Feedback and Control in Biological Circuit Design
Richard M. Murray Control & Dynamical Systems / Bioengineering
California Institute of Technology !
Synthetic Biology and Control Engineering Workshop Oxford, 10 September 2014
Outline • Philosophy and approach • Results and examples • Challenges and questions
Funding: Army Research Office (ARO), National Science Foundation (NSF), Office of Naval Research (ONR), Defense Advanced Research Projects Agency (DARPA), Albert and Mary Yu Foundation, Gordon and Betty Moore Foundation, Air Force Office of Scientific Research (AFOSR)
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Principles of Control TheoryBasic idea: sensing, actuation + computation
• Interconnection of the output of a system to its input via a control law (algorithm)
• Goals: stability, performance, robustness P1: Robustness to Uncertainty thru Feedback
• Allows high performance in the presence of uncertainty
• Key idea: accurate sensing to compare actual to desired, correction thru computation and actuation
P2: Design of Dynamics through Feedback • Feedback allows dynamics of system to be modified;
closed loop behavior can be different than open loop
P3 (corollary): Modularity through Feedback • By designing dynamics that are robust with respect to
interconnection, we get modularity in design
Control involves (computable) tradeoffs between robustness and performance
• Fundamental limits (eg, Bode integral formula) limit what can be achieved
• Typically requires accurate sensing at some point in loop
2
Sense
Compute
Actuate
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Example: AM Radios
http://www.roetta.it/ik3hia
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Minimal radio (1906) • Crystal radio w/
antenna, diode, earphone
• 6 components, but not very robust…
Basic radio • Add feedback amplifier
stages
• 2 transistors, 5 resis-tors, 5 capacitors, antenna, speaker
• More robust…
“Modern” radio (1954) • Add auto gain
control, power supply buffering, tempera-ture compensation…
• 30-50 components • Robust enough to sell
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Synthetic Biology ApplicationsMaterials Synthesis
• Conversion of input resources to output products in modular way
• Control systems to regu-late metabolic pathways and stress response
• Provide modularity and robustness, with ability to rapidly redesign pathway for new input/output pairs
Event Detectors
• Component technologies: signal detection, event memory, species com-parison, logic functions
• Event detectors: A > B, A followed by B, A > thresh
• Interconnection framework: modular techniques for interconnecting compo-nents and detectors
Artificial Cells
• Self-contained nanoscale biomolecular machines
• Subsystems: chassis, power supply, sensing (internal and external), actuation, regulation
• All components should be synthetic and pro-grammed (compiled)
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Living Foundries: The Vision
Chemicals
Fuels
Pharma
DNA instruction set
PolymersCatalysts Electronic/ optical materials
Multi-cellular constructsSelf-repairing systems“cell-free” systems
Approved for Public Release, Distribution Unlimited 7
AAGCAGTATCATCGACCCAGTAATATAGCAGACCGTTAGATAC…
Genome design Synthesis
Sugar
Cellulose
PET
Coal
Molecules
Custom, distributed, on-demand manufacturing
Natural gas
“cell-like”factory
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Analysis & Design of Biomolecular Feedback Systems
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Circuit Theory • Role of feedback • System identification • Effects of crosstalk
Cell-Free Prototyping • RNA-based genelets • Biomolecular breadboards • Droplet-based automation
Biocircuit Design • Fast feedback mechanisms • Heterogeneous redundancy
• Systematic design methods
SH3
PTaz
LZB
RFP
LZb
CpxR LZbSH3GFP
Pcon HK Pcon RR PCpxR Anti-scaffold
PReference Scaffold
Richard M. Murray, Caltech CDS/BEUIUC, Apr 2012
Genelet Circuits: In Vitro Rate RegulatorIdea for a circuit: produce two chemicals at same rates • Common operation for metabolic networks - maintain stoichiometry • Implemented using in vitro technology (test tubes instead of cells) !!!!!!!!!!
Molecular programming for in vitro systems • Exploit Watson-Crick base pair binding (A-T, C-G) • Can “compile” functional specifications into RNA and DNA sequences • Circuits are biocompatible ⇒ some hope of embedding into cells
6
Ti
Franco and M, CDC 2008 Franco et al, ACS Syn Bio, 2014
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Genelet-Based CircuitsRate regulator circuit (ACS Syn Bio, 2014)
Genelet-based Insulation (PNAS, 2012)
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T1 > T2 T1 = T2
T1 > T2[R1]
[R2]1 2 3 4 5 6 7 8
50
100
nM
Hours
Franco et al, ACS Syn Bio, 2014!Franco et al, PNAS, 2012
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014 Richard M. Murray, Caltech CDS/BEUIUC, Apr 2012 Richard M. Murray, Caltech CDS/BEGloverFest, 23 Sep 2013
Concentration Regulation via Scaffold Proteins
Circuit operation • Use scaffold domains to
modulate activity of histidine kinase and response regulator proteins
• Use anti-scaffold feedback to modulate activity level and regulate con-centration of output to match input
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Hsiao and de los Santso et al, ACS Synthetic Biology 2014
Output
Input
0.5 1 1.5 2 2.5 3 3.5x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2 x 104
Ara0Ara0.0001Ara0.001Ara0.01
0.5 1 1.5 2 2.5 3 3.5x 104
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2 x 104
Ara0Ara0.0001Ara0.001Ara0.01
Scaffold Expression (RFP/OD, a.u)
Anti-
scaff
old
Expr
essi
on (G
FP/O
D, a
.u)
Scaffold Expression (RFP/OD, a.u)
Anti-
scaff
old
Expr
essi
on (G
FP/O
D, a
.u)
WW62 Closed Loop ( - CusS phosphatase)
BW27783 Closed Loop ( + CusS phosphatase)
0 2 4 6 8 10 120
50
100
150
200
250
300
Time(hrs)
aTc
(nM
)
20130905eVH38− No Inductionn=5
aTc Induction
0 2 4 6 8 10 120
50
100
150
200
250
300
350
Time(hrs)
Inte
nsity
/Tot
alAr
ea
Average Fluorescence / Pixel
RFPYFP
0 2 4 6 8 10 120
50
100
150
200
250
300
Time(hrs)
aTc
(nM
)
20130905eVH38− 1 Step Inductionn=5
aTc Induction
0 2 4 6 8 10 120
50
100
150
200
250
300
350
Time(hrs)
Inte
nsity
/Tot
alAr
ea
Average Fluorescence / Pixel
RFPYFP
0 2 4 6 8 10 120
50
100
150
200
250
300
Time(hrs)aT
c (n
M)
20130905eVH38− Two Step Inductionn=5
aTc Induction
0 2 4 6 8 10 120
50
100
150
200
250
300
350
Time(hrs)
Inte
nsity
/Tot
alAr
ea
Average Fluorescence / Pixel
RFPYFP
0 2 4 6 8 10 120
50
100
150
200
250
300
Time(hrs)
aTc
(nM
)
20130905eVH38− Three Step Inductionn=5
aTc Induction
0 2 4 6 8 10 120
50
100
150
200
250
300
350
Time(hrs)
Inte
nsity
/Tot
alAr
ea
Average Fluorescence / Pixel
RFPYFP
Richard M. Murray, Caltech CDS/BEUIUC, Apr 2012 Richard M. Murray, Caltech CDS/BBESB 6.0, 9 Jul 2013
Key characteristics of the cell-free breadboard (Noireaux et al) • Inexpensive and fast: ~$0.03/ul ⇒ $0.30/
expt; typical reactions run for 4-6 hours • Easy to use: works with many plasmids or
linear DNA (PCR products) - Can adjust concentration to explore copy
number/expression strength quickly • Flexible environment: adjust energy level,
pH, temperature, degradation • Many mechanisms being tested:
Event detector
• Add’l proteases, RNAses, etc
• Modulate pH, ATP, etc
• Vary component concentrat’n
• Extract: cytoplasmic proteins
• Amino acid mix
• Buffer + NTP, RNAP, etc
TX TL
TX TL
vesi
cle
orig
ami
Implementation iterations (slow)
Cell-Free Biomolecular Breadboards
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Model
Prototyping Debugging
System Model
Model
Design iterations (fast)
Model
http://www.openwetware.org/wiki/breadboards
Sun et al, JoVE 2013!Sun et al, ACS Syn Bio, 2013 (s)
PI (+ contact) Circuit/Technology 1 2 3Lucks (CH) RNA-sensing TFs ✓✓✓
Del Vecchio (EY) Loading effects ✓✓✓Temme (VH) Orthogonal RNAPs ✓? -
Voigt (DSG) 4 input, 11 gene ✓x -
Tabor (JK) Green light sensor ✓?○
Endy (VH) DNA memory ✓○ -
Del Vecchio (SG) Phospho-insulator ✓✓✓
Kortemme (EdlS) Molecular sensors ✓○ -
Jewett (YW) Butanediol pathway ✓✓○
Richard M. Murray, Caltech CDS/BERice, 10 Mar 2014
Resource Limits (in TX-TL)
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Siegal-Gaskins, Kim, Tuza, Noireaux, M, ACS Syn Bio, 2014
Limited capacity affects performance • Saturate transcriptional and/or
translational machinery
Richard M. Murray, Caltech CDS/BECSHL, 28 Jul 2014
Genetic Context (in TX-TL)
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Enoch Yeung: [email protected]
Effect of intergenic spacing length
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500bp
400bp
300bp
200bp100bp
1 mM
100 µM IPTG
10 µM
PLacPTet
Convergent Orientation
Divergent Orientation
Tandem Orientation
Plasmid DNA Linear DNA Spacing
Q: Which configuration gives the highest level of expression?
A: Ania Baetica (CIT), David Younger (UW), and Enoch Yeung (CIT) TX-TL workshop, June 2014
Richard M. Murray, Caltech CDS/BECSHL, 28 Jul 2014
Effect of Gene Placement
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Linear DNA
Convergent
Tandem
Divergent
Negative control
Enoch Yeung: [email protected]
Effect of intergenic spacing length
9
500bp
400bp
300bp
200bp100bp
1 mM
100 µM IPTG
10 µM
PLacPTet
Convergent Orientation
Divergent Orientation
Tandem Orientation
Plasmid DNA Linear DNA Spacing
Convergent
Divergent
Tandem
Richard M. Murray, Caltech CDS/BECSHL, 28 Jul 2014
Effect of Gene Placement
13
Enoch Yeung: [email protected]
Effect of intergenic spacing length
9
500bp
400bp
300bp
200bp100bp
1 mM
100 µM IPTG
10 µM
PLacPTet
Convergent Orientation
Divergent Orientation
Tandem Orientation
Plasmid DNA Linear DNA Spacing
Plasmid DNA
ConvergentTandem
Divergent
Negative control
Richard M. Murray, Caltech CDS/BECSHL, 28 Jul 2014
Effect of Gene Placement
14
Enoch Yeung: [email protected]
Effect of intergenic spacing length
9
500bp
400bp
300bp
200bp100bp
1 mM
100 µM IPTG
10 µM
PLacPTet
Convergent Orientation
Divergent Orientation
Tandem Orientation
Plasmid DNA Linear DNA Spacing
Enoch Yeung: [email protected]
Effect of intergenic spacing length
9
500bp
400bp
300bp
200bp100bp
1 mM
100 µM IPTG
10 µM
PLacPTet
Convergent Orientation
Divergent Orientation
Tandem Orientation
Plasmid DNA Linear DNA Spacing
Linear DNA100 bp
200 bp
300 bp
400 bp
500 bp
Negative control
Richard M. Murray, Caltech CDS/BEEngSci Syn Bio workshop, Sep 2014
Some Challenges and Research Directions (BFS)Better understanding of uncertainty • How do we capture observed behavior using
structured models for (dynamic) uncertainty
Stochastic specifications and design tools • How do we describe stochastic behavior in a
systematic and useful way? • How do we design stochastic behavior? • What are the right design “knobs”?
Higher level design abstractions • What are the right “device-level” design
abstractions (and corresponding diagrams)?
Redundant design strategies • Start implementing non-minimal designs • Analogy: stochastic memory storage
Scaling up: components → devices → systems • How can we use in vitro “breadboarding” to
design and implement complex systems
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