Engineering Gene Networks: Integrating Synthetic Biology & Systems Biology

James J. CollinsCenter for BioDynamics andDepartment of Biomedical EngineeringBoston University

Human Balance Control and Vibrating Insoles

Directed Evolution of Academic Interests

Charles CantorBoston University and Sequenom

Transfer to cell Test networkdynamics

Encode into DNAplasmid

Design & model network

# o

f C

ells

Off On

1 2 43Gene Expression

Synthetic Biology: Engineered Gene Networks

Inducer 1

Inducer 2

Reporter

Repressor 1

Repressor 2

Schematic Design of Genetic Toggle Switch

TS Gardner et al., Nature, 2000

Toggle Model Identifies the Minimal Conditions for Bistability

Nonlinear ODE model: reduced rate equations for transcription and translation

0

0

Toggle Model Identifies Minimal Conditions for Bistability

Genetic Toggle Switch: Plasmid Design

Experimental Demonstration of Bistability

Results: Switching Threshold

2

3

4

Results: Switching Time

Switching ON Switching OFF

Programmable Cells

Interfacing natural and engineered gene networks

Programmable Cells: DNA Damage Sensor

H Kobayashi et al., PNAS, 2004

DNA Damage Sensor with a Biofilm Readout

Programmable Cells: Crowd Sensor

Enter and Destroy the Biofilm Matrix

Results: Engineered Enzymatically Active Bacteriophage

RNA-based Synthetic Biology

RNA Switches: Engineered Riboregulators

FJ Isaacs et al., Nature Biotechnology, 2004

Engineered Riboregulator: Cis-Repression

Predicted Mfold structures Intermediate transcription

High transcription

Only sequences with one Mfold predicted structure were pursued

Shown in green is the start codon, in blue the ribosome binding site, and in red the cis-repressive sequence

Engineered Riboregulator: Trans-Activation

taR12-crR12 interaction

Steady-State ResponseTransient Response Specificity

Engineered Riboregulator: System Performance

FJ Isaacs et al., Nature Biotechnology, 2004

Engineered Mammalian Gene Switch: RNAi and Repressor Proteins

Engineered Mammalian Gene Switch: Performance Characteristics

Applications of Synthetic Gene Networks

Engineered gene circuits

Biosensors

Cell therapy, stem cells

Functional genomics

Systems Biology: Reverse Engineering Gene NetworksSystems Biology: Reverse Engineering Gene Networks

Gene Circuit Gene Circuit Control ToolboxControl Toolbox

Complex Systems Complex Systems ToolboxToolbox

ReconstructedReconstructedGene CircuitryGene Circuitry

Network Inference via Gene Perturbations & Expression Profiling

Overexpress each gene in network

Obtain expression profiles for each

compound

Process expression data with NIR

algorithm

1.

2.

3.

4.

5.

Reverse engineerregulatory network

Gene 1

Gene 2

Gene 3

Gene 4

Gene 5

NIR

MKS Yeung et al., PNAS, 2002J Tegner et al., PNAS, 2003

E. coli SOS Pathway (DNA-Damage Repair Pathway)

SOS pathway involves over 100 genes

Validation study examined nine-gene subnetwork

TS Gardner et al., Science, 2003

Network Identification by multiple Regression (NIR) Algorithm

• Assay 9 mRNA species

• Quantitative real-time PCR

• SYBR Green protocol w/ 16S RNA normalization

• 8 sample replicates, duplicate PCR rxns

• Statistical filtering for noise reduction

Perturb mRNA Expression

Profile mRNA Expression

Apply NIR Algorithm

• 7-9 genes perturbed

• Wild-type E. coli MG1655 (K-12) cell strain

• Episomal, SC101-based perturbation vector

• Arabinose-inducible expression system

• Estimate perturbation from luciferase control

pBAD253s6640 bp

luc

luc + linker

AP(R)

operator O2

operator O1

CAP site

operator I2 and I1

Unique Q-PCR Tag

cI fragment

araC promoter

arabinose BAD promoter

P(BLA)

SD seq

SC101 Origin (Approximate)

rrnB T1 T2

ApaLI (2829)

ClaI (1726)

NcoI (6050)

AvaI (1417)

AvaI (4076)

BamHI (252)BamHI (6055)

EcoRI (341)

EcoRI (948)

HindIII (2190)

HindIII (6332)

PstI (2024)

PstI (2182)

PstI (3259)

Recover & Apply Network

• Identify critical nodes

• Profile drug interactions

• Optimize lead compounds

16 18 20 22 24-2-101

18 20 22 24 26-2-101

18 20 22 24 26-2-101

24 26 28 30 32-2-101

20 22 24 26 28-2-101

20 22 24 26 28-2-101

18 20 22 24 26-2-101

20 22 24 26 28-2-101

35 40 45-2-101

22 24 26 28 30-2-101

dRn

n

SOS Network Analysis: Experimental-Computational Overview

First-order approximation

Minimum least squares

Constraint: k < N inputs/gene;Search: exhaustive or heuristic

Steady-state;Small perturbations

Linear model w/ confidence estimates

NIR algorithm

Model Structure

Fit Criterion

SolutionSearch Strategy

Data Design & Collection

Estimated Model

General system ID framework

NIR Algorithm for Inferring Genetic Networks

SOS Subnetwork Model Identified by NIR

lexA

recA

recF

rpoD

rpoS

rpoH dinI

ssb

umuDC

-2.920.67-1.680.22

-0.030.010.10

-0.51-0.17

0.01-0.040.16-1.09

-0.01

0000

00000

0000

0000

0000

000000000

0000

0000

0000

0.08

0.52

0.020.03-0.02

-0.150.20-0.02-0.400.11

0.28

0.030.05-0.28-1.190.04

-0.070.09-0.01-0.670.39

0.10-0.01-0.180.40

Connection strengths

recA

lexA

ssb

recF

dinI

umuDC

rpoD

rpoH

rpoS

rpoSrpoHrpoDumuDdinIrecFssblexArecA

Graphical model Quantitative regulatory model

Majority of previously observed influences discovered despite

high noise (68% N/S)

NIR Model Correctly Identifies Major SOS Network Regulators

-0000

0-0000

0-000

000-0

0-000

00000-000

0000-

0000-

0000

0.22

0.10

-0.17

-0.400.11

0.28

-1.190.04

0.39

-0.18

0.080.67-1.68

-0.030.01

0.020.03-0.02

0.20-0.02

0.01-0.040.16

0.030.05

-0.070.09-0.01

-0.010.10-0.01

Influence strengths

recA

lexA

ssb

recF

dinI

umuDC

rpoD

rpoH

rpoS

rpoSrpoHrpoDumuDdinIrecFssblexArecA

-lexA

recA

recF

rpoD

rpoS

rpoH dinI

ssb

umuDC lexA

recA

recA and lexA identified as major regulators in the SOS subnetwork

0

2

4

6

8

10

12

14

16

recA lexA ssb recF dinI umu rpoD rpoH rpoS

Mean influence on other genes

Mean R

esp

one

(%

)

Identified Network Can Be Used to Profile Drug Targets

Treat cells with drug compound

ID direct genetic targets of drug

Obtain expression profile

Filter profile using identified network

drug

drug

Solved Using NIR

NIR Validation: recA/lexA Double Perturbation

Expression changes Following recA/lexA double perturbation

Predicted mediators:lexA and recA

identified as perturbed genes by

network model

Cannot distinguish affected genes using just expression data

Correct mediators of expression profile identified using NIR approach

recA lexA ssb recF dinI umu rpoD rpoH rpoS-2

-1

0

1

2

3

-0.5

0

0.5

1

1.5

2

2.5

recA lexA ssb recF dinI umu rpoD rpoH rpoS

NIR Validation: MMC Mode of Action in E. coli

Expression changesFollowing

mitomycin C perturbation

Predicted mediatorsrecA and umuDC

identified as mediators

0

0.51

1.52

2.5

33.5

4

recA lexA ssb recF dinI umu rpoD rpoH rpoS

recA lexA ssb recF dinI umu rpoD rpoH rpoS

-0.5

0

0.5

1

1.5

2

2.5

Network Model Identifies Mode of Action of Additional Stressors

Mitomycin C

UV radiation

Pefloxacin

Novobiocin

recA lexA ssb recF dinI umu rpoD rpoH rpoS

DNA-damaging agents

Does not damage DNA

Predicted mediators

E. Coli Network Reconstruction on a Genome Scale

Quinolones Induce an Oxidative Damage Cellular Death Pathway

Bactericidal Antibiotics: Stimulate Hydroxyl Radical Formation

Bacteriostatic Antibiotics: Hydroxyl Radicals Are Not Produced

Disabling the SOS Response Potentiates Bactericidal Antibiotics

Extending to Higher Organisms and Diverse Data Sets

1.

2.

Gene 1

Gene 2

NIRAlgorithm

MNIAlgorithm

MNI enables use of compounds, knockouts, mutations, etc. to identify network

Drug1.

2. KO

3. Gene 1

MNI

NIR

D di Bernardo et al., Nature Biotechnology, 2005

Tested MNI on Yeast Data Set of 515 Expression Profiles

515 DiverseTreatments

Measure 6000+RNAs

Data from:• TR Hughes, et al., Cell, 2000 (300 expression profiles)• S Mnaimneh, et al., Cell, 2004 (215 expression profiles)

MNI Identifies Target of Itraconazole

Expression Change MNI Predictions

Filter through MNI-inferred network model

Itraconazole treatment: a known target is ERG11

Erg11Erg11

MNI Identifies Target Pathways/Genes for Multiple Compounds

D di Bernardo et al., Nature Biotechnology, 2005

Identified Novel Anticancer Compound via Chemical Screen

• PTSB inhibits growth in yeast and tumor cell lines

In collaboration with Schaus and Elliot laboratoriesDept. of Chemistry, Boston UniversityCenter for Methodology and Library Development (CMLD), Boston U.

Identification and Validation of PTSB Targets

MNI identifies thioredoxin (TRX2) and thioredoxin reductase (TRR1)

TRR1/TRX2 activity inhibited in presence of PTSB

-0.01

0.04

0.09

0.14

0.19

0.24

0.29

0.34

0 2 4 6 8 10

Time (min)

Ab

so

rban

ce 4

12

nm

B

A

D

C

Biochemical Assay:

• Thioredoxin reduction of dithio(bis)nitrobenzoic acid (DTNB)

• Product of reaction = thiolate anion, measured via A412

0 uM PTSB

1 uM PTSB

5 uM PTSB

50 uM PTSB

A Network Biology Approach to Prostate Cancer

Key Enriched Pathways and Associated Genetic Mediators

AR Gene Rankings: MNI vs Expression Change Alone

Applied Biodynamics Labhttp://www.bu.edu/abl