Christina Smolke (Stanford) at a LASER on "Synthetic Biology"

Synthetic Biology:The next generation of biotechnologyChristina D. SmolkeDepartment of BioengineeringStanford UniversityJuly 9, 2012LASER Talk, University of San Francisco

Engineering systems Circuitry Sensors (Inputs) Circuitry signal processing remote control Signal Processing Temperature Chemicals memory automated response Automated Response Sensors (Inputs) Light dynamic controlTouch communication Dynamic Control Memor chemicals Remote control Communication biomolecules temperature light Actuators (Outputs) Actuators (Outputs) Motility Reporting reporting phenotype Delivery Self-Organization delivery self-organization Modify Environment motility synthesis

Synthetic biologyensors (Inputs) Circuitry CircuitryTemperature Chemicals signalProcessing Signal processing remote controlLight Automated Response automated response memory Touch Sensors (Inputs) Communication dynamicControl Dynamic control Memory communicationRemote control chemicals biomolecules temperature light Actuators (Outputs) Actuators (Outputs) Motility Reporting reporting phenotype Delivery self-organization delivery Self-Organization motility synthesis Modify Environment

Tools driving genetic engineering Recombinant DNA Polymerase Chain Reaction DNA SequencingFirstGen.Biotech = Basic “Cut” & “Paste” Amplify & Make Simple Changes Read Out the Genetic Code Insulin Production Erythropoietin Production ? 10-1000’s genes complex chemicals & materials organisms as products 1973 1985 ….bacterial cell culture, 1 gene mammalian cell culture, 1 gene

Ongoing tools revolution Recombinant DNA Polymerase Chain Reaction DNA SequencingFirstGen.Biotech = Basic “Cut” & “Paste” Amplify & Make Simple Changes Read Out the Genetic CodeNextGen.BiotechAdds = ...NewTools c/o D. Endy (Stanford University)

Transformative advances in fabrication platforms DNA Construction = #1 Tech. of 21st Ctry. From absract information to physical, living DNA designs. Organic chemistry Biochemistrynstruction = #1 Tech. of 21st Ctry. TAATACGACTCACTATAGGGAGA …TAATGCAGCTTATTACA…(<200 nt) enzymes 2004: 10,000 bp 2010: 1,000,000 bp 2016: 100 million? 26 (~1000 –ATACGACTCACTATAGGGAGA 10000 bp) A T G C

Transformative advances in fabrication platforms Carr PA, Church GM. 2009. Nat Biotech. 27: 1151-1162Cellular (in vivo) assembly 1 Mb+ engineered natural genome transplantation Gibson DG, et al. 2010. Science. 329: 52-6

Now that we canwrite in DNA, what do we say?Challenge: Design Gap

Buffe Inverter1+Invert Buff Inver 2xInvert Invert 2xInver Buffer1+Buff 2xBuf 2xBuf Inver Buf 2xInver 2xBuf Buf 2xBuf Buf 2xBuff D Buff 2xBuff output protein (high when AA) Biomanufacturing Platforms Next-Generation Therapies C AND gate D16 14.0 Device response in unit expression 14 A input A input B AB output Apps B 12 [SI 1.2] A B output 10 theo tc GFP 8 0 0 0 0 1 0 6 1 0 0 4 3.1 AAAAA AAA 1 1 1 2.0 2 0 0 GFP low low low high output protein (high when AB) theo + + tc + + Scalable Biological Computation E NOR gate F 10 9 8.1 Device response in unit expression input A input B Circuits A 8 A+B output B 7 [SI 1.3] A B output 6 theo tc GFP 5 0 0 1 4 0 1 0 3 2.0 1 0 0 AAAAA 2 1.1 1 1 0 1 0 0 GFP high low low low output protein (high when A+B) theo + + tc + +input module actuator module output module assembled RNA device I/O Tools Spatial Engineering Figure 2 F Win and Smolkeprotein sensor alternative exon inclusion gene of interest Tools Golgi  Vacuole  + + GOI GOI output 3’ss 5’ss ER  The identity of the RNA sensor and Mitochondrion  Nucleus  its location in the intronic space will determine the effect of input binding exon inclusion exon exclusion input A on the device output. (low device output) (high device output) The Smolke Laboratory

Molecular computers enable programming of functioninput sensor1 / transmitter1 / actuator output input A sensor1 / transmitter2 / actuator output input A sensor2 / transmitter1 / actuator output input B output input output Win MN, Smolke CD. 2007. PNAS. 104: 14283-8

Molecular computers enable programming of functioninput sensor1 / transmitter1 / actuator output input A sensor1 / transmitter2 / actuator output input A sensor2 / transmitter1 / actuator output input B output n sensors / n transmitters / actuator A B output 0 0 1 0 1 1 1 0 1 1 1 0Win MN, Smolke CD. 2008. Science. 322: 456-60

Fluorescence low GFP AAA AtropineCellular biosynthesis Muscarinic blockerplatforms GFP AAA Paclitaxel Cancer Ligand present Ribozyme inactive GFP AAA Fluorescence high Digoxin Arrhythmias Ceftaroline Antibiotic Prostratin HIV 1 2

H 3 CO H 3CO NCH3 NCH 3 HO HO O OH antimicrobial Norcoclaurine Coclaurine N-Methylcoclaurine anticancer Berbamunine Tyrosine BisBIAs 3’-Hydroxy-N- methylcoclaurineantiviral Norlaudanosoline 6-O-Methyl- norlaudanosoline Reticuline hair growth Salutaridinol-7- Salutaridinol Salutaridine O-acetate Scoulerine Tetrahydro- Canadine Berberine columbamine Thebaine Neopinone Codeinone antimicrobial Berberine branch analgesic antioxidant antitussive Cheilanthifoline Codeine Cis-N- Dihydro- Sanguinarine Stylopine Methylstylopine sanguinarine Morphine branch Morphine Sanguinarine branch Hawkins KM, Smolke CD. 2008. Nat Chem Biol. 4: 564

Building a microbial drug factoryEnzymes catalyze reactions:

Building a microbial drug factory Bis-BIAs Sanguinarine / Morphinan Berberine Alkaloids Alkaloids O N O OMe OMe

Building a microbial drug factory DNA protein metabolite Bis-BIAs Sanguinarine / Morphinan Berberine Alkaloids Alkaloids O N O OMe OMe

Building a microbial drug factory Bis-BIAs Sanguinarine / Morphinan Berberine Alkaloids Alkaloids O N O OMe OMe

Molecular tools for optimizing metabolite productionChallenge: Often need to screen through large libraries of pathway/enzymevariants – invasive / analytical procedures are too time and resource intensiveSolution: Engineer scalable platforms for noninvasive sensors of metaboliteconcentrations in single living cells AAA AAA

I/O tools as noninvasive, real-time metabolite sensors AAAA GFP Ligand absent Ribozyme active GFP AAA Fluorescence low Ligand absent Ribozyme active GFP AAAA AAA GFP Fluorescence low AAA Ligand absent GFP Ribozyme active GFP AAA Fluorescence low GFP AAA GFP GFP AAA AAAA GFP AAA GFP AAA Ligand present Ribozyme inactive GFP GFP AAA AAA Fluorescence high GFP AAAA Ligand present Ribozyme inactive GFP AAAMichener JK, Smolke CD. 2012. Metab Eng. In press Fluorescence high Ligand present Ribozyme inactive GFP AAA

Evolution… in the laboratory mutagenesis … identify best variant X

High-throughput enzyme library screening methods plate-based assay (library size: ~103) FACS-based screen (library size: ~106-107) Molecular computer links enzyme activity to fluorescence Michener JK, Smolke CD. 2012. Metab Eng. In press

Li 2 0.1 a yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 (-) Control b 1.8 300 α-actin α-V5 KM, app 0 1.6 0.0 Selectivity 250 0% 20% 40% 60% 80% 100%1.4 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Rapid Fluorescence Change of enzymes with improved activities identification Relative Activity KM, app (mM) 200 Selectivity 1.2 35c 0.8 d1.0 vmax, app/KM, app 150 0.8 30Library Frequency Theophylline production Fold Improvement 0.7 Relative Enzyme model1.0x 1.0x Expression 0.6 1.0x 1.2x 0.9x 0.7x 0.0x 0.6 0.4 25 100 0.5 50 c 0.2 20 0.4 0.0 0 yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 0.3 15 0.2 56 d 10 0.1 54 0.0 5 Post-sort #3 52 0.0 0.2 Post-sort #1 T50 (oC) 0.4 0.6 Pre-sort #1 50 0 0.8 1.0 yCDM1 yCDM2 yCDM3 yCDM4 yCDM5 yCDM6 yCDM7 yCDM8 1.2 48 Relative Activity Plate Based FACS 46 a yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 (-) Control 44b 1.8 300 α-actin α-V5 KM, app 1.6 Selectivity 42 250 1.4 40 Wildtype yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 KM, app (mM) 200 Selectivity 1.2 1.0 150 0.8 Relative 0.6 100 Catalytic 1.0x 1.2x and selectivity 1.0x Expression activity 1.0x 0.9x 0.7x 0.0x 0.4 increases through evolutionary 50 c 0.2 trajectory 0.0 yCDM1 yCDM3 yCDM5 yCDM6 yCDM7 yCDM8 0 Michener JK, Smolke CD. 2012. Metab Eng. In press 56 d 54

Cellular therapies: engineering the immune system transfer engineered cells into patient harvest lymphocytesrecover and engineer from patientdesired cell typehttp://www.discoverymedicine.com/Leslie-E-Huye/files/2010/03/

A molecular computer controlling immune response proliferation apoptosis / death Chen YY, Jensen MC, Smolke CD. 2010. PNAS. 107: 8531-6

A molecular computer controlling immune response 1.2E+09 L2bulge9(3x) No Theo  L2bulge9(3x) 0 uM Theo Flux (photons/sec) 1.0E+09 No drug L2bulge9(3x) 500 μM Theo  L2bulge9(3x) 500 uM Theo 8.0E+08 6.0E+08 4.0E+08 2.0E+08 0.0E+00 0 2 4 6 8 10 12 14 16 Days Post InjectionWith drug Chen YY, Jensen MC, Smolke CD. 2010. PNAS. 107: 8531-6

AppsStandardization Abstraction Synthesis Device Design Languages & Grammar AGGTACAGTTATCA CCCATTGCATGGTA TTCAAAGAAGTCGT output GGCCCAGATTCGAC AAATCGTGTAGTAA TGGTCCAGCTGATT input Tools GGTTCAAATAACGG

Postdoctoral Researchers The Smolke LaboratoryKate ThodeyEric HaydenGraduate ResearchersRyan BloomAndy ChangLeo d’EspauxStephanie GalanieKatie GallowayDrew KennedyJoe LiangMelina MathurJosh MichenerMichael SiddiquiIsis TrenchardJay Vowles Alumni CollaboratorsYen-Hsiang Wang Andrew Babiskin Michael Jensen (SCRI, FHCRC)Kathy Wei Travis BayerRemus Wong Chase Beisel Funding Sources Yvonne Chen Defense Advanced Research Projects Stephanie Culler Agency Kristy Hawkins Bill and Melinda Gates Foundation Kevin Hoff National Institutes of Health (NIGMS, NCI) Maung Nyan Win National Science Foundation (CBET, CCF)

Christina Smolke (Stanford) at a LASER on "Synthetic Biology"

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Christina Smolke (Stanford) at a LASER on "Synthetic Biology" Presentation Transcript