Transcript Satellite Workshop: Information Processing in the Biological

Satellite Workshop:
Information Processing in the
Biological Organism
(A Systems Biology Approach)
Fred S. Roberts
Rutgers University
We are all well aware by now that many
fundamental biological processes involve
the flow of information.
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The potential for dramatic new biological
knowledge arises from investigating the
complex interactions of many different
levels of biological information.
Levels of Biological Information
DNA
mRNA
Protein
Protein interactions and biomodules
Protein and gene networks
Cells
Organs
Individuals
Populations
Thanks to Leroy Hood
Ecologies
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, including
environmental interactions.
There was also a session on new challenges for
mathematics, computer science, and physics.
Example 1:
Information
processing
between bacteria
helps this squid
in the dark.
Bonnie Bassler
Princeton 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
Bacillus anthracis
Helicobacter pylori
Bacillus halodurans
Klebsiella pneumoniae
Shewanella
putrefaciens
Staphylococcus aureus
Bacillus subtilis
Lactococcus lactis
Borrelia burgdorferi
Leuconostoc oenos
Campylobacter jejuni
Listeria monocytogenes Streptococcus gordonii
Neisseria gonorrhoeae Streptococcus mutans
Clostridium
acetobolyticum
Staphylococcus
epidermidis
Deinococcus
radiodurans
Neisseria meningitidis Streptococcus
Pasteurella multocida pneumoniae
Streptococcus
Porphyromonas
pyogenes
gingivalis
Vibrio anguillarum
Proteus mirabilis
Escherichia coli
Salmonella paratyphi
Vibrio cholerae
Enterococcus faecalis
Salmonella typhi
Vibrio harveyi
Haemophilus
influenzae
Salmonella
typhimurium
Vibrio vulnificus
Clostridium difficile
Clostridium
perfringens
Yersinia pestis
Thanks to Bonnie Bassler
Example 2: The P53-MDM2 Feedback Loop
and DNA Damage Repair
P53-CFP
Mdm2-YFP
Uri Alon, Weizmann Institute
Galit Lahav, Harvard University
Kohn, Mol Biol Cell, 1999
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.
The p53 Network
MDM2
p53
One cell death =
Protection of the
whole organism
Cell cycle arrest
G1/S
G2/M
Is the damage
repairable?
no
Apoptosis
yes
DNA repair
Thanks
to Galit
Lahav
Example 3: Mathematical
Modeling of Multiscale
phenomena arising in
excitation/contraction
coupling in the heart.
Raimond Winslow, Johns Hopkins
Canine Heart
•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.
Ca2+ Release
Channels
(RyR)
<-10 nm->
Calcium release unit
in the myocite
L-Type Ca2+
Channel
Thanks to Raimond Winslow
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
Preliminary
Regulatory
Network
in the
Urchin
Endomesodermal
Development
Gene Regulatory
Network
in the
SeaSea
Urchin
for for
Endomesodermal
Development
Mat cb
Mat cb
Nuc
Nuc
Mat
Mat
Otx
Otx
a2
a2
frizzle
frizzle
d
d
nbTCF
nbTCF
Endo-Mes
Endo-Mes
Wnt8
Wnt8
to 4th
– 6th
to
4th – 6th
Cleavage
Cleavage
Endo-Mes
Endo-Mes
MV2L
MV2L
cb
cb
a2
a2
Krl
Krl
LiCl
LiCl
GSK-3
GSK-3
SoxB1
SoxB1
Repressor
Repressor
of Wnt8
of Wnt8
Frz
Frz
(Outside
(Outside
endomes?)
endomes?)
Hnf 6
Hnf 6
Wnt8
Wnt8
7th
-9th cleavage
th-9th cleavage
7micendomes
micendomes
n1
n1 Y
b
b
Krox
Krox
nbTCF
nbTCF
PM
PM
C
C
Data mapping to
Data mapping
to
Endomesoderm
model
Endomesoderm
model
June 20th, 2001
June 20th, 2001
Mat Otx
Mat Otx
LiCl
LiCl
GSK-3
GSK-3
Krox
Krox
b
b
Maternal
Maternal
& early
&
early
interactions
interactions
Otx
Otx
Interactions
Interactions
in definitive
in
definitive
territories
territories
Repressor
Repressor
of Otx
of Otx
a Otx
a Otx
Late Wnt8
Late
signalWnt8
from
signal
veg2 from
veg2
Y
Su(H)+N
Su(H)+N
Repressor
Repressor
of Wnt8
of Wnt8
Hbx12
Hbx12
Repressor
Repressor
of TBr
of TBr
Repressor
Repressor
of Delta
of Delta
Ub
Ub
Delta
Delta
Ub
Ub
TBr
TBr
Cyclophillin,
Cyclophillin,
EpHx, Ficolin, Sm50
EpHx,
Ficolin, Sm50
Sm37, Sm30
Sm37,
Sm30
Sm27, Msp130,
Sm27,
Msp130,
MSP130L
Bra
Bra
E(S) ?
E(S) ?
FoxA
FoxA
Hmx
Hmx
Gcm
Gcm
Delta
Delta
NK1
NK1
?
?
GataC
GataC
Nrl
Nrl
Me
Me
s
s
CAPK
CAPK
UI
UI
Endo
Endo
Dpt
Dpt
Pks
Pks
Apo bec
Apo bec
Kakapoo Endo16 OrCT
Kakapoo Endo16 OrCT
GataE
GataE
FoxB
FoxB
Eve
Eve
Lim
Lim
Hox11/13b
Hox11/13b
Veg1
Veg1
Terminal or
Terminal
peripheralor
peripheral
downstream
downstream
genes
genes
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