Satellite Workshop: Information Processing in the

Biological Organism(A Systems Biology Approach)

Fred S. RobertsRutgers University

We are all well aware by now that many fundamental biological processes involve the flow of information.

TTAGGCCCCAATGTGTCCCGATTGAA

The potential for dramatic new biological knowledge arises from investigating the complex interactions of many different levels of biological information.

Levels of Biological InformationDNA

mRNA

Protein

Protein interactions and biomodules

Protein and gene networks

Cells

Organs

Individuals

Populations

EcologiesThanks to Leroy Hood

The workshop investigated information processing in biological organisms from a systems point of view.

Thanks to Leroy Hood

The list of parts is a necessary but not sufficient condition for understanding biological function.

Understanding how the parts work is also important.

But it is not enough. We need to know how they work together. This is the systems approach.Thanks to Gustavo Stolovitzky

Understanding biological systems from this point of view can be greatly aided by the use of powerful mathematical and computer models.

The Workshop Was Organized Around Four Themes:

•Genetics to gene-product information flows.•Signal fusion within the cell.•Cell-to-cell communication.•Information flow at the system level, includingenvironmental interactions.

There was also a session on new challenges for mathematics, computer science, and physics.

Example 1:Informationprocessing between bacteriahelps this squid in the dark.

Bonnie BasslerPrinceton Univ.

Bacteria process the information about the local density of other bacteria. They use this to produce luminescence.The process involved can be modeled by a mathematical model involving quorum sensing.

Similar quorum sensing has been observed in over 70 species

Helicobacter pylori

Klebsiella pneumoniae

Lactococcus lactis

Leuconostoc oenos

Listeria monocytogenes

Neisseria gonorrhoeae

Neisseria meningitidis

Pasteurella multocida

Porphyromonas gingivalis

Proteus mirabilis

Salmonella paratyphi

Salmonella typhi

Salmonella typhimurium

Bacillus anthracis

Bacillus halodurans

Bacillus subtilis

Borrelia burgdorferi

Campylobacter jejuni

Clostridium acetobolyticum

Clostridium difficile

Clostridium perfringens

Deinococcus radiodurans

Escherichia coli

Enterococcus faecalis

Haemophilus influenzae

Shewanella putrefaciens

Staphylococcus aureus

Staphylococcus epidermidis

Streptococcus gordonii

Streptococcus mutans

Streptococcus pneumoniae

Streptococcus pyogenes

Vibrio anguillarum

Vibrio cholerae

Vibrio harveyi

Vibrio vulnificus

Yersinia pestisThanks to Bonnie Bassler

Example 2: The P53-MDM2 Feedback Loop

and DNA Damage Repair

Kohn, Mol Biol Cell, 1999 Uri Alon, Weizmann InstituteGalit Lahav, Harvard University

P53-CFP

Mdm2-YFP

Network motifs are conceptual units that are dynamic and larger than single components such as genes or proteins. Such motifs have helped to understand the nonlinear dynamics of the process by which the P53 - MDM2 feedback loop contributes to the regulation of DNA damage repair.

Is the damage repairable?

Apoptosis

no

Cell cycle arrestG1/S G2/M

One cell death =

Protection of the whole organism

yes

DNA repair

Stress signals p53 MDM2

The p53 Network

Thanksto GalitLahav

Example 3: Mathematical Modeling of Multiscale phenomena arising in excitation/contraction coupling in the heart.

Raimond Winslow, Johns HopkinsCanine Heart

Ca2+ ReleaseChannels (RyR)

L-Type Ca2+

Channel

<-10 nm->

•The models study the stochastic behavior of calcium release channels.•Model components range in size from 10 nanometers to 10 centimeters. •The work has application to the connection between heart failure and sudden cardiac death.

Thanks to Raimond Winslow

Calcium release unit in the myocite

Challenge 1: Methods to go from DNA to RNA to Protein to

Systems

Thanks to Leroy Hood

Challenge 2: Methods to Deal with Multiscale Models: Spatial Structure, Temporal Dynamics

Challenge 3: Develop Models that are “Reusable”, Portable,

Transportable

Challenge 4: “Reverse Engineering”Go from the behavior of an airplane to a

blueprint of how it is put together.

Go from observations about development to a gene regulatory network.

Next slide thanks to Leroy Hood

Endo-Mes

Data mapping to Endomesoderm model

June 20th, 2001

TBr

PMC

Sm50

Repressor of Delta

Hnf 6

DeltaHbx12

MV2L

Krox Otx

7th-9th cleavage

micendomes

Eve

Lim

Mat Otx

Repressor of Otx

Gcm

GataC

Dpt Pks

Mes

to 4th – 6th CleavageEndo-Mes

NK1

FoxABra

UI

Endo16

Endo

GataE

Nrl Hox11/13b

FoxB

Veg1

Late Wnt8signal from veg2

NucMat Otx

Repressor of Wnt8

nTCF

Mat c

frizzled

GSK-3

LiCl

Wnt8

Maternal & earlyinteractions

Interactionsin definitiveterritories

YN

E(S) ? Hmx

nTCF

FrzGSK-3

LiCl

Wnt8

c

Krox Otx

(Outside endomes?)

Repressor of TBr

Terminal orperipheral downstream genes

Delta ?

Apo bec Kakapoo

Cyclophillin, EpHx, Ficolin, Sm37, Sm30Sm27, Msp130, MSP130L

Repressor of Wnt8

OrCTCAPK

Ub

Su(H)+

SoxB1Krl

Ub

Preliminary Regulatory Network in the Sea Urchin for Endomesodermal Development

Endo-Mes

Data mapping to Endomesoderm model

June 20th, 2001

TBr

PMC

Sm50

Repressor of Delta

Hnf 6

DeltaHbx12

MV2L

Krox Otx

7th-9th cleavage

micendomes

Eve

Lim

Mat Otx

Repressor of Otx

Gcm

GataC

Dpt Pks

Mes

to 4th – 6th CleavageEndo-Mes

NK1

FoxABra

UI

Endo16

Endo

GataE

Nrl Hox11/13b

FoxB

Veg1

Late Wnt8signal from veg2

NucMat Otx

Repressor of Wnt8

nTCF

Mat c

frizzled

GSK-3

LiCl

Wnt8

Maternal & earlyinteractions

Interactionsin definitiveterritories

YN

E(S) ? Hmx

nTCF

FrzGSK-3

LiCl

Wnt8

c

Krox Otx

(Outside endomes?)

Repressor of TBr

Terminal orperipheral downstream genes

Delta ?

Apo bec Kakapoo

Cyclophillin, EpHx, Ficolin, Sm37, Sm30Sm27, Msp130, MSP130L

Repressor of Wnt8

OrCTCAPK

Ub

Su(H)+

SoxB1Krl

Ub

Gene Regulatory Network in the Sea Urchin for Endomesodermal Development

Support of Research: Databases

•Databases of Data•Databases of Models

•There are Major accompanying research challenges

Data Cleaning

Data Visualization

Data Mining

“Curation” of Databases•Error correction•Validation of Data•Updating•Interoperability

The Development of Methods to Handle Large,

Heterogeneous Data Sets

The Developing Partnership between the Biological and Mathematical Sciences

•Math/CS help Bio: New algorithms, new numerical methods for simulation, etc.

•Biology problems stimulate Math/CS research.

The Developing Partnership between the Biological and Mathematical Sciences

•Biological research leads to new paradigms in Math/CS:

•Biological architectures suggest new computer architectures•The exquisite sensitivity and dynamic range of biological sensors aid in the design of new sensors•Biological computing

•National Science Foundation

•Gary Strong•Co-Chair: Eduardo Sontag•Moderators:

•Tom Deisboeck, Harvard•Leslie Loew, UConn•Stas Shvartsman, Princeton•Joel Stiles, CMU•Gustavo Stolovitzky, IBM