Behavior: Inverter • “ideal” transfer curve: • gain (flat,steep,flat) • adequate noise margins “gain” [output] 0 1 [input] This curve can be achieved using proteins that cooperatively bind dna!

23. Measuring a Transfer Curve “drive” gene • Construct a circuit that allows: • Control and observation of input protein levels • Simultaneous observation of resulting output levels inverter CFP YFP R output gene • Also, need to normalize CFP vs YFP

24. Repressors & Inducers active repressor inactive repressor RNAP inducer transcription no transcription RNAP gene gene promoter promoter operator operator • Inducers that inactivate repressors: • IPTG (Isopropylthio-ß-galactoside)  Lac repressor • aTc (Anhydrotetracycline)  Tet repressor • Use as a logical Implies gate: (NOT R) OR I Repressor Output Inducer

25. Drive Input Levels by Varying Inducer plasmid IPTG (or ECFP) IPTG (uM) lacI [high] YFP 0 (Off) P(R) P(LtetO-1) IPTG 0 250 1000 promoter protein coding sequence

26. Controlling Input Levels Also use for yfp/cfp calibration

27. Measuring a Transfer Curve for lacI/p(lac) aTc tetR [high] lacI CFP 0 (Off) YFP P(R) P(lac) P(LtetO-1) aTc measure TC “drive” output

28. Transfer Curve Data Points 01 10 undefined 1 ng/ml aTc 10 ng/ml aTc 100 ng/ml aTc

29. lacI/p(lac) Transfer Curve tetR [high] lacI CFP 0 (Off) YFP P(R) P(lac) P(LtetO-1) aTc gain = 4.72

30. Evaluating the Transfer Curve • Noise margins: • Gain / Signal restoration: high gain • note: graphing vs. aTc (i.e. transfer curve of 2 gates)

31. Transfer Curve of Implies tetR [high] lacI YFP aTc IPTG

32. Measure cI/P(R)Inverter • cI is a highly efficient repressor cooperative binding high gain OR2 OR1 structural gene P(R-O12) cI bound to DNA • Use lacI/p(lac) as driver lacI [high] cI CFP 0 (Off) YFP P(R) P(lac) IPTG

33. Initial Transfer Curve for cI/P(R) • Completely flat • Reducing IPTG  no additional fluorescence • Hard to debug! • Process engineering: • Is there a mismatch between inverters based on lacI/p(lac) and cI/P(R)?

34. Inverters Rely onTranscription & Translation translation mRNA mRNA ribosome ribosome transcription operator RNAp promoter

35. Process Engineering I:Different Ribosome Binding Sites translation start RBS Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak BioSpice Simulations

36. Experimental Results forModified Inverter

37. Process Engineering II:Mutating the P(R) orig: TACCTCTGGCGGTGATA mut4: TACATCTGGCGGTGATA mut5: TACATATGGCGGTGATA mut6 TACAGATGGCGGTGATA BioSpice Simulations OR1

38. Experimental Results for Mutating P(R)

39. Lessons for BioCircuit Design • Naive coupling of gates not likely to work • Need to understand “device physics” • enables construction of complex circuits • Use process engineering • modify gate characteristics

40. Outline • Programming Biological Substrates • Pattern Formation • Microbial Colony language • In-vivo digital circuits • Cellular gates: Inverter, Implies • BioSpice circuit simulations & design • Measuring and modifying “device physics” • Intercellular communications • Additional gate: AND • BioSpice simulations & design • Measuring “device physics”

41. Intercellular Communications • Certain inducers useful for communications: • A cell produces inducer • Inducer diffuses outside the cell • Inducer enters another cell • Inducer interacts with repressor/activator  change signal main metabolism (1) (2) (3) (4)

42. Activators & Inducers • Inducers can activate activators: • VAI (3-N-oxohexanoyl-L-Homoserine lacton)  luxR • Use as a logical AND gate: inactive activator active activator RNAP inducer transcription no transcription RNAP gene gene promoter promoter operator operator Activator Output Inducer

43. BioSpice: Intercellular Communications • Small simulation: • 4x4 grid • 2 cells (outlined) (1) original I = 0 chemical concentration (2) introduce D send msg M (3) recv msg set I (4) msg decays I latched

44. Eupryma scolopes Light organ

45. Quorum Sensing (Light) hv Luciferase LuxR LuxI P luxR luxI luxC luxD luxA luxB luxE luxG P Structural Genes Regulatory Genes • Cell density dependent gene expression Example: Vibrio fischeri [density dependent bioluminscence] The lux Operon LuxI metabolism  autoinducer (VAI)

46. Density Dependent Bioluminescence O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O N H N H N H N H N H N H N H N H N H N H N H N H N H N H N H N H O O O O O O O O O O O O O O O O Low Cell Density High Cell Density LuxR LuxR (Light) hv Luciferase LuxR LuxR LuxI LuxI (+) P P luxR luxI luxC luxD luxA luxB luxE luxG luxR luxI luxC luxD luxA luxB luxE luxG P P free living, 10 cells/liter <0.8 photons/second/cell symbiotic, 1010 cells/liter 800 photons/second/cell • A positive feedback circuit

47. Similar Signalling Systems Species Relation to Host Regulate Production of I Gene R Gene Vibrio fischeri marine symbiont Bioluminescence luxI luxR Vibrio harveyi marine symbiont Bioluminescence luxL,M luxN,P,Q Pseudomonas aeruginosa Human pathogen Virulence factors lasI lasR Rhamnolipids rhlI rhlR Yersinia enterocolitica Human pathogen ? yenI yenR Chromobacterium violaceum Human pathogen Violaceum production Hemolysin Exoprotease cviI cviR Enterobacter agglomerans Human pathogen ? eagI ? Agrobacterium tumefaciens Plant pathogen Ti plasmid conjugation traI traR Erwinia caratovora Plant pathogen Virulence factors Carbapenem production expI expR Erwinia stewartii Plant pathogen Extracellular Capsule esaI esaR Rhizobium leguminosarum Plant symbiont Rhizome interactions rhiI rhiR Pseudomonas aureofaciens Plant beneficial Phenazine production phzI phzR N-acyl-L-Homoserine Lactone Autoinducers in Bacteria

48. Circuits for Controlled Sender & Receiver • Genetic networks: VAI VAI * * E. coli strain expresses TetR (not shown) • Logic circuits: LuxR 0 GFP tetR 0 luxI VAI VAI aTc aTc pLuxI-Tet-8 pRCV-3

49. Experimental Setup insert • BIO-TEK FL600 Microplate Fluorescence Reader • Costar Transwell microplates and cell culture inserts with permeable membrane (0.1μm pores) • Cells separated by function: • Sender cells in the bottom well • Receiver cells in the top well

50. Time-Series Response to Signal Fluorescence response of receiver (pRCV-3) positive control 10X VAI extract direct signalling negative controls