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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. TTAGGCCCCAATGTGTCCCGATTGAA 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 nbTCF nbTCF Endo-Mes Endo-Mes Wnt8 Wnt8 to 4th – 6th to 4th – 6th Cleavage Cleavage Endo-Mes Endo-Mes MV2L MV2L 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 7micendomes micendomes n1 n1 Y b b Krox Krox nbTCF nbTCF 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
