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
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
A Genetic Circuit Building Block � Digital Inverter � Amplifier � Threshold Detector � Delay Logic Circuits based on Inverters X R 1 X = R 1 Z Z gene Y Y R 1 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! 3
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 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 4
Genetic Circuit Elements translation RBS RBS input output ribosome ribosome mRNA mRNA transcription operator promoter RNAp Modeling a Biochemical Inverter input repressor promoter output 5
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 _ [R] [A] _ R _ A [S] [B] time (x100 sec) [ B ] _ B [ C ] S [ 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
The IMPLIES Gate active inactive repressor repressor RNA P inducer transcription no transcription RNA P gene gene promoter operator promoter 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 Inducer Output 0 0 1 Repressor Output 0 1 1 Inducer 1 0 0 1 1 1 The Toggle Switch [Gardner & Collins, 2000] pIKE = lac / tet pTAK = lac / cIts 7
Actual Behavior of Toggle Switch [Gardner & Collins, 2000] promoter protein coding sequence The Ring Oscillator [Elowitz, Leibler 2000] 8
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
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 Transfer curve: “gain” � gain (flat,steep,flat) [output] � adequate noise margins 0 [input] 1 • Curve can be achieved with certain dna-binding proteins • Inverters with these properties can be used to build complex circuits 10
Measuring a Transfer Curve • Construct a circuit that allows: – Control and observation of input protein levels – Simultaneous observation of resulting output levels inverter CFP YFP R “drive” gene output gene • Also, need to normalize CFP vs YFP Flow Cytometry (FACS) 11
Drive Input Levels by Varying Inducer IPTG (uM) lacI [high] 0 YFP P(lacIq) P(lac) (Off) 0 IPTG 100 P(lacIq) lacI IPTG P(lac) YFP 1000 promoter protein coding sequence Controlling Input Levels 1,000.00 100.00 pINV-112-R1 FL1 pINV-102 10.00 1.00 0.1 1.0 10.0 100.0 1,000.0 10,000.0 IPTG (uM) Also use for CFP/YFP calibration 12
Cell Population Behavior Red = pPROLAR Rest = pINV-102 with IPTG (0.1 to 1000 uM) CFP: a Weak Fluorescent Protein IPTG Fluorescence Induction of CFP expression 13
Measuring a Transfer Curve for lacI/p(lac) tetR lacI 0 [high] CFP YFP λ P(R) (Off) P(LtetO-1) P(lac) aTc measure TC λ P(R) tetR P(lac) YFP aTc P(Ltet-O1) lacI CFP Transfer Curve Data Points 0 � 1 1 � 0 undefined 1,400 1,400 1,400 1,200 1,200 1,200 1,000 1,000 1,000 Events 800 Events 800 Events 800 600 600 600 400 400 400 200 200 200 0 0 0 1 10 100 1,000 10,000 1 10 100 1,000 10,000 1 10 100 1,000 10,000 Fluorescence (FL1) Fluorescence (FL1) Fluorescence (FL1) 1 ng/ml aTc 10 ng/ml aTc 100 ng/ml aTc 14
lacI/p(lac) Transfer Curve tetR lacI 0 [high] CFP YFP λ P(R) (Off) P(LtetO-1) P(lac) aTc 1000 gain = 4.72 gain = 4.72 100 Output (YFP) 10 1 1 10 100 1000 Input (Normalized CFP) Evaluating the Transfer Curve • Gain / Signal restoration: • Noise margins: 1,000 1,400 1,200 1,000 Fluorescence 100 800 Events high gain high gain 600 10 400 200 30 ng/ml 3 ng/ml aTc aTc 1 0 0.1 1.0 10.0 100.0 1 10 100 1,000 aTc (ng/ml) Fluorescence * note: graphing vs. aTc (i.e. transfer curve of 2 gates) 15
Transfer Curve of Implies tetR [high] lacI YFP aTc IPTG 3 10 2 Median FLR 10 1 10 3 10 0 10 2 2 10 10 IPTG (mM) 1 10 1 0 10 10 aTc (ng/ml) The Cellular Gate Library Add the cI/ λ P(R) Inverter • cI is a highly efficient repressor cooperative high binding gain O R 2 O R 1 structural gene λ P(R-O12) cI bound to DNA • Use lacI/p(lac) as driver lacI cI 0 [high] CFP YFP λ P(lac) (Off) P(R) IPTG 16
Initial Transfer Curve for cI/ λ P(R) 1,000.00 Output (YFP) 100.00 10.00 1.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) P(lacIq) lacI λ YFP P(R) IPTG P(lac) cI CFP Recall Inverter Components translation RBS RBS input output ribosome ribosome mRNA mRNA transcription operator promoter RNAp 17
Functional Composition of an Inverter cooperative translation transcription inversion binding “gain” ψ Ζ ρ Α ψ Ζ + + = φ Α 0 1 0 ψ Α 1 0 φ Α 1 0 ρ Α 1 ψ Α + + = scale input “clean” signal invert signal digital inversion ψ Α = input mRNA ρ Α = bound operators φ Α = input protein ψ Ζ = output mRNA Genetic Process Engineering I: Reducing Ribosome Binding Site Efficiency translation stage φ Α translation start ψ Α S B R Inversion ψ Ζ Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak ψ Α 18
Experimental Results for cI/ λ P(R) Inverter with Modified RBS 1,000.00 100.00 Output (YFP) pINV-107/pINV-112-R1 pINV-107/pINV-112-R2 pINV-107/pINV-112-R3 10.00 1.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) Genetic Process Engineering II: Mutating the λ P(R) operator cooperative binding ρ Α φ Α ψ Ζ orig: TACCTCTGGCGGTGATA mut4: TAC A TCTGGCGGTGATA mut5: TAC A T A TGGCGGTGATA mut6 TAC AGA TGGCGGTGATA O R 1 ψ Α BioSPICESimulation 19
Experimental Results for Mutating λ P(R) 1,000.00 100.00 Output (YFP) pINV-107-mut4/pINV-112-R3 pINV-107-mut5/pINV-112-R3 pINV-107-mut6/pINV-112-R3 10.00 1.00 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) Genetic Process Engineering 1,000.00 mutate operator #2: mutate operator Output (YFP) 100.00 modify RBS 10.00 RBS 1.00 #1: modify RBS 0.1 1.0 10.0 100.0 1,000.0 IPTG (uM) • Genetic modifications required to make circuit work • Need to understand “device physics” of gates – enables construction of complex circuits 20
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 Lab-on-a-chip: µFACS [Quake] the µFACS device Molecular Evolution of the Circuit 21
Prediction of Circuit Behavior Input Output signal signal Can the behavior of a complex circuit be predicted using only the behavior of its parts? 1,000 1,000 1,000 ? 100 100 100 = 10 10 10 1 1 1 0.1 1.0 10.0 100.0 0.1 1.0 10.0 100.0 0.1 1.0 10.0 100.0 Prediction of Circuit Behavior preliminary results P(bla) tetR P(lac) cI aTc λ P(R) P(tet) lacI CFP YFP aTc # cells YFP 22
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