Satellite Workshop: Information Processing in the Biological Organism (A Systems Biology Approach) Fred S. Roberts Rutg

1. Satellite Workshop: Information Processing in the Biological Organism(A Systems Biology Approach)Fred S. RobertsRutgers University

2. 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.

3. Levels of Biological Information DNA mRNA Protein Protein interactions and biomodules Protein and gene networks Cells Organs Individuals Populations Ecologies Thanks to Leroy Hood

4. The workshop investigated information processing in biological organisms from a systems point of view. Thanks to Leroy Hood

5. 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

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

7. 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.

8. Example 1: Information processing between bacteria helps this squid in the dark. Bonnie Bassler Princeton Univ.

9. 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

10. Bacillus anthracis Bacillus halodurans Bacillus subtilis Borrelia burgdorferi Campylobacter jejuni Clostridium acetobolyticum Clostridium difficile Clostridium perfringens Deinococcus radiodurans Escherichia coli Enterococcus faecalis Haemophilus influenzae 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 Shewanella putrefaciens Staphylococcus aureus Staphylococcus epidermidis Streptococcus gordonii Streptococcus mutans Streptococcus pneumoniae Streptococcus pyogenes Vibrio anguillarum Vibrio cholerae Vibrio harveyi Vibrio vulnificus Yersinia pestis Thanks to Bonnie Bassler

11. P53-CFP Mdm2-YFP Example 2: The P53-MDM2 Feedback Loop and DNA Damage Repair Kohn, Mol Biol Cell, 1999 Uri Alon, Weizmann Institute Galit Lahav, Harvard University

12. 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.

13. The p53 Network Stress signals Cell cycle arrest G1/S G2/M no yes Apoptosis DNA repair MDM2 p53 One cell death = Protection of the whole organism Is the damage repairable? Thanks to Galit Lahav

14. Example 3: Mathematical Modeling of Multiscale phenomena arising in excitation/contraction coupling in the heart. Raimond Winslow, Johns Hopkins Canine Heart

15. 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

16. Challenge 1: Methods to go from DNA to RNA to Protein to Systems Thanks to Leroy Hood

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

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

19. 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

20. Preliminary Regulatory Network in the Sea Urchin for Endomesodermal Development Gene Regulatory Network in the Sea Urchin for Endomesodermal Development Data mapping to Endomesoderm model June 20th, 2001 Mat cb Mat Otx LiCl Data mapping to Endomesoderm model June 20th, 2001 Mat cb Mat Otx LiCl a2 GSK-3 frizzled Nuc Mat Otx a2 GSK-3 Endo-Mes frizzled Nuc Mat Otx Endo-Mes nbTCF Maternal & early interactions nbTCF Maternal & early interactions Wnt8 Wnt8 SoxB1 Krl Otx Krox b SoxB1 Krl Otx Krox b Repressor of Wnt8 LiCl Repressor of Wnt8 LiCl (Outside endomes?) (Outside endomes?) Interactions in definitive territories cb a2 GSK-3 Frz Repressor of Otx Interactions in definitive territories cb a2 GSK-3 Frz to 4th – 6th Cleavage Endo-Mes Repressor of Otx to 4th – 6th Cleavage Endo-Mes nbTCF nbTCF PMC Wnt8 a b PMC Otx Krox Wnt8 7th-9th cleavage a b Otx Krox MV2L Late Wnt8 signal from veg2 7th-9th cleavage micendomes MV2L Late Wnt8 signal from veg2 micendomes n1 Hnf 6 n1 Y N Hnf 6 Y N Su(H)+ Su(H)+ GataE Bra Repressor of Delta FoxA Repressor of Wnt8 GataE Bra Eve Repressor of Delta FoxA Repressor of Wnt8 Eve E(S) ? Hmx E(S) ? Hmx Ub Ub FoxB NK1 UI Lim Gcm FoxB NK1 UI Lim Gcm ? Delta Delta ? Hbx12 Delta Delta Hbx12 Ub GataC Ub Nrl Hox11/13b GataC Nrl Hox11/13b Repressor of TBr TBr Repressor of TBr TBr Mes Veg1 Endo Mes Veg1 Endo Cyclophillin, EpHx, Ficolin, Sm37, Sm30 Sm27, Msp130, MSP130L Cyclophillin, EpHx, Ficolin, Sm37, Sm30 Sm27, Msp130, MSP130L Terminal or peripheral downstream genes Terminal or peripheral downstream genes Sm50 CAPK Dpt Pks Apo bec Sm50 Kakapoo Endo16 OrCT CAPK Dpt Pks Apo bec Kakapoo Endo16 OrCT

21. Support of Research: Databases • Databases of Data • Databases of Models • There are Major accompanying research challenges

22. Data Cleaning

23. Data Visualization

24. Data Mining

25. “Curation” of Databases • Error correction • Validation of Data • Updating • Interoperability The Development of Methods to Handle Large, Heterogeneous Data Sets

26. 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.

27. 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

28. 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