“Transforming Cells into Automata” “ Index-based search of single s equences ”

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

2. Transforming Cells Into Automata Ravi Tiruvury

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. 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. 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. EE vs Bio Genetic Circuits Electrical 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. 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. Before we dive in… Protein RNAp mRNA Fluorescent Protein DNA Promoter Reporter Gene • 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.

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

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

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

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

13. More Gates - NAND AND through NAND + NOT

14. “Celebrating Cells” - A fun circuit! P1 P3 R1 P2 R3 R2 1 1 1 • Idea: • Each Promoter-Repressor {Pi, Ri} set is an inverter • R1 represses P2, R2 represses P3, and R3 represses P1 • So of R1 is ↑ then R2 is ↓ and R3 is ↑

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. 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. 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 = [x1, … xn] includes concentrations of proteins, mRNAs, other molecules and fi is a nonlinear function

18. Modeling an Inverter A mRNAA mRNAZ A2 PZ 1. I/P mRNA to I/P Protein (A) Translation • 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. 2. I/P Protein Dimerization and Decay 3. Cooperative Binding of I/P Protein ODE for simulating promoter PZ bound by dimer A2 4. Transcription 5. O/P mRNA Degradation

19. Inverter – Dynamic Behavior

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. 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. Revisiting the Inverter! Possibilities: P1: I2: P2: R2: P3: R3: P1: I2: P2: R2: P3: R3: PROBLEM!! Here, even for low R3 levels, YFP is 0 as R3 is a very efficient repressor even at low concentrations.

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. 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. Var1 Var2 DNA Desired Outcome Var3 Var4

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 Autoinducerinto the cells’ surroundings. • The Autoinducer permeates the cells, and its concentration keeps increasing as the cell grows.

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. VAI VAI Cell-Cell Communication Demystified Quorum Sensing constructs from Vibrio fischeri for communication in E. coli aTc • 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 luxPR promoter

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. luxR 30C6HSL luxR.30C6HSL lacI IPTG cI ~cI GFP 1 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 1 1 0 0 0 1 1 Communication in Multicellular systems

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. Signal Processing Source S (say HSL)is recognized by 4 Colored Reporters: BFP, GFP, YFP and RFP. BFP: Sconc (1 – 0.8) GFP: Sconc (0.8 – 0.7) YFP: Sconc (0.7 – 0.5) RFP: Sconc (< 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. Signal Processing ZW ~ ZX depends on Zconc depends on GFP • Circuit Explained: • Analyte Detection Component: Detects HSL presence and transcribes mRNAXY 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.

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. That’s it for today! Questions?