Synthetic Biology: A New Application Area for Design Automation - - PowerPoint PPT Presentation

Synthetic Biology: A New Application Area for Design Automation Research

Chris Myers

University of Utah

Carnegie Mellon University December 10, 2009

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

James Watson

Biology has at least 50 more interesting years (1984).

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Michael Samoilov and Adam Arkin

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Phage λ Virus

Release

• E. coli bacterial cell

Host chromosome Lysogeny Pathway Cell division Induction Lysis Pathway event Lysogeny Lysis Attachment Penetration Replication Assembly Phage λ

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Phage λ Decision Circuit

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Asynchronous Circuit?

McAdams/Shapiro, Science (1995)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Stochastic Circuit?

Arkin/Ross/McAdams, Genetics (1998)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Stochastic Asynchronous Circuit? N cIH CroH Cro cI cII cIII

0.2· kPL kPL kPL kPRM k5 k2 k6 kPRE k1 kPR k3 k4 kPR 0.5· kPR kPR

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Stochastic Asynchronous Circuit Results

1e-05 0.0001 0.001 0.01 0.1 1 0.1 1 10 100 Estimated Fraction of Lysogens Average Phage Input (API) Stochastic Asynchronous Circuit (starved) O- Experimental (starved) P- Experimental (starved) Master Eqn Simulation (starved)

SAC results generated in only 7 minutes. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Stochastic Asynchronous Circuit Results

1e-06 1e-05 0.0001 0.001 0.01 0.1 1 0.1 1 10 100 Estimated Fraction of Lysogens Average Phage Input (API) Stochastic Asynchronous Circuit (starved) O- Experimental (starved) P- Experimental (starved) Master Eqn Simulation (starved) Stochastic Asynchronous Circuit (well-fed) O- Experimental (well-fed)

SAC results generated in only 7 minutes. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Synthetic Biology

(From “Adventures in Synthetic Biology” - Endy et al.)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Engineering vs. Synthetic Biology

Genetic engineering (last 30 years):

Recombinant DNA - constructing artificial DNA through combinations. Polymerase Chain Reaction (PCR) - making many copies of this new DNA. Automated sequencing - checking the resulting DNA sequence.

Synthetic biology adds:

Standards - create repositories of parts that can be easily composed. Abstraction - high-level models to facilitate design. Automated construction - separate design from construction.

(source: Drew Endy)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Design Automation (GDA)

Standards, abstraction, and automated construction are the cornerstones

• f Electronic Design Automation (EDA).

EDA facilitates the design of more complex integrated circuits each year. Crucial to the success of synthetic biology is an improvement in methods and tools for Genetic Design Automation (GDA). Experiences with EDA can jump start the development of GDA.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Current State of GDA (Standards)

Registry of standard biological parts used to design synthetic genetic circuits (http://partsregistry.org). Adequate characterization of these parts is an ongoing effort. Systems Biology Markup Language (SBML) has been proposed as a standard representation for the simulation of biological systems. Many simulation tools have been developed that accept models in the SBML format (Copasi, Jarnac, CellDesigner, SimBiology, iBioSim, etc.).

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Current State of GDA (Abstraction)

Existing SBML-based GDA tools model biological systems at the molecular level. A typical SBML model is composed of a number of chemical species (i.e., proteins, genes, etc.) and reactions that transform these species. This is a very low level representation which is roughly equivalent to the layout level for electronic circuits. Designing and simulating genetic circuits at this level of detail is extremely tedious and time-consuming.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Current State of GDA (Automated Construction)

Several companies have formed that will construct a plasmid from an arbitrary DNA sequence. It is still difficult, however, to separate design and construction issues. To achieve this, a GDA tool that supports higher-levels of abstraction for modeling, analysis, and design of genetic circuits is essential.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Phage λ Decision Circuit

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Phage λ Decision Circuit

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription cI cII CI Dimer DNA

Pre OR OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Transcription CI Dimer DNA

Pre

Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Transcription CI Dimer DNA

Pre

Promoters Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA

Pre

Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA

Pre

Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA

Pre

Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

RNAP

Pr

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA

Pre

Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

RNAP

Pr

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA

Pre

Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein Translation CII Protein Operator Sites Transcription CI Dimer DNA

Pre

mRNA Promoters Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein Operator Sites CI Dimer DNA

Pre

mRNA Translation CII Protein Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein CI Dimer DNA

Pre

mRNA Translation CII Protein Operator Sites Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer CI Protein CI Dimer DNA

Pre

Activation mRNA Translation CII Protein Operator Sites Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer CI Dimer DNA

Pre

Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer DNA

Pre

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Degradation Repression Dimerization

Pr

CI Dimer DNA

Pre

CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

mRNA Translation Transcription CI Dimer DNA

Pre

CI Dimer Activation CI Protein CII Protein Operator Sites Promoters Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Degradation Dimerization Repression

Pr

Activation CI Protein mRNA Translation CII Protein Transcription CI Dimer DNA

Pre

CI Dimer Operator Sites Promoters Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuits

RNAP RNAP RNAP RNAP RNAP

Repression Degradation Dimerization

Pr

CI Dimer Activation CI Protein Transcription CI Dimer DNA

Pre

mRNA Translation CII Protein Operator Sites Promoters Genes

OR

cI cII

OE

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Francis Crick

DNA makes RNA, RNA makes protein, and proteins make us.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Johann Von Neumann

The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed

• phenomena. The justification of such a

mathematical construct is solely and precisely that it is expected to work.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Scott Adams

There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards, or crystal balls. Collectively, these methods are known as “nutty methods.” Or you can put well-researched facts into sophisticated computer models, more commonly referred to as “a complete waste of time.”

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Circuit Model (GCM)

Provides a higher level of abstraction than SBML. Includes only important species and their influences upon each other. GCMs also include structural constructs that allow us to connect GCMs for separate modules through species ports.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

A Genetic Not Gate

C

P1

A

P1 C A

c

C A

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

A Genetic Nor Gate

B C

P1 P1

A

C A B P1

c

B A C

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

A Genetic Nand Gate

A B C

P1 P2

C B C A P1 P2

c c

C A B

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

A Genetic Oscillator

CI CII Pre Pr

Pr

CI Dimer

Pre

CI Dimer CI Protein CII Protein

OR

cI cII

OE

CI CII

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SBML: Main Elements

Species Global parameters (ex. k1=0.1) Reactions

Reactants Products Modifiers Stoichiometry Reversible Kinetic laws

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Synthesizing SBML from a GCM Representation

Create degradation reactions Create open complex formation reactions Create dimerization reactions Create repression reactions Create activation reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

GCM Example

CI CII Pre Pr

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Degradation Reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Open Complex Formation Reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Dimerization Reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Repression Reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Activation Reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Complete SBML Model

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Classical Chemical Kinetics

Uses ordinary differential equations (ODE) to represent the system to be analyzed, and it assumes:

Molecule counts are high, so concentrations can be continuous variables. Reactions occur continuously and deterministically.

Genetic circuits have:

Small molecule counts which must be considered as discrete variables. Gene expression reactions that occur sporadically.

ODEs do not capture non-deterministic behavior.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Richard Feynmann

A philosopher once said “It is necessary for the very existence of science that the same conditions always produce the same results.” Well, they do

• not. You set up the circumstances, with

the same conditions every time, and you cannot predict behind which hole you will see the electron.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

NYTimes: Expressing Our Individuality, the Way E. Coli Do

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Rainbow and CC

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Stochastic Chemical Kinetics

To more accurately predict the temporal behavior of genetic circuits, stochastic chemical kinetics formalism can be used. Use Gillespie’s Stochastic Simulation Algorithm which tracks the quantities of each molecular species and treats each reaction as a separate random event. Only practical for small systems with no major time-scale separations. Abstraction is essential for efficient analysis of any realistic system.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Automatic Abstraction

Reaction Model

• Reaction-based

Abstraction Abstracted Reaction Model

• State-based

Abstraction SAC Model

• Markov

Chain Analysis

• Stochastic

Simulation Results

Begins with a reaction-based model in SBML. Automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit (SAC) model. Can now utilize Markov chain analysis. Kuwahara et al., Trans. on Comp. Sys. Bio. (2006)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Dimerization Reduction

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Dimerization Reduction

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Operator Site Reduction (PR)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Operator Site Reduction (PR)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Operator Site Reduction (PRE)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Operator Site Reduction (PRE)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Similar Reaction Combination

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Modifier Constant Propagation

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Final SBML Model

10 species and 10 reactions reduced to 2 species and 4 reactions

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

GCM Advantages

Greatly increases the speed of model development and reduces the number of errors in the resulting models. Allows efficient exploration of the effects of parameter variation. Constrains SBML model such that it can be more easily abstracted resulting in substantial improvement in simulation time.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

iBioSim: The Intelligent Biological Simulator

Project management support. GCM Editor - creates Genetic Circuit Models (GCM). SBML Editor - creates models using the Systems Biology Markup Language (SBML).

reb2sac - abstraction-based ODE, Monte Carlo, and Markov analysis.

TSD Graph Editor - visualizes time series data (TSD). Probability Graph Editor - visualizes probability data.

GeneNet - learns GCMs from TSD.

Myers et al., Bioinformatics (2009)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

iBioSim: Genetic Circuit Editor

Myers et al., Bioinformatics (2009)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

iBioSim: SBML Editor

Myers et al., Bioinformatics (2009)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

iBioSim: Analysis Engine

Myers et al., Bioinformatics (2009)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

ODE Results for the Simple Genetic Oscillator

Comparison of ODE to SSA Results

CI_total (ODE)

500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000

Time (s)

5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5 5 0 5 5 6 0 6 5

Number of molecules

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

SSA Results for the Simple Genetic Oscillator

Comparison of ODE to SSA Results

CI_total (ODE) CI_total (SSA)

500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000

Time (s)

1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 100 110 120 130 140 150

Number of molecules

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic Muller C-Element

C

C

A B

A B C 1 C 1 C 1 1 1

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Toggle Switch C-Element (Genetic Circuit)

B A E D F B A X Y Z C

Q S R P1 P2 P3 P7 P8 P4 P5 P6 X X Y A B E D F Z F D C Y Z E x d e x y f f z c y z

Nguyen et al., 13th Symposium on Async. Ckts. & Sys., 2007 (best paper) Nguyen et al., to appear in the Journal of Theoretical Biology

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Toggle Switch C-Element (GCM)

P1 P2 P3 P7 P8 P4 P5 P6 X X Y A B E D F Z F D C Y Z E x d e x y f f z c y z

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Toggle Switch C-Element (SBML)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Toggle Switch C-Element (Abstracted)

Reduced from 34 species and 31 reactions to 9 species and 15 reactions.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Toggle Switch C-Element (Simulation)

Simulation time improved from 312 seconds to 20 seconds.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Majority Gate C-Element (Genetic Circuit)

E C X Y Z D B A

P8 P7 P5 P6 P4 P3 P2 P1 A B X D D Y C E D D Y Z Z X x y d d e c d y z z x

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Speed-Independent C-Element (Genetic Circuit)

A B S1 S2 S3 S4 C

Z P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 S4 S4 X S1 S3 S2 S4 A C S3 S2 S4 B S2 Z S3 Y S1 x s4 y s4 z s3 s1 s2 s3 s1 s2 z c s4

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Genetic C-element Failures

20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0

? Stable Unstable Stable

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Comparison of Failure Rates for the C-element Designs

0.005 0.01 0.015 0.02 500 1000 1500 2000

Failure Rate Time (s) Failure Rate for Each C-Element Design

Majority Gate (High to Low) Speed-Independent (High to Low) Toggle Switch (High to Low) Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Effects of Decay Rates on Failure Rates

0.05 0.1 0.15 0.2 0.25 0.004 0.006 0.008 0.01 0.012 0.014

Failure Rate Decay Rate Failure Rate Versus Decay Rate (Toggle Switch C-element)

High to Low Low to High Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Effects of Decay Rates on Switching Time

400 600 800 1000 1200 1400 1600 1800 2000 0.004 0.006 0.008 0.01 0.012 0.014

Switching Time (s) Decay Rate Switching Time Versus Decay Rate (Toggle Switch C-element)

High Low Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Application: Bacterial Consensus

One interesting application for synthetic biology is the design of bacteria that can hunt and kill tumor cells (Anderson et al.). Care must be taken in determining when to attack potential tumor cells. Can use a genetic Muller C-element and a bacterial consensus mechanism known as quorum sensing. C-element combines a noisy environmental trigger signal and a density dependent quorum sensing signal. Activated bacteria signal their neighbors to reach consensus.

Env E Detect (error rate ε) Muller C-element (state error rate δ) Concentration Threshold A Action cell boundary

Winstead et al., IBE Conference (2008)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Confidence Amplifier

A noisy C-element with a confidence-feedback loop:

A

C

E

The output “rails” to maximum confidence, even if E has low confidence. This configuration only works if the C-element is “noisy”. Otherwise, the circuit is permanently stuck in its initial state.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

Inactive Trigger Circuits

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

Env signal applied (HSL concentration low)

Env

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

One circuit randomly activates (HSL concentration increases)

Env

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

More circuits activate due to HSL (HSL concentration increases sharply)

Env

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

Avalanche effect: most cells activate (HSL concentration saturates)

Env

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

Env signal is removed. (Circuits stay active)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Population Dynamics

Time passes. (Circuits randomly switch off)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Simulation Results

500 1000 1500 2000 2500 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Time Steps Probability Probability of Toggle gate stimuli, E=0.005000 Environmental Trigger Consensus Activator

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Simulation Results

500 1000 1500 2000 2500 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Time Steps Probability Probability of Toggle gate stimuli, E=0.050000 Environmental Trigger Consensus Activator

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Simulation Results

500 1000 1500 2000 2500 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Time Steps Probability Probability of Toggle gate stimuli, E=0.000000 Environmental Trigger Consensus Activator

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Design

Env Complex LuxR LuxR LuxI 3OC6HSL medium

Env

OR

Complex

AND OR

→ LuxI HSL(in) → LuxR HSL(out)

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Quorum Trigger Design

Env Complex LuxR LuxR LuxI 3OC6HSL medium

lacI+pL RBS luxR lux pR RBS GFP RBS luxR RBS luxI

• luxR
• GFP
• luxI
• luxR

R0011 B0034 C0062 B0015 R0062 B0034 E0040 B0034 C0062 B0034 C0061 B0015 F2622 K116634

• Chris Myers (U. of Utah)

Synthetic Biology Carnegie Mellon University

Quorum Trigger Design

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Future GDA Research Directions

Genetic circuits have no signal isolation. Circuit products may interfere with each other and host cell. Gates in a genetic circuit library usually can only be used once. Behavior of circuits are non-deterministic in nature. No global clock, so timing is difficult to characterize. To address these challenges, we are investigating soft logic models based on factor graphs and adapting asynchronous synthesis tools to a genetic circuit technology.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Biologically Inspired Circuit Design

Human inner ear performs the equivalent of one billion floating point

• perations per second and consumes only 14 µW while a game console

with similar performance burns about 50 W (Sarpeshkar, 2006). We believe this difference is due to over designing components in order to achieve an extremely low probability of failure in every device. Future silicon and nano-devices will be much less reliable. For Moore’s law to continue, future design methods should support the design of reliable systems using unreliable components. Biological systems constructed from very noisy and unreliable devices. GDA tools may be useful for future integrated circuit technologies.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Adam Arkin

Since the engineering principles by which such circuitry is constructed in cells comprise a super-set of that used in electrical engineering, it is, in turn, possible that we will learn more about how to design asynchronous, robust electronic circuitry as well.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

More Information

Linux/Windows/Mac versions of iBioSim are freely available from:

http://www.async.ece.utah.edu/iBioSim/

Publications:

http://www.async.ece.utah.edu/publications/

Course materials:

http://www.async.ece.utah.edu/∼myers/ece6760/ http://www.async.ece.utah.edu/∼myers/math6790/

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Engineering Genetic Circuits

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University

Acknowledgments

Nathan Barker Keven Jones Hiroyuki Kuwahara Curtis Madsen Nam Nguyen Chris Winstead

This work is supported by the National Science Foundation under Grants No. 0331270, CCF-07377655, and CCF-0916042.

Chris Myers (U. of Utah) Synthetic Biology Carnegie Mellon University