Design Principles in Synthetic Biology Chris Myers 1 , Nathan Barker - - PowerPoint PPT Presentation
Design Principles in Synthetic Biology Chris Myers 1 , Nathan Barker 2 , Hiroyuki Kuwahara 3 , Curtis Madsen 1 , Nam Nguyen 1 , Michael Samoilov 4 , and Adam Arkin 4 1 University of Utah 2 Southern Utah University 3 Microsoft Research, Trento, Italy
Chris Myers1, Nathan Barker2, Hiroyuki Kuwahara3, Curtis Madsen1, Nam Nguyen1, Michael Samoilov4, and Adam Arkin4
1University of Utah 2Southern Utah University 3Microsoft Research, Trento, Italy 4University of California, Berkeley
Design Principles in Biological Systems April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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:
A better understanding of how microorganisms function by examining differences in vivo compared to in silico (Sprinzak/Elowitz). 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.).
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
This talk describes our research to develop such a GDA tool. This tool has helped us examine design principles for synthetic biology. As a case study, will describe the design of a genetic Muller C-element.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
MIT has created a registry of standard biological parts used to design synthetic genetic circuits (http://parts.mit.edu). Methods and tools are needed to assist in the design and analysis of synthetic genetic circuits using these parts. BioJADE provides a schematic capture interface to the MIT parts registry. 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 (BioPathwise, BioSPICE, CellDesigner, SimBiology, etc.).
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
SBML models 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. Therefore, there is a need for higher-level abstractions for modeling, analysis, and design of genetic circuits.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Genetic Circuit
Host
Experiments
Plasmid
Data
Knowledge
Sequence
GCM Synthesis
Data Abstraction/ Simulation
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Genetic Circuit
Host
Experiments
Plasmid
Data
Knowledge
Sequence
GCM Synthesis
Data Abstraction/ Simulation
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Genetic Circuit
Host
Experiments
Plasmid
Data
Knowledge
Sequence
GCM Synthesis
Data Abstraction/ Simulation
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Genetic Circuit
Host
Experiments
Plasmid
Data
Knowledge
Sequence
GCM Synthesis
Data Abstraction/ Simulation
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Genetic Circuit
Host
Experiments
Plasmid
Data
Knowledge
Sequence
GCM Synthesis
Data Abstraction/ Simulation
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription cI cII CI Dimer DNA
Pre OR OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Transcription CI Dimer DNA
Pre
Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Transcription CI Dimer DNA
Pre
Promoters Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP
RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA
Pre
Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP
RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA
Pre
Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP
RNAP CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA
Pre
Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP
RNAP
Pr
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA
Pre
Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP
RNAP
Pr
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites CI Dimer DNA
Pre
Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein Translation CII Protein Operator Sites Transcription CI Dimer DNA
Pre
mRNA Promoters Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein Operator Sites CI Dimer DNA
Pre
mRNA Translation CII Protein Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein CI Dimer DNA
Pre
mRNA Translation CII Protein Operator Sites Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer CI Protein CI Dimer DNA
Pre
Activation mRNA Translation CII Protein Operator Sites Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer CI Dimer DNA
Pre
Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer DNA
Pre
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer DNA
Pre
CI Dimer Activation CI Protein mRNA Translation CII Protein Operator Sites Promoters Genes Transcription
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
mRNA Translation Transcription CI Dimer DNA
Pre
CI Dimer Activation CI Protein CII Protein Operator Sites Promoters Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
Activation CI Protein mRNA Translation CII Protein Transcription CI Dimer DNA
Pre
CI Dimer Operator Sites Promoters Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
RNAP RNAP RNAP RNAP RNAP
Pr
CI Dimer Activation CI Protein Transcription CI Dimer DNA
Pre
mRNA Translation CII Protein Operator Sites Promoters Genes
OR
cI cII
OE
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Provides a higher level of abstraction than SBML. Includes only important species and their influences upon each other. A GCM is a tuple S,P,G,I,Sd where:
S is a finite set of species; P is a finite set of promoters; G : P → 2S maps promoters to sets of species; I ⊆ S × P ×{a,r} is a finite set of influences; Sd ⊆ S is a set of species that influence as dimers.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
A bipartite graph with species and promoters as the two types of nodes. Species are connected to promoters using influences I, and promoters are connected to species using function G. To simplify presentation, graphs shown using only species as nodes, edges are inferred using I and G, and edges are labeled with the promoter that links the species.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
C A B P1
c
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
C A B P1
c
B A C
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
C B C A P1 P2
c c
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
C B C A P1 P2
c c
C A B
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Parameter Sym Structure Value Units Initial species count ns species molecule Dimerization equilibrium Kd species .05
1 molecule
Degradation rate kd species .0075
1 sec
Initial promoter count ng promoter 2 molecule Stoichiometry of production np promoter 10 molecule Degree of cooperativity nc promoter 2 molecule RNAP binding equilibrium Ko promoter .033
1 molecule
Open complex production rate ko promoter .05
1 sec
Basal production rate kb promoter .0001
1 sec
Activated production rate ka promoter .25
1 sec
Repression binding equilibrium Kr influence .5
1 moleculenc
Activation binding equilibrium Ka influence .0033
1 molecule(nc+1)
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Create degradation reactions Create open complex formation reactions Create dimerization reactions Create repression reactions Create activation reactions
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Uses ordinary differential equations (ODE) to represent the system to be analyzed, and it assumes:
A system is well-stirred. Number of molecules in a cell is high. Concentrations can be viewed as continuous variables. Reactions occur continuously and deterministically.
Genetic circuits involve small molecule counts. Gene expression can have substantial fluctuations. ODEs do not capture non-deterministic behavior.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
To more accurately predict the temporal behavior of genetic circuits, stochastic chemical kinetics formalism can be used. Probabilistically predicts the dynamics of biochemical systems. Describes the time evolution of a system as a discrete-state jump Markov process governed by the chemical master equation (CME). Can simulate it using Gillespie’s Stochastic Simulation Algorithm (SSA). It exactly 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.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reaction Model
Abstraction Abstracted Reaction Model
Abstraction SAC Model
Chain Analysis
Simulation Results
Begins with a reaction-based model in SBML. Next, it automatically abstracts this model leveraging the quasi-steady state assumption, whenever possible. Finally, it encodes chemical species concentrations into Boolean (or n-ary) levels to produce a stochastic asynchronous circuit model. It can now be analyzed using Markov chain analysis.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
10 species and 10 reactions reduced to 2 species and 4 reactions
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
http://www.async.ece.utah.edu/BioSim/
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
http://www.async.ece.utah.edu/BioSim/
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
http://www.async.ece.utah.edu/BioSim/
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
http://www.async.ece.utah.edu/BioSim/
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
http://www.async.ece.utah.edu/BioSim/
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
B A C’ A B C’ 1 C 1 C 1 1 1
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Reduced from 34 species and 31 reactions to 9 species and 15 reactions.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Simulation time improved from 312 seconds to 20 seconds.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
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
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Use Law of Mass Action to derive an ODE model. Study behavior of our model at steady state. Analyze nullclines to characterize the gate.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs low Z Y dY=0 dZ=0
Stable
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0 20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Stable Unstable Stable
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs High Z Y dY=0 dZ=0
Stable
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0 20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Stable Unstable Stable
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
500 1000 1500 2000 0.005 0.01 0.015 0.02 0.025 0.03 Low to High Time (s) Failure Rate maj−heat−high maj−light−high tog−heat−high tog−light−high si−heat−high si−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
500 1000 1500 2000 0.005 0.01 0.015 0.02 0.025 0.03 High to Low Time (s) Failure Rate maj−heat−low maj−light−low tog−heat−low tog−light−low si−heat−low si−light−low
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
1 1.5 2 2.5 3 3.5 4 4.5 5 0.05 0.1 0.15 0.2 0.25 Low to High Number of Genes Failure Rate maj−heat−high maj−light−high tog−heat−high tog−light−high si−heat−high si−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
1 1.5 2 2.5 3 3.5 4 4.5 5 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Low to High Cooperativity Failure Rate maj−heat−high maj−light−high tog−heat−high tog−light−high si−heat−high si−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
10
−1
10 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Low to High Repression Failure Rate maj−heat−high maj−light−high tog−heat−high tog−light−high si−heat−high si−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
0.005 0.01 0.015 0.02 0.025 0.03 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Low to High Decay Rate Failure Rate maj−heat−high maj−light−high tog−heat−high tog−light−high si−heat−high si−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
500 1000 1500 2000 0.005 0.01 0.015 0.02 0.025 0.03 Low to High Time (s) Failure Rate single−tog−heat−high single−tog−light−high dual−tog−heat−high dual−tog−light−high
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
20 40 60 80 100 120 20 40 60 80 100 120 Toggle, Inputs Mixed Z Y dY=0 dZ=0
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
500 1000 1500 2000 0.05 0.1 0.15 0.2 0.25 High to Low Time (s) Failure Rate single−tog−heat−low single−tog−light−low dual−tog−heat−low dual−tog−light−low
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Speed-independence does not necessarily imply better robustness. Higher gene counts improve production rates, higher equilibrium values, and more robust operation. Cooperativity of at least two is required to produce the necessary non-linearity for state-holding. Repressors should bind efficiently. Decay rates cannot be too high. Dual-rail outputs are essential.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
More levels of hierarchy are needed in the GCM format. We plan to create structural constructs that allow us to connect GCM’s for separate modules through species ports. Allow design at the logical and higher levels of abstraction.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Human inner ear performs the equivalent of one billion floating point
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. Biological systems tend to be more asynchronous and analog in nature, so future engineered circuits will likely need to be also.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008
Nathan Barker Hiroyuki Kuwahara Nam Nguyen Curtis Madsen Michael Samoilov Adam Arkin
This work is supported by the National Science Foundation under Grants No. 0331270 and CCF07377655.
Design Principles in Synthetic Biology IMA Workshop / April 24, 2008