Can Synthetic Biology Solve Real-World Problems?

What is Synthetic Biology?

Words/Phrases that you think of when you hear “SYNTHETIC BIOLOGY” S 5 POSITIVE S 5 NEGATIVE What’s in a name? News Feature, Nature Biotech, December 2009 What is Synthetic Biology?

S One possible definition: “Synthetic biology is the discovery, invention, and manufacture of biochemical elements and systems to produce tools, materials, organisms, and devices that meet human needs.”

S Two Areas S A) the design and construction of new biological parts, devices, and systems, and S B) the re-design of existing, natural biological systems for useful purposes. Wöhler Synthesis of Urea: The Birth of Synthetic Organic Chemistry (1828)

• First example of combining inorganic reactants (isocyanic acid + ammonia) to produce an organic compound (urea)

• Wöhler to his mentor Berzelius: "I cannot, so to say, hold my chemical water and must tell you that I can make urea without thereby needing to have kidneys, or anyhow, an animal, be it human or dog".

http://en.wikipedia.org/wiki/File:Wohler_synthesis.gif Why should you be interested in synthetic biology? • “We have got to the point in human history where we simply do not have to accept what nature has given us…” • “By combining elements of engineering, chemistry, computer science, and molecular biology, synthetic biology seeks to assemble the biological tools necessary to redesign the living world…” • “Synthetic biology will create cheap drugs, clean fuels, and new organisms to siphon carbon dioxide from the atmosphere.”

http://www.newyorker.com/ What Can We GET From Engineering Biological Systems?

Toxic Waste

Global Warming

Energy

http://doegenomestolife.org/ New Therapeutics What do we NEED from engineered biological systems and how do we get there?

Engineered systems must • Do we need new be biology? – Reliable • Do we need a new – Predictable way of engineering? – Functionally robust A Synthetic Biologists Specific Aims:

• Describe and predict the behavior of biological networks – Isolate modules of gene networks (autoregulatory networks, toggle switches, etc.) – Develop theoretical models of gene regulation to predict and describe the behavior of simple networks – Modeling may be utilized to guide experimentation – Couple well-characterized modules in order to deduce the behavior of large-scale networks

• Design new genetic components that can serve as modular, global components – Sensors of environmental stimuli – Probes to explore the behavior of biological networks – Regulators to control genetic, regulatory or metabolic networks

• Recode & engineer whole organisms – rE.coli

Gain a better understanding of gene expression & cellular behavior

Utilize this knowledge to engineer cellular function Cellular Phone: Designed and built by engineers EVERY component is characterized

Cellular Network: Exhibit remarkably robust, precise behavior in the absence of our understanding Biological Complexity

reduce the complexity of networks from natural complex biological setting to isolate and study modular components that perform a specific function

Modular Cell Biology

Modules: composed of many types of molecules - DNA, RNA, proteins, small molecules - which have discrete functions that arise from interactions among their components

Hartwell, Hopfield, Leibler, Murray Nature 402 (1999) Arnone & Davidson Development 124 (1997) Genome Sequences: “the framework”

“The sequence provides the framework upon which all the genetics, biochemistry physiology, and ultimately phenotype depend...The sequence is only the first level of understanding the genome. All genes and control elements must be identified; their functions in concert as well as in isolation, defined; their sequence variation worldwide described; and the relation between genome variation and specific phenotypic characteristics determined. Now we know what we have to explain.”

J.C. Venter et al. Science 291 (2001) DNA Sequencing Technologies

Systems Biology Synthetic Biology Reduced Genomes Essential Genes Emergent Properties of Genome Reduced-Genome Escherichia Transplantation coli Lartigue, Venter et al. Science (2007) Hutchison, Venter et al. Lartigue, Glass et al. Science (1999) Science (2009)

Pósfai, Blattner et al. Science (2006)

Merging Genomes Genome Restructuring Refactoring bacteriophage T7 Chan, Kosuri and Endy Molecular Genome Systems Biology (2005) Itaya, Fujita et al. Synthesis PNAS (2006) Gibson, Smith et al. Science (2008) Natural & Synthetic Gene Circuits à Engineering Biology

Sprinzak & Elowitz Nature 438 (2005) The Design Cycle: Coupling Theory and Experiment (Street and Mayo 1999)

Computational Methods predict P the “Best” theoretical solutions A R P A R M E E D T E I R C I T Z I A O T N Experimental Methods I measure, compare and quantify O N success and failure of predictions Synthetic Gene Networks:

• Construction of small gene networks from well-characterized biological parts, guided by models

Bistable Toggle Switch Gardner, Cantor & Collins Nature 403 (2000)

Repressilator Elowitz & Leibler Nature 403 (2000)

Feedback Loops

Freeman Nature 313 (2000) Becskei et al Nature 405 (2000) & EMBO J 20 (2001) Isaacs et al PNAS 100 (2003) Good Review: Sprinzak & Elowitz Nature 438 (2005) Building blocks: Promoters, Repressors and Feedback Natural Negative Feedback: Promoter (PR) expresses Protein (Cro) which represses PR

Synthetic Negative Feedback: Tighter distribution of protein product than unregulated protein production

Hasty & Collins Nature 420 (2002) Building blocks: Promoters, Repressors and Feedback

Synthetic positive feedback: can be self- perpetuating and Natural positive feedback: essentially irreversible Mos-MEK-p42 MAPK cascade required for Xenopus oocyte maturation Hasty & Collins Nature 420 (2002) Ferrell Curr Opin Chem Bio (2002) Bistability: Double-negative or Positive Feedback Loops

• Bistability: two stable states (minima) are separated by a peak (maximum)

Ferrell Curr Opin Cell Bio (2002) Bistable Toggle Switch Gardner, Cantor & Collins Nature 403 (2000)

Natural toggle-switch: l-phage lysis or lysogeny decision

Synthetic toggle-switch: • “Repress my repressor” cascade • Only requires pulse of one trigger to switch the state • IPTG (inducer) inhibits lacI repression of Ptrc-2: • CI expressed, represses lacI • GFP expressed indefinitely • Heat (inducer) inhibits cIts repression of PLs1con: • lacI expressed, represses GFP Fluorescence GFP cIts • GFP repressed indefinitely Genetic Logic Gates

Hasty & Collins Nature 420 (2002) The Repressilator Elowitz & Leibler Nature 403 (2000)

Simple computational model of transcriptional regulation with parameters for: • dependence of transcription rate on repressor concentration, • translation rate, • decay rates of the protein and messenger RNA.

Two types of solutions are possible: • system may converge to stable steady state, or • steady state may become unstable, leading to sustained limit-cycle oscillations

Oscillations predicted when: • strong promoters coupled to efficient ribosome- binding sites • tight transcriptional repression (low `leakiness'), • cooperative repression characteristics, • comparable protein and mRNA decay rates The Repressilator Elowitz & Leibler Nature 403 (2000) Can we design a biological function from the ground up? Variability Instability

Even well-characterized components did not yield a well-

Fluorescence behaved system Time (min)

Unpredictable Elowitz & Leibler Nature 403 (2000) Design of cell-cell communication: Genetic Band-Pass Detector Basu, Gerchman, Collins, Arnold & Weiss Nature 434 (2005)

The PARTS: • LuxI: Enzyme produces AHL • AHL: Signaling molecule, activates LuxR • LuxR: Senses AHL, activates LacIm1 & CI • LacIm1: Weak repressor of GFP • CI: Strong repressor of LacI • LacI: Repressor of GFP Design of cell-cell communication: Genetic Band-Pass Detector Basu, Gerchman, Collins, Arnold & Weiss Nature 434 (2005)

BAND-PASS DETECTOR FEATURES: • Completely dependent on strength of LuxR • CI better repressor than LacIm1, better response to LuxR • High-Detect: Upper threshold of AHL for GFP inhibition • Low-Detect: Lower threshold of AHL for GFP inhibition • Band-detect: Middle range of AHL for GFP expression

Changing LuxR strength shifts both the high detect and low detect thresholds for AHL detection and hence GFP expression Coordinating cellular networks to achieve higher-order patterning

RED: Strong LuxR GREEN: Weak LuxR

[AHL] µ 1 . LuxR Design of global cell-to-cell communications to generate synchronized oscillations Danino, Mondragon-Palomina, Tsimring & Hasty Nature 463 (2010)

THE PARTS: • luxI: enzyme produces AHL • AHL: signaling molecule, activates LuxR ABOVE A CONCENTRATION THRESHOLD (Quorum) • LuxR: transcriptional activator of luxI, GFP, & aiiA • aiiA: degrades AHL Design of global cell-to-cell communications to generate synchronized oscillations Danino, Mondragon-Palomina, Tsimring & Hasty Nature 463 (2010) Design of global cell-to-cell communications to generate synchronized oscillations Danino, Mondragon-Palomina, Tsimring & Hasty Nature 463 (2010)

Generating synchronized waves of fluorescence in a longer device Design of global cell-to-cell communications to generate synchronized oscillations Danino, Mondragon-Palomina, Tsimring & Hasty Nature 463 (2010) 3D device shows radial waves after quorum is setup From circuits to systems: where do we stand?

The total number of systems (circuits) described has increased, but not the complexity of individual systems. Harnessing biology for next generation biofuels

Image by Eric Steen, JBEI

• US consumes 9 million barrels (1,400,000 m3) petroleum / day as motor fuel

Image by Jonathan Remis, JBEI Biosynthesis of Artemisinin: A global health success story

Artemisinin

Malaria: Will reduce cost of treatment 10- 300-500 fold million new infections each year

1-3 million deaths Engineering an 11-enzyme Pathway in Parts

Keasling JACS Chem Biol 3 (2007) Compartmentalization and Shutting off Competing Pathways

Martin et al. Nature Biotech 21 (2003) Combinatorial Tuning of Intergenic Regions in Synthetic Operon

Occluded RBS

Keasling JACS Chem Biol 3 (2007) Pfleger et al Nature Biotech 24 (2006) Synthetic scaffolds for selecting optimal operon stoichiometry

Keasling JACS Chem Biol 3 (2007) Dueber et al Nature Biotech 27 (2009) De novo gene synthesis of tobacco ADS enzyme with E. coli codon optimization Overlapping Oligo Assembly: DETAILS IN JIM’s LECTURE

Martin et al. Nature Biotech 21 (2003) Keasling JACS Chem Biol 3 (2007) Functional genomic discovery of native A. annua oxidase

• Create leaf cDNA library from A. annua • Degenerate PCR from cDNA using lettuce and sunflower (closely related Phylogenetic tree of Asteraceae crops that new A. annua P450 produce terpenes) P450 97 71C1 85 100 71C2 71C3 sequences 71C4 90 71D12 60 100 71D13 • Subclone novel P450 and 86 71D18 88 100 71D16 71D20 redox partner into S. 71AV1 92 73A1 cerevisiae 98A3 66 91 79F1 90 701A3 74 • MIRACLE (three steps 93C1 100 75A1 90 79 75B2 catalyzed!) to produce 86 76B6 706B1 artemisinic acid 84A1 88A3 100 100 90A1 90B1 50 changes Biosynthesis of Artemisinin: A global health success story

Artemisinin

Malaria: Will reduce cost of treatment 10- 300-500 fold million new infections each year

1-3 million deaths Can we move to a new paradigm?

• Conceive a desired biological function • Design an engineered biological system • Build it • It works as predicted