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Design of Digital Logic by Genetic Regulatory Networks

Design of Digital Logic by Genetic Regulatory Networks. Ron Weiss Department of Electrical Engineering Princeton University. Computing Beyond Silicon Summer School, Caltech, Summer 2002. Programming Cell Communities. Diffusing signal. E. coli. proteins.

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Design of Digital Logic by Genetic Regulatory Networks

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  1. Design of Digital Logic by Genetic Regulatory Networks Ron Weiss Department of Electrical Engineering Princeton University Computing Beyond Silicon Summer School, Caltech, Summer 2002

  2. Programming Cell Communities Diffusing signal E. coli proteins Program cells to perform various tasks using: • Intra-cellular circuits • Digital & analog components • Inter-cellular communication • Control outgoing signals, process incoming signals

  3. Programmed Cell Applications • Biomedical • combinatorial gene regulation with few inputs; tissue engineering • Environmental sensing and effecting • recognize and respond to complex environmental conditions • Engineered crops • toggle switches control expression of growth hormones, pesticides • Cellular-scale fabrication • cellular robots that manufacture complex scaffolds analyte source Reporter rings Pattern formation Analyte source detection

  4. Outline • In-vivo logic circuits • Intercellular communications • Signal processing and analog circuits • Programming cell aggregates

  5. A Genetic Circuit Building Block • Digital Inverter • Threshold Detector • Amplifier • Delay

  6. 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!

  7. 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] • Also working on combining analog &digital circuitry

  8. 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 • single cells, small cell aggregates

  9. Genetic Circuit Elements translation RBS RBS input mRNA output mRNA ribosome ribosome transcription operator promoter RNAp

  10. Modeling a Biochemical Inverter input repressor promoter output

  11. A BioSPICE Inverter Simulation input repressor promoter output

  12. “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] • However, cells are very complex environments • Current modeling techniques poorly predict 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)

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

  14. The Toggle Switch[Gardner & Collins, 2000] pIKE = lac/tet pTAK = lac/cIts

  15. Actual Behavior of Toggle Switch[Gardner & Collins, 2000] promoter protein coding sequence

  16. The Ring Oscillator[Elowitz, Leibler 2000]

  17. Example of Oscillation

  18. Evaluation of the Ring Oscillator • [Elowitz & Leibler, 2000] Reliable long-term oscillation doesn’t work yet: • Will matching gates help? • Need to better understand noise • Need better models for circuit design

  19. A Ring Oscillator with Mismatched Inverters A = original cI/λP(R) B = repressor binding 3X weaker C = transcription 2X stronger

  20. Device Physics in Steady State “Ideal” inverter • Transfer curve: • gain (flat,steep,flat) • adequate noise margins “gain” [output] 0 1 [input] • Curve can be achieved with certain dna-binding proteins • Inverters with these properties can be used to build complex circuits

  21. 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

  22. Flow Cytometry (FACS)

  23. Drive Input Levels by Varying Inducer promoter protein coding sequence IPTG (uM) lacI [high] YFP 0 (Off) P(lacIq) P(lac) 0 IPTG 100 P(lacIq) lacI IPTG P(lac) YFP 1000

  24. Controlling Input Levels Also use for CFP/YFP calibration

  25. Cell Population Behavior Red = pPROLAR Rest = pINV-102 with IPTG (0.1 to 1000 uM)

  26. CFP: a Weak Fluorescent Protein IPTG Fluorescence Induction of CFP expression

  27. Measuring a Transfer Curve for lacI/p(lac) tetR [high] lacI CFP 0 (Off) YFP P(R) P(lac) P(LtetO-1) aTc measure TC P(R) tetR P(lac) YFP aTc P(Ltet-O1) lacI CFP

  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. The Cellular Gate LibraryAdd the 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) P(lacIq) lacI P(R) YFP IPTG P(lac) cI CFP

  34. Recall Inverter Components translation RBS RBS input mRNA output mRNA ribosome ribosome transcription operator promoter RNAp

  35. Functional Composition of an Inverter digital inversion scale input “clean” signal invert signal cooperative binding translation transcription inversion “gain” + + = yZ rA yZ fA 0 1 0 1 0 1 0 1 yA fA rA yA + + = yA = input mRNA fA = input protein rA = bound operators yZ = output mRNA

  36. Genetic Process Engineering I:Reducing Ribosome Binding Site Efficiency translation start RBS Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak translation stage fA yA Inversion yZ yA

  37. Experimental Results forcI/P(R) Inverter with Modified RBS

  38. Genetic Process Engineering II:Mutating the P(R) operator orig: TACCTCTGGCGGTGATA mut4: TACATCTGGCGGTGATA mut5: TACATATGGCGGTGATA mut6 TACAGATGGCGGTGATA OR1 cooperative binding rA fA yZ yA BioSPICE Simulation

  39. Experimental Results for Mutating P(R)

  40. Genetic Process Engineering mutate operator modify RBS • Genetic modifications required to make circuit work • Need to understand “device physics” of gates • enables construction of complex circuits #2: mutate operator RBS #1: modify RBS

  41. Self-perfecting Genetic Circuits[Arnold, Yokobayashi, Weiss] • Use directed evolution to optimize circuits • Screening criteria based on transfer curve • Initial results are promising optical micrograph of the μFACS device Lab-on-a-chip: μFACS [Quake]

  42. Molecular Evolution of the Circuit

  43. Prediction of Circuit Behavior Input signal Output signal Can the behavior of a complex circuit be predicted using onlythe behavior of its parts? ? =

  44. Prediction of Circuit Behavior preliminary results P(bla) tetR P(lac) cI aTc P(tet) lacI CFP P(R) YFP aTc # cells YFP

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