1. “Transforming Cells into Automata” “Index-based search of single sequences” Presenting:Ravi Tiruvury / Omkar Mate Scribing:Rashmi Raj / Abhita Chugh DFLW:Wissam Kazan Upcoming: 10/19:“Multiple indexes and multiple alignments”Siddharth Jonathan 10/24:“Evolution of Multidomain Proteins”Wissam Kazan “Human Migrations”Anjalee Sujanani 10/17

2. Transforming Cells Into Automata Ravi Tiruvury

3. 3 Imagine… Ron suffers from hypoglycemia (low blood sugar) – but its fine! His “programmed” cells constantly “monitor” the sugar concentrations and stabilize it. Clara’s family has a history of high Cholesterol. But the pre- programmed genetic circuits in her body regularly watch out for cholesterol buildups in the arteries. The Mayor of LA is concerned about the ever-rising pollution levels in the city. Simple solution: Release “cellular robots” into the atmosphere that detect and clean environmental pollutants.

4. 4 Today’s Highlights Gene Networks –What are they? –Why do we need them? Genetic Circuit Building Blocks (Bio-Bricks!) –Logic Gates and Simple Circuits Circuit Design Methodologies –Rational Design –Directed Evolution Cell-Cell Communication Signal Processing

5. 5 Genetic Networks What are they? –Comprise cells and genetic components (Proteins, Inducer molecules, DNA fragments), which “ideally” behave the way we want them to! How is this done? –Exercise “external” control over genetic components by defining and regulating their interaction.

6. 6 EE vs Bio Electrical Circuits Genetic Circuits Basic component of an Electrical Circuit: Transistor Binary “1” => “high” voltage output Binary “0” => “low” voltage output Communication occurs in a fixed, closed environment (like a wire) Outcomes are deterministic Basic component of a Genetic Circuit: Gene Binary “1” => “high” protein concentration Binary “0” => “low” protein concentration Communication occurs in an open environment with the signal possibly received by other than intended receipients Outcomes are stochastic

7. 7 Building Genetic Circuits Step 1: Build a Genetic Component Library –Biochemical Inverter –IMPLIES Gate –NAND Gate –AND Gate Step 2: Assemble them into a Biocircuit Step 3: Tweak/tune the circuit and its components till the desired output is reached. Step 4 : Check output by using a fluorescent protein as a reporter. (for illustrative purposes)

8. 8 Before we dive in… Gene to Protein Translation –RNA Polymerase *binds* to a region of the DNA strand called a Promoter –RNA Polymerase transcribes the gene to mRNA. –mRNA is then translated to Protein. How do we know if a protein has been produced? –Use Reporters - genes that are inserted downstream of a Promoter, which transcribes into a Fluorescent Protein that glows. RNAp Promoter Reporter Gene mRNA Protein Fluorescent Protein DNA

9. 9 Biochemical Inverter Gene RNA Polymerase Target mRNA Gene RNA Polymerase Repressor Protein Repressor Protein Nothing! No RepressorTarget mRNARepressorNo Target mRNA Green Fluorescent Protein (GFP) GFP

10. 10 Inverter Functional Model Monomers → Polymers (bind the Operator) Concentration of Operator bound to the Repressor

11. 11 Implies Logic Function Gene Promoter Gene Promoter Active Repressor Gene Promoter Active Repressor Active Inducer No Effect!! Active Repressor Repressor Inducer Output 0 0 1 0 1 1 1 0 0 1 1 1 Nothing! Gene Promoter

12. 12 AND Gate Notes: 1.Operator is the sequence which regulates the accessibility of the Promoter 2.RNA p has low affinity for promoter, hence, no basal transcription activity 3.Activator has low affinity for operator. Binds to promoter only when an Inducer binds to it

13. 13 More Gates - NAND XYRXRYRZ 001110 011010 100110 110001 AND through NAND + NOT

14. 14 “Celebrating Cells” - A fun circuit! 111 P1P1 R1R1 P2P2 R2R2 P3P3 R3R3 Idea: Each Promoter-Repressor {P i, R i } set is an inverter R 1 represses P 2, R 2 represses P 3, and R 3 represses P 1 So of R 1 is ↑ then R 2 is ↓ and R 3 is ↑ R1R2R3 101 010

15. 15 Circuit Design Goal –Design a DNA sequence that reliably implements a desired cellular function with quantitative precision Approaches –Rational Design (Intelligent design by humans) Gain accurate knowledge about the behavior of genetic components Model the gene network and modify it until the components achieve desired characteristics –Directed Evolution Introduce random mutations in the gene to produce different gene variants Screen the variations that yield the desired behavior.

16. 16 Rational Design Modeling is a common tool for systematic circuit design. Why is modeling a genetic circuit more complicated? –Interactions between circuit components (genes & proteins) are *not* fixed. –State transitions are *rarely* simultaneous –Outcomes are *not* deterministic –Gene networks tend to exhibit significant noise even in the simplest configurations Depending on the requirement, deterministic and stochastic models are used.

17. 17 Modeling Genetic Circuits A common method for modeling biological circuits – use nonlinear ordinary differential equations (ODEs). –The circuit components, i.e. RNA, Protein and other molecule concentrations, are represented by time-dependent variables. –Rate equations describe biochemical reactions as a function of concentrations of the circuit components. They are of the form: where vector x = [x 1, … x n ] includes concentrations of proteins, mRNAs, other molecules and f i is a nonlinear function

18. 18 Modeling an Inverter 1. I/P mRNA to I/P Protein (A) Translation 2. I/P Protein Dimerization and Decay 3. Cooperative Binding of I/P Protein 4. Transcription 5. O/P mRNA Degradation A A2A2 PZPZ mRNA A mRNA Z ODE for simulating promoter P Z bound by dimer A 2 Key Takeaway Each differential equation describes the time-domain behavior of a particular molecular species based on all the equations in the biochemical model that include that particular molecule.

19. 19 Inverter – Dynamic Behavior

20. 20 Deterministic vs Stochastic Models ODE’s are good for: –Systems with large number of molecules for any given species –Systems which are both continuous and deterministic. However, in reality: –Biochemical systems consist of few molecules for a given species –They are usually discrete (reactions change population dynamics at irregular intervals) and stochastic (outcomes vary with order of reactions, environment, inter-component interactions) Tradeoff: –Use Deterministic models if only average behavior needs to be modeled, and computational resources are limited. –Use Stochastic models if accurate quantitative information about noise is available and large computational resources are available.

21. 21 Circuit Design Issues Primary concern –Design genetic circuits such that components work together and yield correct output. –Else, interacting components can produce unexpected results Question –In an unstable, unpredictable environment, how can we make sure we get the expected outcome of a gate or a device? Solution –Construct a circuit wherein the input can be externally controlled, to achieve desirable output. Inverter Example –Couple the inverter to an IMPLIES gate, where we can control one input.

22. 22 Revisiting the Inverter! R1R1 R2R2 I2I2 R3R3 CFPYFP 010001 011110 Possibilities: P 1 : I 2 : P 2 : R 2 : P 3 : R 3 : P 1 : I 2 : P 2 : R 2 : P 3 : R 3 : PROBLEM!! Here, even for low R3 levels, YFP is 0 as R3 is a very efficient repressor even at low concentrations.

23. 23 Rational Design explained To overcome the previous problem, modify some protein sites until the desired response is obtained. Say repressor RBS is mutated to three mutants – RBS 1, RBS 2 and RBS 3. We can see that RBS 2 and RBS 3 gave a promising response. Key Question: How can we find RBS 2 and RBS 3? How do we know which sites to mutate/modify in the DNA sequence?

24. 24 Directed Evolution Do not have to tackle with the issue of what DNA sites to mutate. Technique –Library Creation: Mutate/recombine the gene (encoding the protein of interest) at random. Create a large library of variants. –Variant Screening: Test how the variants perform and contribute to the overall response of the circuit. –If favorable, screen those components, discard the rest, and proceed with mutating another component. DNA Desired Outcome Var1 Var2 Var3 Var4

25. 25 Cell-Cell Communication Cell-cell communication involves a “chemical message” from a sender cell to a receiver cell, wherein subsequently a remote transcriptional response is activated. Quorum-sensing –It’s a bacterial communication and coordination system that allows them to sense their own population density through diffusion of a chemical signal –This is done by diffusion of a chemical signal molecule called Autoinducer into the cells’ surroundings. –The Autoinducer permeates the cells, and its concentration keeps increasing as the cell grows.

26. 26 Cell-cell Communication Schematics 1. Sender cell produces small signaling molecules using metabolic pathways 2. The molecules diffuse outside the membrane and into the environment 3. The signals then diffuse into the neighboring cells 4. Signals interact with proteins in receiver cells.

27. 27 Cell-Cell Communication Demystified aTc VAI tetRaTcluxI (VAI) luxRGFP 10010 11111 Notes: tetR represses luxI. But inducer aTc overrides tetR and induces luxI production VAI => Vibrio Auto Inducer. Chemically, this is GFP => Green Fluorescent Protein, located downstream of luxP R promoter Quorum Sensing constructs from Vibrio fischeri for communication in E. coli

28. 28 Communication - Analysis Receiver cell cultures with different VAI levels incubated @37°C for 5 hrs Observation: Increasing levels of VAI result in corresponding increases in GFP until saturation is reached. A Visual Experiment A small droplet of sender cells was placed in the vicinity of receiver colonies, and a brightfield image was captured to mark the location of various colonies. Observation: VAI Autoinducer diffused at the rate of approx. 1 cm/hour.

29. 29 Communication in Multicellular systems luxR30C 6 HSLluxR.30C 6 HSL 10 0 11 1 lacIIPTG cI 000 101 ~cI 1 0 GFP 0 0 10 0 11 1 011 000 0 1 0 1

30. 30 Signal Processing What if we want Cell A to respond to Cell B only if the signal sent by the sender falls in a particular concentration range? Real-world Example: The retina generates electrical nerve signals in response to the photons detected by rhodopsin in retinal cells. Here, its not just the “presence”, but also the “strength” or “concentration” of the photons is important to generate an appropriate signal. Illustrative Example: Analyte Source Detection –Assume there is an analyte, which is a chemical secretion in a cellular grid. We want to know “where” the chemical is originating from. –Intuitively, we can see that if a chemical is secreted from a point, its concentration is “highest” in the region around the center and decreases as we move away from the origin.

31. 31 Signal Processing Source S (say HSL) is recognized by 4 Colored Reporters: BFP, GFP, YFP and RFP. BFP: S conc (1 – 0.8) GFP: S conc (0.8 – 0.7) YFP: S conc (0.7 – 0.5) RFP: S conc (< 0.5) Notes: Spread the environment with Reporter Proteins which can detect pre- specified chemical concentrations For a specified concentration range, these cells will fluoresce in a ring pattern around the source. When detecting multiple ranges, as above, each ring represents a different analyte concentration forming a bullseye pattern.

32. 32 Signal Processing Circuit Explained: Analyte Detection Component: Detects HSL presence and transcribes mRNA XY to Proteins X and Y Low Threshold Component: Upon *high* HSL and *high* X input, Z gets suppressed. High Threshold Component: Upon *high* HSL, *high* Y and *low* W input, high Z O/P obtained Negating Component: The net difference of O/P concentrations of Z from Low and High Threshold components eventually determines the net concentration of Z and GFP. GFP Z conc Z W ~ Z X depends on

33. 33 Closing Notes Goal –To create synthetic gene networks for modifying and extending the behavior of living organisms Progress to date: –Characterization and assembly of a genetic component library –Successful implementation of prototype circuits –Circuit design strategies such as Rational Design and Directed Evolution –Simulation of cell-cell communication and signal processing Challenges: –Inability to devise models and perform simulations that can *accurately* predict outcome of genetic networks –Overcome constraining factors such as unreliable computing elements, noise and imperfect communication.

34. 34 That’s it for today! Questions?