Living in a World of Nanobioinfotechnology

Invited Talk

Invitrogen

Carlsbad, CA

September 12, 2007

Dr. Larry Smarr

Director, California Institute for Telecommunications and Information Technology

Harry E. Gruber Professor,

Dept. of Computer Science and Engineering

Jacobs School of Engineering, UCSD

California’s Institutes for Science and Innovation A Bold Experiment in Collaborative Research

UCSBUCLA

California NanoSystems Institute

UCSF UCB

California Institute for Bioengineering, Biotechnology,

and Quantitative Biomedical Research

UCI

UCSD

California Institute for Telecommunications and Information Technology

Center for Information Technology Research

in the Interest of Society

UCSC

UCDUCM

www.ucop.edu/california-institutes

UC San Diego

Two New Calit2 Buildings Provide New Laboratories for “Living in the Future”

• “Convergence” Laboratory Facilities– Nanotech, BioMEMS, Chips, Radio, Photonics

– Virtual Reality, Digital Cinema, HDTV, Gaming

• Over 1000 Researchers from 24 Departments– Buildings Linked via Dedicated Optical

Networks

UC Irvinewww.calit2.net

Preparing for a World in Which Distance is Eliminated…

Federal Agency Source of Funds

Federal Agencies Have Funded $350 Million to Over 300 Calit2 Affiliated Grants

Creating a Rich Ecologyof Basic Research

50 Grants Over $1 Million

Broad Distribution of Medium and Small Grants

In Spite of the Bubble Bursting, Calit2 Has Partnered with over 130 Companies

Industrial Partners > $1 Million

Over $80 Million From Industry

So Far

1000

10000

100000

1000000

10000000

100000000

0 20 40 60 80

Rank D

olla

rs R

ecei

ved

Per

Co

mp

any

Broad Range of Companies

More Than 80 Have Provided Funds or In-kind

Accelerator: The Perfect Storm-- Convergence of Engineering with Bio, Physics, & IT

2 mm

HP MemorySpot

Nanobioinfotechnology

1000x Magnification

2 micron

DNA-Conjugated Microbeads

Human Adenovirus

400x Magnification

IBM Quantum CorralIron Atoms on Copper

5 nanometers

400,000 x !

Calit2 is Creating a Nano-Bio-Info Innovation Laboratory

Donald Bren School of

Information and

Computer Science

INRF Supports Researchers in Nano and BioMEMS

Spray atomization of nano powders

New methods of making arrays of nanowires

Boron-based nanowires for novel circuits

Carbon nanotubes for sensor and electronic applications

Micromirror on a catheter for optical biopsy using coherence tomography

Protein crystallization in nanovolumes

0 ms 200 ms

400 ms 600 ms

Microfluidic devices for electrophoretic separations

Microfluidic devices using droplets, CD microfluidics andmagnetohydrodynamics

BioMEMS and Medical Applications

Nanotechnology / Nanofabrication

INRF Partners Companies with University Researchers: 70 Past and Current Collaborating Companies

• Advanced Customs Sensors Inc.• Agilient• Alpha Industry/Network Device Inc.• AXT/Alpha Photonics Incorporated• Alpine Microsystems Incorporated• Auxora, Inc.• Bethel Material Research• Broadcom• Broadley-James Corp.• Cito Optronics, Inc.• Coherent, Inc.• Conexant• Coventor• DRS Sensors• Endevco

Friends USA• General Monitors• Global Communication Semiconductor• Hewlett Packard• Hitachi Chemical Research• IJ Research• Impco Technology• Intelligent Epitaxy Incorporated• International Technology Works• IOS • Irvine Sensors• Jazz Semiconductor• Linfinity Microelectronics• Maxwell Sensors• Metrolaser Incorporated

Microtek Lab Incorporated• MicroWave Technology• Moog, Inc. • Network Device• Newport Opticom

• NexGen Research Corporation• Northrop Grumman Corporation• Numerical Technologies

Ormet Corp.• Oplink Communications• Optical Crossing• Optinetrics• Optiswitch Technology• Physical Optics Corp.• Printronix• ProComm Enterprises• Rainbow Communications• Raytheon Systems• Rockwell• RF Integrated Corp.• Sabeus Photonics• Saddleback Aerospace• SAIC

Second Sight, LLC• Semco Laser Technology• Sequenom• Silicon Storage Technology, Inc.• Simax• Skyworks Solutions• SVT Associates• Tamarack Scientific, Inc.• Tanner Research, Inc.• Texas Instruments, Inc.• TRW• U Machines• Versa Technology• VSK Photonics• WIN Semiconductors• Xtal Technologies• Y Media Corporation

40 UCI Faculty from

a Dozen Departments

INRFCalit2BiON

ZeissCenter of

Excellence

Micro/Nano Materials and Devices

Bio-Organic Nano Lab

SEM,Advanced

Characterization

Three centers share a common infrastructure

Photonics,RF,

ChipLabs

Integrate with

Chips, Telecom

Calit2@UCI Nanobioinfotechnology“Innovation Pipeline”

Source: GP Li, Calit2

Example: Real-Time Electronic Readout from Single Biomolecule Sensors

• Carbon Nanotube Circuits Provide Nanoscale Connectivity

• New Techniques Integrate Single-Molecule Attachments

• Dynamics and Interactions With the Environment Can be Directly Measured

• Electronic Readout Compatible With Hand-held, Low-power Devices

Source: Phil Collins & Greg Weiss, Calit2@UCI

1 nm wiring

1 proteinmolecule

… and withoutdevice in buffer with reagents

Schematic & SEM Image of Carbon Nanotube-based Device

LifeChips: the merging of two major industries, the microelectronic chip industry

with the life science industry

LifeChips medical devices

Lifechips--Merging Two Major Industries: Microelectronic Chips & Life Sciences

65 UCI Faculty

Calit2@UCSD Building Anchors “Bio-Nano-IT Convergence Quad”

Calit2

Bioengineering

Computer Science and Engineering

Faculty Leadership Team

Sadik Esener Dept. of Electrical and Computer Engineering

Michael Heller Dept. of Bioengineering

Sungho Jin Dept. of Mechanical and Aerospace Engineering

Jan Talbot Dept. of Mechanical and Aerospace Engineering -

Chemical Engineering Program

Kenneth Vecchio Dept. of Mechanical and

Aerospace Engineering

In the past five years alone, these five faculty filed 51 patent applications and licensed 6 inventions to private companies

Corporate SupportersGMT Ventures ~ Invitrogen ~ Pfizer ~ Illumina ~ QUASAR Federal Systems ~ ResMed ~

Enterprise Partners Venture Capital ~ Varian Medical Systems ~ Sun Microsystems ~ SAIC ~ Intel

UCSD Jacobs School of Engineering NanoEngineering Department Proposal Proponents

Nano3 FacilityCALIT2.UCSD

10,000 sq. feet State-of-the-Art

Materials and Devices Laboratory

Calit2 Nano3 Clean Rooms Helps “Jump Start”The New Jacobs School Dept. of Nanoengineering

Source: Bernd Fruhberger, Calit2

http://nano3.calit2.net/

45 Faculty with Nano Projects at

Calit2@UCSD

Yu-Hwa Lo Research GroupElectrical and Computer Engineering

Integrating Photonics and Microfluidics

Microfluidic Flow Cytometry Chip for Portable, Low-cost Blood Analysis. The Chip Contains 12 Lenses and 6 Waveguides to Illuminate a Single Cell and Collect the Light it Scatters

Treatment, Understanding, and Monitoring of Cancer (UCSD, Burnham Institute, UCSB, UCR, UCI --PI: Sadik Esener)

Michael J. Sailor Research GroupChemistry and Biochemistry

Nanostructured “Mother Ships” for deliveryof cancer therapeutics.

Nanodevices for In-vivo Detection & Treatment of Cancerous Tumors

Porous Photonic Crystals for Cell-based Biosensor

Human epithelial (HeLa) cells on a photonic crystal.The colors observed can be used to monitor thephysiological status of the cells.

Nano-Structured Porous SiliconApplied to Cancer Treatment

TEM Images of Hollow Silica Nanospheres for Drug and Gene Delivery Applications

100 nm

100 nm

Silica gel wall 5 nm thick

100 nm cavity

J. Yang, J. U. Lind, W. C. Trogler, Department of Chemistry and Biochemistry and

Calit2 Nanomedicine Laboratory

Single Cancer Cell Isolated on EngineeredSurface for Phenotyping and Genotyping

Hoechst Nuclear Stain (Blue), Nano-bead Membrane Dye (Green),

Surface AttachmentSite (Red).

Devices for Fast and Automated Detection ofSingle Cancer Cells in Tissue Margins

Advanced Staining Procedures Allows Us to DistinguishEpithelial Cells (Cancerous) From Non-Epithelial Cells

Developing Small Molecule and Quantum Dot Cell Staining Procedures to

Identify Single Cancer Cells in Otherwise Normal Tissue

Green:CancerSpecificStain II

Red:CancerSpecificStain I

Blue:HoechstNucleusStain

Finding the Needle in the Haystack

Cancer NanotechnologyKummel / Trogler / Schuller Cancer Center Project

Quantum Dot Staining of Filled Cells: Light Microscopy

astro volume (gray); perimeter cx43 (red); internal cx43 (yellow)

Dye Filled Astrocytes in the Brain are Immuno-Labeled

with QDs for Connexin Proteins

(Cell-Borders in Yellow)

Diffraction-Limit Volumes Can Then be Acquired

and the Distribution of the Protein over the Cells

Can be Quantified and Analyzed

Immuno-Labelling of Cx43 with Quantum Dots Allows for Immuno-Labelling of Cx43 with Quantum Dots Allows for Correlated LM and EM of cx43 on the Dye-Filled Astrocyte.Correlated LM and EM of cx43 on the Dye-Filled Astrocyte.

Source: B. Smarr, M. Ellisman, UCSD NCMIR

Calit2 Brings Computer Scientists and Engineers Together with Biomedical Researchers

• Some Areas of Concentration:– Algorithmic and System Biology

– Bioinformatics

– Metagenomics

– Cancer Genomics

– Human Genomic Variation and Disease

– Proteomics

– Mitochondrial Evolution

– Computational Biology

– Multi-Scale Cellular Imaging

– Information Theory and Biological Systems

– Telemedicine

UC Irvine

UC Irvine

Southern California Telemedicine Learning Center (TLC)

National Biomedical Computation Resource an NIH supported resource center

Calit2 Facilitated Formation of the Center for Algorithmic and

Systems Biology

http://casb.ucsd.edu/

UCI’s IGB Develops a Suite of Programs and Servers for Protein Structure and Structural Feature Prediction

www.igb.uci.edu/tools.htm

Source: Pierre Baldi, UCI

Sixty Affiliated IGB Labs at UCI

e.g.:

Information Theorists Working with Bio, IT, and Nano Researchers Will Radically Transform Our View of Living Systems

"Through the strong loupe of information theory,

we will be able to watch how such [living] beings

do what nonliving systems cannot do:

extract information from their surrounds,

store it in a stable molecular form,

and eventually parcel it out for their creative endeavors. ... So viewed, the information

circle becomes the unit of life.”--Werner Loewenstein

The Touchstone of Life (1999)Calit2’s

Information Theory and Applications Center

http://ita.ucsd.edu

PI Larry Smarr

Paul Gilna Ex. Dir.

Announced January 17, 2006$24.5M Over Seven Years

Marine Genome Sequencing Project – Measuring the Genetic Diversity of Ocean Microbes

Sorcerer II Data Will Double Number of Proteins in GenBank!

Specify Ocean Data

Each Sample ~2000

Microbial Species

Plus 155 Marine

Microbial Genomes

Enormous Increase in Scale of Known Genes Over Last Decade

1995First Microbe Genome

2007Ocean Microbial Metagenomics

6.3 Billion Bases 5.6 Million Genes

1.8 Million Bases 1749 Genes

~3300x

CAMERA 1.1 is Up and Running!

Can We Create a “Facebook” for Science Researchers? Microbial Metagenomics as a Cyber-Community

1300 Registered CAMERA Users From 45 Countries

From Microbial Genomes To Human Disease

• Microbes Have a Much Simpler Genome Than Humans

• However, Microbes Share Many of the Core Components of the Molecular Signaling Machinery Used by Humans

• Understand Both the Evolution and Regulation of Signaling Systems, First in Microbes and Then in Humans

• We Illustrate This Using the Protein Kinase Superfamily That is Implicated in Numerous Human Diseases

Source: Susan Taylor, SOM, UCSD

Identified 15,000 New Kinases

In Venter Global Ocean Sampling Data

Medically Relevant Metagenomic Data SetsAre Rapidly Being Accumulated

• “A majority of the bacterial sequences corresponded to uncultivated species and novel microorganisms.”

• “We discovered significant inter-subject variability.” • “Characterization of this immensely diverse ecosystem is the first step in

elucidating its role in health and disease.”

“Diversity of the Human Intestinal Microbial Flora” Paul B. Eckburg, et al Science (10 June 2005)

395 Phylotypes

Full Genome Sequencing is Exploding:Most Sequenced Genomes are Bacterial

www.genomesonline.org

>100Metagenomes

First Genome 1995 6 Genomes/ Year 20001600 Genomes 2007

Moore Foundation Funded the Venter Institute to Provide the Full Genome Sequence of 155+ Marine Microbes

Phylogenetic Trees Created by Uli Stingl, Oregon State

Blue Means Contains One of the Moore 155 Genomes

www.moore.org/microgenome/trees.aspx

Acidobacteria

Bacteroides

Fibrobacteres

Gemmimonas

Verrucomicrobia

Planctomycetes

Chloroflexi

Proteobacteria

Chlorobi

FirmicutesFusobacteria Actinobacteria

Cyanobacteria

Chlamydia

Spriochaetes

Deinococcus-Thermus

Aquificae

Thermotogae

TM6OS-K

Termite GroupOP8

Marine GroupAWS3

OP9

NKB19

OP3

OP10

TM7

OP1OP11

Nitrospira

SynergistesDeferribacteres

Thermudesulfobacteria

Chrysiogenetes

Thermomicrobia

Dictyoglomus

Coprothmermobacter

Well sampled phyla

No cultured taxa

DOE Genomic Encyclopedia of Bacteria and Archaea (GEBA) / Bergey Solution: Deep Sampling Across Phyla

Source: Eddie Rubin, DOE JGI

2007 Goal: Finish ~100 Bacterial and Archaeal Genomes from Culture Collections

Project Lead -- Jonathan Eisen (JGI/UC Davis)

The Bioinformatics Core of the Joint Center for Structural Genomics is Housed in the Calit2@UCSD Building

Extremely Thermostable -- Useful for Many Industrial Processes (e.g. Chemical and Food)

173 Structures (122 from JCSG)

• Determining the Protein Structures of the Thermotoga Maritima Genome • 122 T.M. Structures Solved by JCSG (75 Unique In The PDB) • Direct Structural Coverage of 25% of the Expressed Soluble Proteins• Probably Represents the Highest Structural Coverage of Any Organism

Source: John Wooley, UCSD

Interactive Exploration of the Proteins of the Marine Microbe Thermatoga

Building Genome-Scale Models of Living Organisms

• E. Coli– Has 4300

Genes– Model Has

2000!

Regulatory Actions

Input Signals

Monomers &Energy

Proteins

Genomics

Transcriptomics

Proteomics

Metabolomics

EnvironmentInteractomics

Transcription &Translation

Metabolism

Regulation

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

E4PX5PGLC

G6P

F6P

FDP

DHAP

3PG

DPG

GA3P

2PG

PEP

PYR

AcCoA

SuccCoA

SUCC

AKG

ICIT

CIT

FUM

MAL

OAA

Ru5P

R5P

S7P

6PGA 6PG

ACTPETH

ATP

NADPHNADH FADH

SUCCxt

pts

pts

pgi

pfkA

fba

tpi

fbp

gapA

pgk

gpmA

eno

pykFppsAaceE

zwfpgl gnd

rpiA

rpe

talAtktA1 tktA2

gltA

acnA icdA

sucA

sucC

sdhA1

frdA

fumA

mdh

adhE

AC

ackA

pta

pckA

ppc

cyoA

pnt1A

sdhA2nuoA

atpA

ACxtETHxt

O2O2xt

CO2 CO2xt

Pi Pixt

O2 trx

CO2 trx

Pi trx

EXTRACELLULARMETABOLITE

reaction/gene name

Map Legend

INTRACELLULARMETABOLITE

GROWTH/BIOMASSPRECURSORS

ETH trxAC trx

SUCC trx

acs

FOR

pflA

FORxt

FOR trx

dld

LAC

LACxtLAC trx

PYRxt PYR trx

glpDgpsA

GL3P

GL glpK

GLxt

GL trx

GLCxtGLC trx

glk

RIB

rbsK

RIBxt

RIB trx

FORfdoH

pnt2A

H+ Qh2

GLX

aceA

aceB

maeB

sfcA

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi2nPi

Pi

b9

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi

Pi

b9

G1 + RNAP G1*

v1

nNTP

mRNA1 nNMPb4

b2

v2

v3=k1[mRNA1]

2aGTP

rib

rib1*

protein1b3

v4 (subject to global max.)

v5

aAA-tRNA

b7

2aGDP + 2aPib8

b5

b1 aAAatRNA

aATP

aAMP

+ 2aPi

b6

v6

2nPi2nPi

Pi

b9

Pi

b9

Gc2

tc2

Rc2

Pc2 Carbon2A

Oc2

Carbon1

(indirect)

(-)

If [Carbon1] > 0, tc2 = 0

G2a

t2a

R2a

P2a BC + 2 ATP + 3 NADH

O2a

B(+)

G5

t5

R5

P5 C + 4 NADH

O5

(+)

3 E

If R1 = 0, we say [B] is not in surplus, t2a = t5 = 0

G6a

t6a

R6a

P6aH

O6a

(-)

Hext

If Rh> 0, [H] is in surplus, t6a = 0

Gres

tres

Rres

Pres O2 + NADH

ATP

Ores

O2

(+)

G3b

t3b

R3b

P3bG

O3b

(+)

0.8 C + 2 NADH

If Oxygen = 0, we say [O2] = 0, tres= t3b = 0

G + 1 ATP + 2 NADH

Gc2

tc2

Rc2

Pc2 Carbon2A

Oc2

Carbon1

(indirect)

(-)

If [Carbon1] > 0, tc2 = 0

G2a

t2a

R2a

P2a BC + 2 ATP + 3 NADH

O2a

B(+)

G5

t5

R5

P5 C + 4 NADH

O5

(+)

3 E

If R1 = 0, we say [B] is not in surplus, t2a = t5 = 0

G6a

t6a

R6a

P6aH

O6a

(-)

Hext

If Rh> 0, [H] is in surplus, t6a = 0

Gres

tres

Rres

Pres O2 + NADH

ATP

Ores

O2

(+)

G3b

t3b

R3b

P3bG

O3b

(+)

0.8 C + 2 NADH

If Oxygen = 0, we say [O2] = 0, tres= t3b = 0

G + 1 ATP + 2 NADH

E. coli i2K

Source: Bernhard PalssonUCSD Genetic Circuits Research Group

http://gcrg.ucsd.edu

JTB 2002

JBC 2002

in Silico Organisms Now Available

2007:

•Escherichia coli •Haemophilus influenzae •Helicobacter pylori •Homo sapiens Build 1•Human red blood cell •Human cardiac mitochondria •Methanosarcina barkeri •Mouse Cardiomyocyte •Mycobacterium tuberculosis •Saccharomyces cerevisiae •Staphylococcus aureus

Biochemically, Genetically and Genomically (BiGG) Genome-Scale Metabolic Reconstructions

H. influenzae

H. pylori

S. aureus

S. typhimurium

M. barkeri• 619 Reactions• 692 Genes

S. cerevisiae• 1402 Reactions• 910 Genes

E. coli• 2035 Reactions• 1260 Genes

S. aureus• 640 Reactions• 619 Genes Mitoc.

• 218 Rxns

RBC• 39 Rxns

H. sapiens• 3311 Reactions• 1496 Genes

S. typhimurium• 898 Reactions• 826 Genes

H. pylori• 558 Reactions• 341 Genes

H. influenzae• 472 Reactions• 376 Genes

M. tuberculosis• 939 Reactions• 661 Genes

Systems Biology Research Grouphttp://systemsbiology.ucsd.edu

With Nanobioinfo Devices Everywhere, How Can We Handle the Data Flows?

• Mobile Broadband– 0.1-0.5 Mbps

• Home Broadband– 1-5 Mbps

• University Dorm Room Broadband– 10-100 Mbps

• Calit2 Global Broadband– 1,000-10,000 Mbps

100,000 Fold Range All Here Today!

“The future is already here, it’s just not evenly distributed”

William Gibson, Author of Neuromancer

The OptIPuter Project: Defining a High Performance Collaboration Infrastructure for Eng-Med

Picture Source:

Mark Ellisman,

David Lee, Jason Leigh

Calit2 (UCSD, UCI) and UIC Lead Campuses—Larry Smarr PIUniv. Partners: SDSC, USC, SDSU, NW, TA&M, UvA, SARA, KISTI, AIST

Industry: IBM, Sun, Telcordia, Chiaro, Calient, Glimmerglass, Lucent

$13.5M Over Five

Years

Scalable Adaptive Graphics

Environment (SAGE)

My OptIPortalTM – AffordableTermination Device for the OptIPuter Global Backplane

• 20 Dual CPU Nodes, Twenty 24” Monitors, ~$50,000• 1/4 Teraflop, 5 Terabyte Storage, 45 Mega Pixels--Nice PC!• Scalable Adaptive Graphics Environment ( SAGE) Jason Leigh, EVL-UIC

Source: Phil Papadopoulos SDSC, Calit2

OptIPuter Scalable Displays Are Used for Multi-Scale Biomedical Imaging

Green: Purkinje CellsRed: Glial CellsLight Blue: Nuclear DNA

Source: Mark

Ellisman, David Lee,

Jason Leigh

Two-Photon Laser Confocal Microscope Montage of 40x36=1440 Images in 3 Channels of a Mid-Sagittal Section

of Rat Cerebellum Acquired Over an 8-hour Period

200 Megapixels!

Scalable Displays Allow Both Global Content and Fine Detail

Allows for Interactive Zooming from Cerebellum to Individual Neurons

Interactive Exploration of Marine Genomes Using 100 Million Pixels

Ginger Armburst (UW), Terry Gaasterland (UCSD SIO)

Nearly One Half Billion Pixelsin Calit2 Extreme Visualization Project!

Connected at 2,000 Megabits/s!

UC Irvine

UC San Diego

UCI HIPerWall Analyzing Pre- and Post- Katrina

Falko Kuester, UCSD; Steven Jenks, UCI

NW!

CICESE

UW

JCVI

MIT

SIO UCSD

SDSU

UIC EVL

UCI

OptIPortals

OptIPortal

An Emerging High Performance Collaboratoryfor Microbial Metagenomics

UC Davis

UMich

3D OptIPortal Calit2 StarCAVE Telepresence “Holodeck”

60 GB Texture Memory, Renders Images 3,200 Times the Speed of Single PC

Source: Tom DeFanti, Greg Dawe, Calit2Connected at 200 Gb/s

30 HD Projectors!

www.glif.is

Created in Reykjavik, Iceland 2003

Countries are Aggressively Creating Gigabit Services:Interactive Access to CAMERA Data System

Visualization courtesy of Bob Patterson, NCSA.