Post on 25-Feb-2016
description
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.
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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 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 pyloriKlebsiella pneumoniaeLactococcus lactisLeuconostoc oenosListeria monocytogenesNeisseria gonorrhoeaeNeisseria meningitidisPasteurella multocidaPorphyromonas gingivalisProteus mirabilisSalmonella paratyphiSalmonella typhiSalmonella typhimurium
Bacillus anthracisBacillus haloduransBacillus subtilisBorrelia burgdorferiCampylobacter jejuniClostridium acetobolyticumClostridium difficileClostridium perfringensDeinococcus radioduransEscherichia coliEnterococcus faecalisHaemophilus influenzae
Shewanella putrefaciensStaphylococcus aureusStaphylococcus epidermidisStreptococcus gordoniiStreptococcus mutansStreptococcus pneumoniaeStreptococcus pyogenesVibrio anguillarumVibrio choleraeVibrio harveyiVibrio vulnificusYersinia pestis
Thanks 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