Design of Digital Logic by Genetic Regulatory Networks. Programming Cell Communities

Transcription

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 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 1

2 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 Outline In-vivo logic circuits Intercellular communications Signal processing and analog circuits Programming cell aggregates 2

3 A Genetic Circuit Building Block Digital Inverter Threshold Detector Amplifier Delay Logic Circuits based on Inverters X R 1 X Y R 1 Z = gene Y R 1 gene Z 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! 3

4 Why Digital? We know how to program with it Signal restoration + modularity = robust complex circuits Cells do it Phage? ci repressor: Lysis or Lysogeny? [Ptashne, A Genetic Switch, 1992] Circuit simulation of phage? [McAdams & Shapiro, Science, 1995] Also working on combining analog & digital circuitry BioCircuit Computer-Aided Design steady state dynamics intercellular SPICE BioSPICE BioSPICE: a prototype biocircuit CAD tool simulates protein and chemical concentrations intracellular circuits, intercellular communication single cells, small cell aggregates 4

5 Genetic Circuit Elements translation RBS RBS input mrna ribosome output mrna ribosome operator transcription promoter RNAp Modeling a Biochemical Inverter input repressor promoter output 5

6 A BioSPICE Inverter Simulation input repressor promoter output Proof of Concept Circuits Work in BioSPICEsimulations [Weiss, Homsy, Nagpal, 1998] RS-Latch ( flip-flop ) Ring oscillator time (x100 sec) _ [R] _ [S] _ R A [A] [B] [B] _ S B [C] [A] time (x100 sec) time (x100 sec) 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 6

7 The IMPLIES Gate RNA P active repressor inactive repressor no transcription inducer transcription RNA P promoter operator gene promoter operator gene 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 Inducer Output Repressor Inducer Output The Toggle Switch [Gardner & Collins, 2000] pike = lac/tet ptak = lac/cits 7

8 Actual Behavior of Toggle Switch [Gardner & Collins, 2000] promoter protein coding sequence The Ring Oscillator [Elowitz, Leibler 2000] 8

9 Example of Oscillation 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 9

10 A Ring Oscillator with Mismatched Inverters A = original ci/? P(R) B = repressor binding 3X weaker C = transcription 2X stronger Device Physics in Steady State Ideal inverter [output] gain Transfer curve: gain (flat,steep,flat) adequate noise margins [input] 0 1 Curve can be achieved with certain dna-binding proteins Inverters with these properties can be used to build complex circuits 10

11 Measuring a Transfer Curve Construct a circuit that allows: Control and observation of input protein levels Simultaneous observation of resulting output levels CFP R inverter YFP drive gene output gene Also, need to normalize CFP vs YFP Flow Cytometry (FACS) 11

12 Drive Input Levels by Varying Inducer IPTG (um) 0 (Off) P(lacIq) laci [high] IPTG P(lac) YFP 0 P(lacIq) laci IPTG 100 P(lac) YFP 1000 promoter protein coding sequence Controlling Input Levels 1, FL1 pinv-112-r1 pinv , ,000.0 IPTG (um) Also use for CFP/YFP calibration 12

13 Cell Population Behavior Red = pprolar Rest = pinv-102 with IPTG (0.1 to 1000 um) CFP: a Weak Fluorescent Protein IPTG Induction of CFP expression Fluorescence 13

14 Measuring a Transfer Curve for laci/p(lac) 0 tetr [high] λ P(R) (Off) P(LtetO -1) atc laci CFP P(lac) measure TC YFP λ P(R) tetr atc P(lac) YFP P(Ltet-O1) laci CFP Transfer Curve Data Points 0 1 undefined 1 0 1,400 1,400 1,400 1,200 1,200 1,200 1,000 1,000 1,000 Events Events Events ,000 10, ,000 10, ,000 10,000 Fluorescence (FL1) Fluorescence (FL1) Fluorescence (FL1) 1 ng/ml atc 10 ng/ml atc 100 ng/ml atc 14

15 laci/p(lac) Transfer Curve 0 tetr [high] λ P(R) (Off) P(LtetO -1) atc laci CFP P(lac) YFP 1000 Output (YFP) gain = Input (Normalized CFP) Evaluating the Transfer Curve Gain / Signal restoration: Noise margins: 1,000 1,400 1,200 Fluorescence high gain Events 1, atc (ng/ml) ng/ml atc ,000 Fluorescence 3 ng/ml atc * note: graphing vs. atc (i.e. transfer curve of 2 gates) 15

16 Transfer Curve of Implies tetr [high] atc laci IPTG YFP 10 3 Median FLR atc (ng/ml) IPTG (mm) The Cellular Gate Library Add the ci/λ P(R) Inverter ci is a highly efficient repressor cooperative binding high gain O R 2 O R 1 structural gene λ P(R-O12) Use laci/p(lac) as driver ci bound to DNA 0 (Off) laci [high] P(lac) ci CFP λ P(R) YFP IPTG 16

17 Initial Transfer Curve for ci/λ P(R) 1, Output (YFP) ,000.0 IPTG (um) P(lacIq) laci IPTG λ P(R) YFP P(lac) ci CFP Recall Inverter Components translation RBS RBS input mrna ribosome output mrna ribosome operator transcription promoter RNAp 17

18 Functional Composition of an Inverter translation cooperative binding transcription inversion φ Α + + = ψ Ζ ρ Α ψ Ζ gain 0 1 ψ Α 0 1 φ Α 0 1 ρ Α 0 1 ψ Α scale input + clean signal + invert signal = digital inversion ψ Α = input mrna φ Α = input protein ρ Α = bound operators ψ Ζ = output mrna Genetic Process Engineering I: Reducing Ribosome Binding Site Efficiency translation stage φ Α translation start ψ Α RBS Inversion Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak ψ Ζ ψ Α 18

19 Experimental Results for ci/λ P(R) Inverter with Modified RBS 1, Output (YFP) pinv-107/pinv-112-r1 pinv-107/pinv-112-r2 pinv-107/pinv-112-r ,000.0 IPTG (um) Genetic Process Engineering II: Mutating the λ P(R) operator cooperative binding ρ Α φ Α orig: mut4: mut5: mut6 TACCTCTGGCGGTGATA TACATCTGGCGGTGATA TACATATGGCGGTGATA TACAGATGGCGGTGATA O R 1 ψ Ζ ψ Α BioSPICESimulation 19

20 Experimental Results for Mutating λ P(R) 1, Output (YFP) pinv-107-mut4/pinv-112-r3 pinv-107-mut5/pinv-112-r3 pinv-107-mut6/pinv-112-r ,000.0 IPTG (um) Genetic Process Engineering 1, Output (YFP) modify RBS mutate operator #2: mutate operator ,000.0 IPTG (um) RBS #1: modify RBS Genetic modifications required to make circuit work Need to understand device physics of gates enables construction of complex circuits 20

21 Self-perfecting Genetic Circuits [Arnold, Yokobayashi, Weiss] Use directed evolution to optimize circuits Screening criteria based on transfer curve Initial results are promising Lab-on-a-chip: µfacs [Quake] optical micrograph of the µfacs device Molecular Evolution of the Circuit 21

22 Prediction of Circuit Behavior Can the behavior of a complex circuit be predicted using only the behavior of its parts? Input signal Output signal 1, , ? = 1, Prediction of Circuit Behavior preliminary results P(bla) tetr atc P(lac) ci P(tet) laci CFP λ P(R) YFP # cells atc YFP 22