Cellular Computation and Communications using Engineered Genetic Regulatory Networks

1
Cellular Computation and Communicationsusing
Engineered Genetic Regulatory Networks
Ron Weiss Advisors Thomas F. Knight, Gerald Jay
Sussman, Harold Abelson Artificial Intelligence
Laboratory, MIT
2
Cellular Robotics
Environment
Biochemical Logic circuit
actuators
sensors

3
Vision

• A new substrate for engineering living cells
• interface to the chemical world
• cell as a factory / robot
• Logic circuit process description
• extend/modify behavior of cells
• Challenge engineer complex, predictable
  behavior

4
Applications

• Real time cellular debugger
• detect conditions that satisfy logic statements
• maintain history of cellular events
• Engineered crops / farm animals
• toggle switches control expression of growth
  hormones, pesticides
• Biomedical
• combinatorial gene regulation with few inputs
• sense recognize complex environmental
  conditions
• Molecular-scale fabrication
• cellular robots that manufacture complex
  scaffolds

5
Programming Cells
plasmid user program
6
Biochemical Inverters
signal concentration of specific
proteins computation regulated protein
synthesis decay
7
Engineering Challenges

• Map logic circuits to biochemical reactions
• Circuit design and implementation
• conventional interfaces
• sensitivities to chemical concentrations
• understand affinities of molecules to each other
• process engineering to adjust trigger levels,
  gains
• CAD tools (BioSpice)

8
Contributions

• Experimental results
• Built and characterized a small library of logic
  gates
• 4 different dna-binding proteins (lacI, tetR, cI,
  luxR)
• 12 modifications of gates based on cI protein
• transfer functions (input/output relationship)
• Built and tested several logic circuits
• combined 3 gates based on transfer functions
• Engineered communication between cells
• chemical diffusions carry message
• CAD tools and program design
• BioSpice (circuit design/verification)
• Microbial Colony Language

9
Outline

• A Model for Programming Biological Substrates
• Example Pattern formation
• Microbial Colony language
• In-vivo digital circuits
• Cellular gates Inverter, Implies
• BioSpice circuit simulations design
• Measuring and modifying device physics
• Intercellular communications
• Additional gate AND
• BioSpice simulations design
• Measuring device physics

10
Programming Biological Substrates

• Constraints/Characteristics
• Simple, unreliable elements
• Local, unreliable communication
• Elements engineered to perform tasks
• Example task form cellular-scale patterns

11
Another Example Differentiation
Cells differentiate into bands of alternating C
and D type segments.
12
A program for creating segments
Microbial Colony Language (MCL)
(start Crest ((send (make-seg C 1)
3))) ((make-seg seg-type seg-index) (and Tube
(not C) (not D)) ((set seg-type) (set
seg-index) (send created 3))) (((make-seg) (
0)) Tube ((set Bottom))) (((make-seg) (gt 0))
Tube ((unset Bottom)))
(created (or C D) ((set Waiting 10))) (
(and Bottom C 1 (Waiting ( 0))) ((send
(make-seg D 1) 3))) ( (and Bottom D 1
(Waiting ( 0))) ((send (make-seg C 2) 3))) (
(and Bottom C 2 (Waiting ( 0))) ((send
(make-seg D 2) 3))) ( (and Bottom D 2
(Waiting ( 0))) ((send (make-seg C 3) 3)))
13
How can we accomplish this?

• Boolean state variables
• DNA binding proteins
• Biochemical logic circuits
• genetic regulatory networks
• Intercellular signaling chemicals
• enzymes that make small molecules

biocompiler MCL ? genetic circuits
14
Outline

• Programming Biological Substrates
• Pattern Formation
• Microbial Colony language
• In-vivo digital circuits
• Cellular gates Inverter, Implies
• BioSpice circuit simulations design
• Measuring and modifying device physics
• Intercellular communications
• Additional gate AND
• BioSpice simulations design
• Measuring device physics

15
Why Digital?

• We know how to program with it
• Signal restoration modularity robust complex
  circuits
• Cells do it
• Phage ? cI repressor Lysis or Lysogeny?Ptashne,
  A Genetic Switch, 1992
• Circuit simulation of phage ?McAdams Shapiro,
  Science, 1995
• Ultimately, combine analog digital circuitry

16
Logic Circuits based on Inverters
R1
X

X
R1
Z
Z
gene
Y
R1
Y
gene
NAND
NOT
gene

• Proteins are the wires/signals
• Promoter decay implement the gates
• NAND gate is a universal logic element
• any (finite) digital circuit can be built!

17
Examples of Useful Circuits

• Logic statements
• (x AND y AND z) OR (NOT u)
• Decoders
• Turn ON 1 of 8 genes using only 3 inputs
• Counters
• Memory, Toggle switches
• Clocks

18
BioCircuit Computer-Aided Design
SPICE
BioSPICE

• BioSpice a prototype biocircuit CAD tool
• simulates protein and chemical concentrations
• intracellular circuits
• intercellular communication

19
Proof of Concept Circuits

• Work in BioSpice simulations Weiss, Homsy,
  Nagpal, 1998
• They work in vivo
• Flip-flop Gardner Collins, 2000, Ring
  oscillator Elowitz Leibler, 2000
• Models poorly predict their behavior

RS-Latch (flip-flop)
Ring oscillator
_ R
A
_ R
_ S
A
B
time (x100 sec)
B
B
_ S
C
A
time (x100 sec)
time (x100 sec)
20
Evaluation of the Ring Oscillator
Elowitz Leibler, 2000
Reliable long-term oscillation doesnt work yet

• Need to match gates

21
Measuring Modifying Device Physics

• Why?
• Different elements have widely varying
  characteristics
• Need to be matched
• Assembled and characterized a library of
  components
• Constructed and measured gates using 4 genetic
  candidates
• lac, tet, cI, lux
• Created 12 variations of cI in order to match
  with lac
• modified repressor/operator affinity
• modified RBS efficiency
• other mechanisms protein decay, promoter
  strength, etc..
• Established component evaluation criteria
• Initially, focused on steady state behavior

22
Steady-State Behavior Inverter

• ideal transfer curve
• gain (flat,steep,flat)
• adequate noise margins

gain
output
0
1
input
This curve can be achieved using proteins that
cooperatively bind dna!
23
Measuring a Transfer Curve

• Construct a circuit that allows
• Control and observation of input protein levels
• Simultaneous observation of resulting output
  levels

inverter
R
YFP
CFP
output gene

• Also, need to normalize CFP vs YFP

24
Repressors Inducers

• Inducers that inactivate repressors
• IPTG (Isopropylthio-ß-galactoside) ? Lac
  repressor
• aTc (Anhydrotetracycline) ? Tet repressor
• Use as a logical Implies gate
  (NOT R) OR I

Repressor
Output
Inducer
25
Drive Input Levels by Varying Inducer
IPTG (uM)
lacI high
YFP
0 (Off)
?P(R)
P(LtetO-1)
IPTG
0
250
1000
promoter
protein coding sequence
26
Controlling Input Levels
Also use for yfp/cfp calibration
27
Meausring a Transfer Curve for lacI/p(lac)
drive
output
28
Transfer Curve Data Points
0?1
1?0
undefined
1 ng/ml aTc
10 ng/ml aTc
100 ng/ml aTc
29
lacI/p(lac) Transfer Curve
gain 4.72
30
Evaluating the Transfer Curve

• Gain / Signal restoration

high gain

• note graphing vs. aTc (i.e. transfer curve
  of 2 gates)

31
Measure cI/?P(R) Inverter

• cI is a highly efficient repressor

cooperative binding
high gain
OR1
OR2
structural gene
?P(R-O12)
cI bound to DNA

• Use lacI/p(lac) as driver

lacI high
cI CFP
0 (Off)
YFP
?P(R)
P(lac)
IPTG
32
Initial Transfer Curve for cI/?P(R)

• Completely flat
• Reducing IPTG ? no additional fluorescence
• Hard to debug!
• Process engineering
• Is there a mismatch between inverters based on
  lacI/p(lac) and cI/?P(R)?

33
Inverters Rely onTranscription Translation
translation
mRNA
mRNA
ribosome
ribosome
transcription
operator
RNAp
promoter
34
Process Engineering IDifferent Ribosome Binding
Sites
BioSpice Simulations
35
Experimental Results forModified Inverter
36
Process Engineering IIMutating the ?P(R)
37
Experimental Results for Mutating ?P(R)
38
Lessons for BioCircuit Design

• Naive coupling of gates not likely to work
• Need to understand device physics
• enables construction of complex circuits
• Use process engineering
• modify gate characteristics

39
Outline

• Programming Biological Substrates
• Pattern Formation
• Microbial Colony language
• In-vivo digital circuits
• Cellular gates Inverter, Implies
• BioSpice circuit simulations design
• Measuring and modifying device physics
• Intercellular communications
• Additional gate AND
• BioSpice simulations design
• Measuring device physics

40
Intercellular Communications

• Certain inducers useful for communications
• A cell produces inducer
• Inducer diffuses outside the cell
• Inducer enters another cell
• Inducer interacts with repressor/activator ?
  change signal

main metabolism
(1)
(2)
(3)
(4)
41
Activators Inducers
inactive activator
active activator
RNAP
inducer
transcription
no transcription
RNAP
gene
gene
operator
promoter
operator
promoter

• Inducers can activate activators
• VAI (3-N-oxohexanoyl-L-Homoserine lacton) ? luxR
• Use as a logical AND gate

Activator
Output
Inducer
42
BioSpice Intercellular Communications

• Small simulation
• 4x4 grid
• 2 cells (outlined)

chemical concentration
(1) original I 0
(2) introduce D send msg M
(3) recv msg set I
(4) msg decays I latched
43
Eupryma scolopes
Light organ
44
Quorum Sensing

• Cell density dependent gene expression
• Example Vibrio fischeri density dependent
  bioluminscence

The lux Operon
LuxI metabolism ? autoinducer (VAI)
45
Density Dependent Bioluminescence
Low Cell Density
High Cell Density
LuxR
LuxR
(Light) hv
Luciferase
LuxR
LuxR
LuxI
LuxI
()
P
P
luxR
luxI
luxC
luxD
luxA
luxB
luxE
luxG
luxR
luxI
luxC
luxD
luxA
luxB
luxE
luxG
P
P
free living, 10 cells/liter lt0.8
photons/second/cell
symbiotic, 1010 cells/liter 800
photons/second/cell

• A positive feedback circuit

46
Similar Signalling Systems
N-acyl-L-Homoserine Lactone Autoinducers in
Bacteria
47
Circuits for Controlled Sender Receiver

• Genetic networks

VAI
VAI

E. coli strain expresses TetR (not shown)

• Logic circuits

LuxR
GFP
0
tetR
0
luxI ?VAI
VAI
aTc
aTc
pLuxI-Tet-8
pRCV-3
48
Experimental Setup

• BIO-TEK FL600 Microplate Fluorescence Reader
• Costar Transwell microplates and cell culture
  inserts with permeable membrane (0.1µm pores)
• Cells separated by function
• Sender cells in the bottom well
• Receiver cells in the top well

49
Time-Series Response to Signal
positive control
10X VAI extract
direct signalling
negative controls

• Fluorescence response of receiver (pRCV-3)

50
Characterizing the Receiver

• Response of receiver to different levels of VAI
  extract

51
Controlling the Senders Signal Strength
Dose response of receiver cells to aTc induction
of senders
52
receivers
senders
53
receivers
senders
overlay
54
(No Transcript)
55
(No Transcript)
56
Summary

• Built, characterized, and modified a library of
  cellular gates (TTL Data Book)
• Using parts that match, built and tested several
  small in-vivo digital circuits
• Engineered and tested programmable intercellular
  communications
• BioSpice (circuit design/verification)
• Microbial Colony Language

57
Future Work

• New programming paradigms
• Bio-compiler
• Additional CAD tools
• Bio-fab
• Large scale circuit design, production, and
  testing
• Simpler more complex organisms
• Eukaryotes
• Mycoplasmas
• Biologically inspired logic gates
• Engineer multicellular organisms
• Molecular scale fabrication

58
THE END
59
senders
receivers
overlay
60
Transfer Curve of Implies
lacI
YFP
aTc
IPTG
61
Modifying the Ribosome Binding Site (RBS)
Orig ATTAAAGAGGAGAAATTAAGCATG strong RBS-1
TCACACAGGAAACCGGTTCGATG RBS-2
TCACACAGGAAAGGCCTCGATG RBS-3
TCACACAGGACGGCCGGATG weak
?
BioSpice Simulations
Real Experiments
62
lacI/p(lac) Transfer Curve linear scale
63
Outline

• In-vivo digital circuits
• Cellular gates Inverter, Implies
• BioSpice circuit simulations design
• Measuring and modifying device physics
• Intercellular communications
• Additional gate AND
• BioSpice simulations design
• Measuring device physics
• New programming methodologies
• Microbial Colony language
• Simulation environment

64
Electrical Circuits vs. Bio-circuits
65
Glossary

• DNA
• RNAp
• Promoter
• Proteins (Repressors/Activators/Enzymes)
• Transcription
• Translation
• mRNA
• Ribosomes
• Plasmid

66
First, Program Cells to Form Patterns
from Frank Netter Atlas of Human Anatomy
67
Amorphous Computing Substrates

• Many identical, unreliable, computing elements
• Constrained to interact locally
• Need programming paradigms
• initially, focus on obtaining patterns

68
Additional Examples
Somite formation
Tube formation using gradients
69
Future Work Bio-compiler
Origami Shape Language (Radhika Nagpal)
Growing Point Language (Daniel Coore)
70
(No Transcript)
71
An Example of Signal Oscillation
72
Transfer Curve
Note Units on input are different than units on
output
73
Inverter Chemical Reactions
74
Pattern Formation in Amorphous Substrates
Diffusion-based communications can yield complex
patterns
Example forming a chain of inverters using
only local communications
75
Overview
finish
Microbial Colony Language
start
76
BioCircuit Design (TTL Data Book)

• Data sheets for components
• imitate existing silicon logic gates
• new primitives from cellular regulatory elements
• e.g. an inverter that can be induced
• Assembling a large library of components
• modifications that yield desired behaviors
• Constructing complex circuits
• matching gates is hard
• need standard interfaces for parts
• from black magic to you can do it too

77
Programming Cooperative Behavior

• Engineer loosely-coupled multicellular systems
  that display coordinated behavior
• Use localized cell-to-cell communications
• Robust programming despite
• faulty parts
• unreliable communications
• no global synchronization
• Control results in
• Patterned biological behavior
• Patterned material fabrication
• Massively parallel computation with local
  communication
• Suitable for problems such as physical simulation
  

78
High Level Programming

• Requires a new paradigm
• colonies are amorphous
• cells multiply die often
• expose mechanisms cells can perform reliably
• Microbial programming language
• example pattern generation using aggregated
  behavior

79
Limitations

• DNA Binding Protein Logic is Slow
• milli Hertz (even with 1012 cells, still too
  slow)
• Limited number of intra- and inter-cellular
  signals
• Amount of extracellular DNA that can be inserted
  into cells
• Reduction in cell viability due to extra
  metabolic requirements
• We need a writeable long term storage

80
(No Transcript)
81
A Computer Engineers Motivation

• An exciting substrate for computing and robotics
• Special features of cells
• small, self-replicating, energy-efficient
• Applications
• Biomedical
• Environmental (sensors effectors)
• Embedded systems
• Interface to chemical world
• Molecular scale fabrication

Toxin A
kills
pathogen
Toxin A
pathogen
Antibiotic A
detection
Customized Receptor Cell
antibiotic synthesis machine
82
Approach
Engineer Genetic Regulatory Networks that
implement in-vivo logic circuits

• Control intracellular processes
• Perform intercellular communications

• Methodology
• Borrow from existing genetic elements
• Integrate elements into new hosts
• Characterize, modify if necessary
• Combine into new circuits that perform desired
  functions

83
Toggle Switch
(Gardner Collins, Nature, 2000)
84
Ring Oscillator
(Elowitz Leibler, Nature, 2000)
simulation

• Three inverters in a ring circuit
• Desire Signals oscillating between high and low

85
Challenges

• Engineer the system support for experimental
  cellular engineering into living cells
• Engineer component interfaces
• Develop instrumentation and modeling tools
• Obtain missing data in spec sheet fields
• Discover unknown fields in the spec sheet
• Create computational organizing principles
• Invent languages to describe phenomena
• Builds models for organizing cooperative behavior
• Create a new discipline crossing existing
  boundaries
• Educate a new set of engineering oriented students

86
Bio/SPICE (Arkin)
87
Modify translation
88
Additional Logic Gates using Inverters
R1
X
Z
z (x OR y)
gene
gene
R2
Y
Z
gene
gene
R1
X
z (x AND y)
Z
gene
R1
Y
gene
gene
89
- IPTG
Inducer (IPTG)
IPTG
90
Naturally Occurring Sensor and Actuator Parts
Catalog

• Sensors
• Light (various wavelengths)
• Magnetic and electric fields
• pH
• Molecules
• Autoinducers
• H2S
• maltose
• serine
• ribose
• cAMP
• NO
• Internal State
• Cell Cycle
• Heat Shock
• Chemical and ionic membrane potentials

• Actuators
• Motors
• Flagellar
• Gliding motion
• Light (various wavelengths)
• Fluorescence
• Autoinducers (intercellular communications)
• Sporulation
• Cell Cycle control
• Membrane transport
• Exported protein product (enzymes)
• Exported small molecules
• Cell pressure / osmolarity
• Cell death

91
Experiment I Constant Signaling

• Genetic networks for sender receiver

VAI
VAI

• Logic circuit diagrams for sender receiver

LuxR
GFP
LuxI
VAI
VAI
pSND-1
pRCV-3
92
Matching Gates

• When connecting gates A?B, the output of A must
  match the input of B
• As LOW output range must be within Bs LOW input
  range
• As HIGH output range must be within Bs HIGH
  input range

93
(No Transcript)
94
Growth
Segments can grow by invading neighboring
segments, thereby stimulating them to grow
95
(No Transcript)
96
Lac repressor
97
Modifications to Gates

• modification stage
• Modify repressor/operator affinity C
• Modify the promoter strength T
• Alter degradation rate of a protein C
• Modify RBS strength L
• Increase cistron count T
• Add autorepression C

? Each modification adds an element to the
database
98
Limits to Circuit Complexity

• amount of extracellular DNA that can be inserted
  into cells
• reduction in cell viability due to extra
  metabolic requirements
• selective pressures against cells performing
  computation
• probably not different suitable proteins

99
Related Work

• Universal automata with bistable chemical
  reactions Roessler74,Hjelmfelt91
• Mathematical models of genetic regulatory systems
  Arkin94,McAdams97,Neidhart92
• Boolean networks to describe genetic regulatory
  systems Monod61,Sugita63,Kauffman71,Thomas92
• Modifications to genetic systems Draper92,
  vonHippel92,Pakula89

100
Inverter in more detail
input signal
output signal
cooperative binding
output mRNA
transcription
translation
input mRNA
repression
inputt protein
mRNA synthesis
input protein
mRNA
101
MISC

• Show some results right away
• mention circuit design
• constructed infrastructure

102
Implementing the Digital Abstraction

• In-vivo digital circuits
• signal concentration of specific protein
• computation regulated protein synthesis decay
• The basic computational element is an inverter
• Logical NOT operation (0?1, 1?0)
• More importantly signal restoration
• The output must be better than the input
• low gain in the high and low states
• high gain in intermediate states
• nonlinear transfer curve

103
Programming Model

• Each computing element has state consisting of
  boolean markers.
• Each computing elements program has many
  independent rules.
• Rules are triggered by messages. A rule is
  applicable if a certain boolean combination of
  markers is satisfied.
• When a rule is applied, it may set markers and
  send further messages.
• Messages have hop counts that determine how far
  they diffuse.
• Markers may have lifetimes after which they
  expire.

104
Comments

• Intro talk about building a chip core, then
  having the core control peripherals, I/O
• Summary Again, talk about chip core
• Future different logic gates inspired from
  biological processes