1. Satellite Workshop: Information Processing in the Biological Organism (A Systems Biology Approach) Fred S. Roberts Rutgers 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. 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 pestis Thanks to Bonnie Bassler

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

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. Is the damage repairable? Apoptosis no Cell cycle arrest G1/S G2/M One cell death = Protection of the whole organism yes DNA repair Stress signals p53 MDM2 The p53 Network 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. Ca 2+ Release Channels (RyR) L-Type Ca 2+ Channel 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

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

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