Programming Cells Environment actuators sensors Biochemical logic - PDF document

The Device Physics of Cellular Logic Gates Ron Weiss Department of Electrical Engineering Princeton University NSC-1 Programming Cells Environment actuators sensors Biochemical logic circuit 0.5µm • Understand and engineer: – Genetic regulatory networks – Cell-cell communications 1

Programmed Cell Applications • “Real time” cellular debugger – detect conditions that satisfy logic statements – maintain history of cellular events • Environmental – sense & respond to complex environmental conditions • Biomedical – combinatorial gene regulation with few inputs • Molecular-scale fabrication – cellular robots that manufacture complex scaffolds Programming Cells Biochemical logic circuit proteins synthesize & insert plasmid (plasmid = “user program”) 0.5µm 2

A Biochemical Inverter signal = concentration of specific molecules (mRNA) computation = regulated mRNA and protein synthesis + decay Outline • In-vivo digital circuits • Cellular gates: Inverter, Implies • BioSPICE circuit simulations & design • Measuring & modifying “device physics” • Cell-cell Signaling 3

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 Logic Circuits based on Inverters X R 1 X = R 1 Z Z gene Y R 1 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! 4

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 “Proof of Concept” Circuits • Work in BioSPICE simulations [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] • Models poorly predict their behavior 5

Actual Behavior of Ring Oscillator [Elowitz & Leibler, 2000] The Cellular Gate Library • Assembled and characterized a library of gates – Constructed and measured gates using 4 genetic elements • lac, tet, cI, lux • Genetic process engineering – Different elements have widely varying characteristics • Modify “device physics” of gates until they match – Created 16 variations of cI in order to match with lac : • modified repressor/operator affinity • modified RBS efficiency • Established component evaluation criteria – Initially, focused on steady state behavior 6

Device Physics in Steady State “Ideal” inverter Transfer curve: “gain” � gain (flat,steep,flat) [output] � adequate noise margins 0 1 [input] • Curve can be achieved with certain dna-binding proteins • Inverters with these properties can be used to build complex circuits Measuring a Transfer Curve • Construct a circuit that allows: – Control and observation of input protein levels – Simultaneous observation of resulting output levels inverter R CFP YFP “drive” gene output gene • Also, need to normalize CFP vs YFP 7

The IMPLIES Gate active inactive repressor repressor RNA P inducer transcription no transcription RNA P promoter operator gene promoter operator gene • Inducers that inactivate repressors: IPTG (Isopropylthio-ß-galactoside) � Lac repressor – aTc (Anhydrotetracycline) � Tet repressor – (NOT R) OR I • Use as a logical Implies gate: Repressor Inducer Output 0 0 1 Repressor Output 0 1 1 Inducer 1 0 0 1 1 1 Drive Input Levels by Varying Inducer IPTG (uM) lacI [high] 0 YFP P(lacIq) P(lac) (Off) 0 IPTG 100 lacI P(lacIq) IPTG YFP P(lac) 1000 promoter protein coding sequence 8

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 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 YFP P(lac) aTc lacI CFP P(Ltet-O1) 9

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 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) 10

Evaluating the Transfer Curve • Gain / Signal restoration: • Noise margins: 1,000 1,400 1,200 1,000 100 Fluorescence 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) 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(R) (Off) P(lac) IPTG 11

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 P(R) YFP IPTG P(lac) cI CFP Inverter Components translation RBS RBS input output ribosome ribosome mRNA mRNA transcription operator promoter RNAp 12

Functional Composition of an Inverter cooperative translation transcription inversion binding “gain” ψ Ζ + + = ρ Α ψ Ζ φ Α 0 1 0 1 0 1 0 1 ψ Α φ Α ρ Α 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 B S Inversion R ψ Ζ Orig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak ψ Α 13

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 TACCTCTGGCGGTGATA orig: TACA ATCTGGCGGTGATA A A mut4: TACA A ATA A A A ATGGCGGTGATA mut5: TACAGA AGA AGA AGATGGCGGTGATA mut6 O R 1 ψ Α BioSPICE Simulation 14

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 15

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 Cell-cell Signaling 16

Intercellular Communications • Certain inducers useful for communications: 1. A cell produces inducer 2. Inducer diffuses outside the cell 3. Inducer enters another cell 4. Inducer interacts with repressor/activator � change signal main metabolism (1) (2) (3) (4) The Intercellular AND Gate inactive active activator activator RNA P inducer transcription no transcription RNA P promoter operator gene promoter operator gene • Inducers can activate activators: VAI (3-N-oxohexanoyl-L-Homoserine lacton) � luxR – • Use as a logical AND gate: Activator Inducer Output 0 0 0 Activator 0 1 0 Output 1 0 0 Inducer 1 1 1 17

Eupryma scolopes Light organ Quorum Sensing • Cell density dependent gene expression Example: Vibrio fischeri [density dependent bioluminscence] (Light) (Light) Luciferase LuxI Luciferase hv LuxR hv P luxR luxI luxC luxD luxA luxB luxE luxG P Regulatory Genes Structural Genes The lux Operon LuxI metabolism � autoinducer (VAI) 18