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. E. coli Diffusing signal Programming Cell Communities 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 analyte source Analyte source detection Reporter rings Pattern formation 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

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 Proteins are the wires/signals Promoter + decay implement the gates NAND gate is a universal logic element: –any (finite) digital circuit can be built! X Y R1R1 Z R1R1 R1R1 X Y Z = gene NANDNOT

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 SPICE BioSPICE steady statedynamics intercellular BioSPICE: a prototype biocircuit CAD tool – simulates protein and chemical concentrations – intracellular circuits, intercellular communication – single cells, small cell aggregates

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

10. Modeling a Biochemical Inverter input output repressor promoter

11. A BioSPICE Inverter Simulation input output repressor promoter

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 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

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

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 Reliable long-term oscillation doesn’t work yet:  Will matching gates help?  Need to better understand noise  Need better models for circuit design [Elowitz & Leibler, 2000]

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

20. Device Physics in Steady State Transfer curve:  gain (flat,steep,flat)  adequate noise margins [input] “gain” 01 [output] Curve can be achieved with certain dna-binding proteins Inverters with these properties can be used to build complex circuits “Ideal” inverter

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

22. Flow Cytometry (FACS)

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

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

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

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

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

28. Transfer Curve Data Points 01011010 1 ng/ml aTc undefined 10 ng/ml aTc100 ng/ml aTc

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

32. The Cellular Gate Library Add the cI/ P(R) Inverter OR1OR1OR2OR2 structural gene P(R-O12) cI is a highly efficient repressor cooperative binding IPTG YFP cI CFP lacI [high] 0 (Off) P(R) P(lac) Use lacI/p(lac) as driver high gain cI bound to DNA

33. Initial Transfer Curve for cI/ P(R) lacI P(lacIq) P(lac) IPTG YFP P(R) cICFP

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

35. Functional Composition of an Inverter “clean” signaldigital inversionscale inputinvert signal   translation   01 01   01 ++=   “gain” 01 ++= cooperative binding transcriptioninversion    input mRNA     input protein     bound operators    output mRNA

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

37. Experimental Results for cI/ P(R) Inverter with Modified RBS

38. Genetic Process Engineering II: Mutating the P(R) operator BioSPICE Simulation orig: TACCTCTGGCGGTGATA mut4: TACATCTGGCGGTGATA mut5: TACATATGGCGGTGATA mut6 TACAGATGGCGGTGATA OR1OR1   cooperative binding  

39. Experimental Results for Mutating P(R)

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

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

42. Molecular Evolution of the Circuit

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

44. Prediction of Circuit Behavior preliminary results YFP aTc # cells tetR P(bla) P(tet) aTc cIP(lac) lacICFP YFP P(R)