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

▶
synthetic biology a new application area for design
SMART_READER_LITE
LIVE PREVIEW

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

Dec 08, 2022 333 likes •711 views

Synthetic Biology: A New Application Area for Design Automation Research Chris Myers 1 , Nathan Barker 2 , Hiroyuki Kuwahara 3 , Curtis Madsen 1 , Nam Nguyen 4 , Chris Winstead 5 1 University of Utah 2 Southern Utah University 3 Microsoft Research,

slide-1
SLIDE 1

Synthetic Biology: A New Application Area for Design Automation Research

Chris Myers1, Nathan Barker2, Hiroyuki Kuwahara3, Curtis Madsen1, Nam Nguyen4, Chris Winstead5

1University of Utah 2Southern Utah University 3Microsoft Research, Trento, Italy 4University of Texas at Austin 5Utah State University

NSF EDA Workshop July 8, 2009

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-2
SLIDE 2

Synthetic Biology

Increasing number of labs are designing more ambitious and mission critical synthetic biology projects. These projects construct synthetic genetic circuits from DNA. These synthetic genetic circuits can potentially result in:

More efficient pathways for the production of antimalarial drugs (Dae et al.). Bacteria that can metabolize toxic chemicals (Brazil et al.). Bacteria that can hunt and kill tumors (Anderson et al.).

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-3
SLIDE 3

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:

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-4
SLIDE 4

Genetic Design Automation (GDA)

Electronic Design Automation (EDA) tools have facilitated the design of ever 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). Existing GDA tools require biologists to design at the molecular level. Roughly equivalent to designing electronic circuits at the layout level. Analysis of genetic circuits is also performed at this very low level. A GDA tool that supports higher levels of abstraction is essential.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-5
SLIDE 5

Adventures in Synthetic Biology

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-6
SLIDE 6

A Genetic Not Gate

C

P1

A

P1 C A

c

C A

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-7
SLIDE 7

A Genetic Nor Gate

B C

P1 P1

A

C A B P1

c

B A C

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-8
SLIDE 8

A Genetic Nand Gate

A B C

P1 P2

C B C A P1 P2

c c

C A B

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-9
SLIDE 9

Genetic Circuit versus Molecular Representation

CI CII Pre Pr

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-10
SLIDE 10

Final Molecular Model After Abstraction

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-11
SLIDE 11

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.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-12
SLIDE 12

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-13
SLIDE 13

Rainbow and CC

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-14
SLIDE 14

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.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-15
SLIDE 15

iBioSim: Genetic Circuit Editor

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-16
SLIDE 16

iBioSim: SBML Editor

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-17
SLIDE 17

iBioSim: ODE Analysis

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-18
SLIDE 18

iBioSim: ODE Simulation Results

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-19
SLIDE 19

iBioSim: Gillespie Analysis

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-20
SLIDE 20

iBioSim: Stochastic Simulation Results

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-21
SLIDE 21

Genetic Muller C-Element

C

C

A B

A B C’ 1 C 1 C 1 1 1

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-22
SLIDE 22

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-23
SLIDE 23

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-24
SLIDE 24

Toggle Switch C-Element (SBML)

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-25
SLIDE 25

Toggle Switch C-Element (Abstracted)

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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-26
SLIDE 26

Toggle Switch C-Element (Simulation)

Simulation time improved from 312 seconds to 20 seconds.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-27
SLIDE 27

Application: Bacterial Consensus

One interesting application is designing 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. C-elements behave unreliably (i.e., have probability of switching state).

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-28
SLIDE 28

Quorum Trigger Circuit

Env Complex LuxR LuxR LuxI 3OC6HSL medium

Env

OR

Complex

AND OR

→ LuxI HSL(in) → LuxR HSL(out)

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-29
SLIDE 29

Application: 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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-30
SLIDE 30

Application: 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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-31
SLIDE 31

Application: 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

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-32
SLIDE 32

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. We plan to adapt asynchronous tools to genetic circuit technology.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-33
SLIDE 33

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.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-34
SLIDE 34

More Information

1st International Workshop on Bio-Design Automation July 27th in San Francisco at DAC. 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/

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-35
SLIDE 35

Engineering Genetic Circuits

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009

slide-36
SLIDE 36

Acknowledgments

Nathan Barker Hiroyuki Kuwahara Nam Nguyen Curtis Madsen Chris Winstead

This work is supported by the National Science Foundation under Grants No. 0331270 and CCF07377655.

  • C. Myers et al. (U. of Utah)

Synthetic Biology July 8, 2009