Feedback and Control in Biological Circuit Designsysos.eng.ox.ac.uk/wiki/images/7/74/Murray.pdfFeedback and Control in Biological Circuit Design Richard M. Murray Control & Dynamical

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


Example: AM Radios

http://www.roetta.it/ik3hia

3

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


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)

4

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


Analysis & Design of Biomolecular Feedback Systems

5

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


Genelet-Based CircuitsRate regulator circuit (ACS Syn Bio, 2014)

Genelet-based Insulation (PNAS, 2012)

7

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

8

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


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


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


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

9

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)

10

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)

11

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

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


Effect of Gene Placement

12

Linear DNA

Convergent

Tandem

Divergent

Negative control



9

500bp

400bp

300bp

200bp100bp

1 mM

100 µM IPTG

10 µM

PLacPTet



Tandem Orientation


Convergent

Divergent

Tandem



13



9

500bp

400bp

300bp

200bp100bp

1 mM

100 µM IPTG

10 µM

PLacPTet



Tandem Orientation


Plasmid DNA

ConvergentTandem

Divergent

Negative control



14



9

500bp

400bp

300bp

200bp100bp

1 mM

100 µM IPTG

10 µM

PLacPTet



Tandem Orientation




9

500bp

400bp

300bp

200bp100bp

1 mM

100 µM IPTG

10 µM

PLacPTet



Tandem Orientation


Linear DNA100 bp

200 bp

300 bp

400 bp

500 bp

Negative control


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

15

Documents

Feedback and Control in Biological Circuit Designsysos.eng.ox.ac.uk/wiki/images/7/74/Murray.pdfFeedback and Control in Biological Circuit Design Richard M. Murray Control & Dynamical