Programmed Cell Applications. Programming Cells

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1 The Device Physics of Cellular Logic Gates Ron Weiss Department of Electrical Engineering Princeton University SC-1 Programming Cells Environment actuators sensors Biochemical logic circuit.5µm Understand and engineer: Genetic regulatory networks Cell-cell communications 1

2 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 ).5µm 2

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

4 Why Digital? We know how to program with it Signal restoration + modularity = robust complex circuits Cells doit Phageλ 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 Y R 1 Z = gene Y R 1 gene Z AD T gene Proteins are the wires/signals Promoter + decay implement the gates AD gate is a universal logic element: any (finite) digital circuit can be built! 4

5 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 Proof of Concept Circuits Work in BioSPICE simulations [Weiss, Homsy, agpal, 1998] RS-Latch ( flip-flop ) Ring oscillator time (x1 sec) _ [R] _ [S] _ R A [A] [B] [B] _ S B [C] [A] time (x1 sec) time (x1 sec) They work in vivo Flip-flop [Gardner & Collins, 2], Ring oscillator [Elowitz & Leibler, 2] Models poorly predict their behavior 5

6 Actual Behavior of Ring scillator [Elowitz & Leibler, 2] TheCellularGateLibrary 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

7 Device Physics in Steady State Ideal inverter [output] gain Transfer curve: gain (flat,steep,flat) adequate noise margins 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 CFP R inverter YFP drive gene output gene Also, need to normalize CFP vs YFP 7

8 The IMPLIES Gate RA P active repressor inactive repressor no transcription inducer transcription RA 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: (T R) R I Repressor Inducer utput Repressor Inducer utput Drive Input Levels by Varying Inducer IPTG (um) (ff) P(lacIq) laci [high] IPTG P(lac) YFP P(lacIq) laci IPTG 1 P(lac) YFP 1 promoter protein coding sequence 8

9 Controlling Input Levels 1,. 1. FL1 piv-112-r1 piv ,. 1,. IPTG (um) Also use for CFP/YFP calibration Measuring a Transfer Curve for laci/p(lac) (ff) λ P(R) tetr [high] P(Ltet-1) laci CFP P(lac) YFP atc measure TC λ P(R) tetr atc P(lac) YFP P(Ltet-1) laci CFP 9

10 Transfer Curve Data Points 1 undefined 1 1,4 1,4 1,4 1,2 1,2 1,2 1, 1, 1, Events 8 6 Events 8 6 Events , 1, , 1, , 1, Fluorescence (FL1) Fluorescence (FL1) Fluorescence (FL1) 1ng/mlaTc 1 ng/ml atc 1 ng/ml atc laci/p(lac) Transfer Curve (ff) λ P(R) tetr [high] P(Ltet-1) laci CFP P(lac) YFP atc 1 utput (YFP) 1 1 gain = Input (ormalized CFP) 1

11 Evaluating the Transfer Curve Gain / Signal restoration: oise margins: 1, 1,4 1,2 Fluorescence 1 1 high gain Events 1, atc (ng/ml) 2 3 ng/ml atc 3ng/ml atc , 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 binding high gain R 2 R 1 structural gene λ P(R-12) Use laci/p(lac) as driver ci bound to DA (ff) laci [high] P(lac) ci CFP λ P(R) YFP IPTG 11

12 Initial Transfer Curve for ci/λ P(R) 1,. utput (YFP) ,. IPTG (um) P(lacIq) laci IPTG λ P(R) YFP P(lac) ci CFP Inverter Components translation RBS RBS input mra ribosome output mra ribosome operator transcription promoter RAp 12

13 Functional Composition of an Inverter translation cooperative binding transcription inversion φ Α + + = ψ Ζ ρ Α ψ Ζ gain ψ 1 1 Α φ ρ 1 Α Α 1 ψ Α scale input + clean signal + invert signal = digital inversion ψ Α = input mra φ Α = input protein ρ Α = bound operators ψ Ζ = output mra Genetic Process Engineering I: Reducing Ribosome Binding Site Efficiency translation stage φ Α translation start ψ Α RBS Inversion rig: ATTAAAGAGGAGAAATTAAGCATG strong RBS-1: TCACACAGGAAACCGGTTCGATG RBS-2: TCACACAGGAAAGGCCTCGATG RBS-3: TCACACAGGACGGCCGGATG weak ψ Ζ ψ Α 13

14 Experimental Results for ci/λp(r) Inverter with Modified RBS 1,. utput (YFP) 1. piv-17/piv-112-r1 piv-17/piv-112-r2 piv-17/piv-112-r IPTG (um) 1. 1,. Genetic Process Engineering II: Mutating the λp(r) operator cooperative binding ρα φα orig: mut4: mut5: mut6 TACCTCTGGCGGTGATA TACA ATCTGGCGGTGATA TACA ATA ATGGCGGTGATA TACAGA AGATGGCGGTGATA AGA R1 ψζ ψα BioSPICE Simulation 14

15 Experimental Results for Mutating λ P(R) 1,. 1. utput (YFP) piv-17-mut4/piv-112-r3 piv-17-mut5/piv-112-r3 piv-17-mut6/piv-112-r ,. IPTG (um) Genetic Process Engineering 1,. utput (YFP) modify RBS mutate operator #2: mutate operator ,. IPTG (um) RBS #1: modify RBS Genetic modifications required to make circuit work eed to understand device physics of gates enables construction of complex circuits 15

16 Prediction of Circuit Behavior Can the behavior of a complex circuit be predicted using only the behavior of its parts? Input signal utput signal 1, 1 1 1, 1 1? = 1, Cell-cell Signaling 16

17 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 AD Gate RA P inactive activator active activator no transcription inducer transcription RA P promoter operator gene promoter operator gene Inducers can activate activators: VAI (3--oxohexanoyl-L-Homoserine lacton) luxr Use as a logical AD gate: Activator Inducer utput Activator Inducer utput

18 Eupryma scolopes Light organ Quorum Sensing Cell density dependent gene expression Example: Vibrio fischeri [density dependent bioluminscence] LuxR LuxI Luciferase (Light) hv luxr luxi luxc luxd luxa luxb luxe luxg P P Regulatory Genes Structural Genes The lux peron LuxI metabolism autoinducer (VAI) 18

19 Density Dependent Bioluminescence Low Cell Density H H High Cell Density H H H H H H H H H H LuxR H LuxR H H H LuxR LuxI LuxR LuxI Luciferase (Light) hv hv P luxr luxi luxc luxd luxa luxb luxe luxg P (+) P luxr luxi luxc luxd luxa luxb luxe luxg P free living, 1 cells/liter <.8 photons/second/cell symbiotic, 1 1 cells/liter 8 photons/second/cell A positive feedback circuit Circuits for Controlled Sender & Receiver Sender cells Receiver cells P(tet) tetr atc VAI VAI + P(Ltet-1) luxi luxr Lux P(L) Lux P(R) GFP(LVA) Sender cells Receiver cells atc tetr atc luxi VAI pluxi-tet-8 VAI LuxR prcv-3 GFP 19

20 Time-Series Response to Signal 25 2 positive control Fluorescence X VAI extract direct signalling prcv-3 + puc19 prcv3 + psd-1 prcv-3 prcv-3 + prw-lpr-2 prcv-3 + ptk-1 AI negative controls : :3 1: 1:3 2: Time (hrs) Fluorescence response of receiver (prcv-3) Characterizing the Receiver 1,2 1, Maximum Fluorescence Autoinducer Level Response of receiver to different levels of VAI extract 2

21 Controlling the Sender s Signal Strength 75, Receiver Fluorescence 5, 25, LuxTet4B9 RCV nly 1x AI ull 2 2 2, 2, 2, 2 atc (ng / ml) Dose response of receiver cells to atc induction of senders.1mm senders receivers overlay 21

22 2 µm receivers senders overlay 22

23 Summary Built and characterized an initial cellular gate library Genetic process engineering mutated logic elements to have desired behavior Using parts that match, built and tested several small in-vivo digital circuits Reliable circuits with predictable behavior from reliable components with known behavior BioSPICE for circuit design/verification Cell-cell signaling to control gene expression Tom Knight Gerald J. Sussman Hal Abelson ick Papadakis George Homsy Radhika agpal Dylan Hirsch-Shell Matt Frank Jered Floyd Jonathan Babb Glenn Paradis Subhayu Basu Acknowledgments 23