Databases for Systems Biology

1. Databases for Systems Biology Herbert M Sauro Keck Graduate Institute Claremont, CA, 91711

2. Systems Biology

3. Systems Biology • Computational Systems Biology Group (Peter Spirtes) in Pittsburgh, Pennsylvania • Biochemical Networks Modeling Group (Pedro Mendes) at the Virginia Bioinformatics Institute Computational Systems Biology Group (Reinhard Laubenbacher) at the Virginia Bioinformatics Institute • Evolution of Molecular Networks group (Andreas Wagner) at the University of New Mexico • Systems biology group (Trey Idekeker) at the Whitehead Institute for Biomedical Research, Cambridge (USA) • Computational Cell Biology (Dennis Bray) at the University of Cambridge (UK) STRC Biocomputation Group (Hamid Bolouri) at the University of Hertfordshire • Computational Molecular Biology (Ron Shamir) at the University of Tel Aviv • Complex Systems Division (Carsten Peterson) at the University of Lund • Design Principles of Protein Networks (Uri Alon) at the Weizmann Institute • Design Principles of Protein Networks (Naama Barkai) at the Weizmann Institute • Probabilistic Graphical Models (Daphne Koller) at the University of Stanford • Molecular Biology and Probabilistic Models (Nir Friedman) at the Hewbrew University of Jerusalem • Systems Optimization Group (Eckart Zitzler) at the ETH Zürich • Protein Interaction Group (Benno Schwikowski) at the Systems Biology Institute, Seattle • Systems Biology Center at TU Delft • Integrative Systems Biology at TU Denmark • U Ghent • Institute for Advanced Study, Center for Systems Biology • Ron Weiss group, Princeton University • BII Systems Biology Group (Singapore) • UC San Francisco BioSystems Group • Kitano Systems Biology Group • Davidson Lab at Caltech • Bioinformatics & Systems Biology Group at the Burnham Institute (La Jolla) • Virtual Cell Project, U Connecticut • UC Santa Barbara IGERT Program on Systems Biology • UC San Diego Bioinformatics & Systems Biology Groups • UC San Diego Systems Biodynamics Group • Integrated Systems Biology Group at Rensselaer Polytechnic Institute Groups World-Wide

4. Systems Biology Institutes and Larger Initiatives • BioSPI Project at Weizmann • BioSPICE • BioMaps Institute at Rutgers: • Institute for Systems Biology, Seattle • Bauer Center for Genomics Research (CGR) at Harvard University • Systems Biology Department at Harvard Medical School • Computational and Systems Biology Initiative at MIT • Bio-X at Stanford University • Center for Studies in Physics and Biology at The Rockefeller University • GENSCEND Initiative of the Wellcome Trust • "Genomes to Life program" (a funding initiative of the DOE) • "Cell Systems Initiative" (an initiative of the University of Washington) • "Systems of Life - System Biology" (a funding initiative of the German Ministry of Education and Research, BMBF) • SFB 618 (funded by the German Research Council DFG) • STAGSIM - Systems Biology (An Expression of Interest (EoI) submitted to the EU Framework Program VI) • Systems Biology in Sweden • Institute for Computational Biomedicine at the Weill Medical College of Cornell University. • Pathways/Systems Biology Working Group at I3C.

5. Though coined 40 years ago,1 a lot of people still ask, "What's that?" when the term systems biology comes up. "It is used in so many different contexts, nobody is really clear what you mean by it," says John Yates III, a professor at the Scripps Research Institute in La Jolla, Calif. He's not the only one stumped by the term's meaning. David Placek, president of Sausalito, Calif.-based Lexicon Branding, a company that cooks up names for pharmaceutical products such as Velcade and Meridia, says he's not so hot on the moniker. "Systems biology is just so general that it could apply to many things. When you're naming a category, the underlying principle is that if you make a statement like, 'I'm doing systems biology,' do people know what you're talking about?'“…… Systems Biology Has its Backers and Attackers Revolution or buzzword du jour, pundits ponder a pervasive term | By Mignon Fogarty Volume 17 | Issue 19 | 27 Oct. 6, 2003, The Scientist

6. Systems Biology? High-throughput Data?

7. Systems Biology? Databases? PathDB

8. What is Systems Biology? • Understanding the principles of how physiological/phenotypic characteristics emerge from the properties of the components. • Predicting how these characteristics will change in response to alterations in the environment or system components.

9. What are we dealing with?

10. What are we dealing with? Mirit Aladjem et al., Stke, March 2004

11. Successful Models Yeast Glycolysis EGF Signaling Pathway Bas Teusink Red Blood Cell Calvin Cycle Yeast Cell Cycle Mulquiney, Joshi, Heinrich, … Poolman and Fell Frances Brightman et al John Tyson et al Trypanosoma Brucei Barbara Bakker, Westerhoff and Cornish-Bowden Chemotaxis, ecoli Many Contributors

12. Level of Complexity E. coli composition Molecule # Molecules per cell # of Types Protein 2,360,000 1000-2000 RNA 270,000 5 Small Molecules millions 500 Ions millions 20-30 http://biosci191.bsd.uchicago.edu/L02/ecoli.htm http://opbs.okstate.edu/5753/Composition%20table.html

13. Man-made Complex Devices Intel Pentium 4 42 million transistors

14. Man-made Complex Devices • The AMD Opteron • 105.9 million transistors • Number of gates > 54 Million

15. Man-made Complex Devices • The Intel Itanium 2 • 410 million transistors • Number of gates > 100 Million

16. Man-made Complex Devices • The Intel Itanium 2 • 410 million transistors • Number of gates > 100 Million By 2007 both Intel and AMD are predicting dies with 1 billion transistors

17. Man-made Complex Devices • The Intel Itanium 2 • 410 million transistors • Number of gates > 100 Million By 2007 both Intel and AMD are predicting dies with 1 billion transistors Many of the new graphics chips have over 60 million transistors AMD are working towards 45-nanometer transistors by 2007. The sizes of proteins vary from 2nm to 20 nm.

18. Man-made Complex Devices Probably by 2010, man-made devices will have comparable complexity to bacterial cells if not greater.

19. Cellular Models Building computational models of cells seems more and more like a viable project. Such a project would bring a much clearer understanding of how cellular systems are controlled and ultimately it should bring unprecedented predictive power.

20. Are Biologists Ready? Xo S1 S2 S3 S4 S5 S6 X1 v Xo and X1 fixed, all reactions reversible, assume stable steady state.

21. Are Biologists Ready? 50 % Xo S1 S2 S3 S4 S5 S6 X1 v What happens to the steady state? Xo and X1 fixed, all reactions reversible, assume stable steady state.

22. Are Biologists Ready? 50 % Xo S1 S2 S3 S4 S5 S6 X1 v Students reply: 1. Nothing happens. 2. Nothing happens unless it is the rate-limiting step. 3. The rate v goes down, but that’s all. 4. S3 goes up. 5. S4 goes down. 6. Species downstream of v go up. 7. Steady State flow changes but species levels don’t. 8. Xo and X1 change

23. Are Biologists Ready? 50 % Xo S1 S2 S3 S4 S5 S6 X1 v If we can’t understand this system how can we hope to understand:

24. Functional Motif Identification Computer simulation of EGF signal transductionPC12 cells. Frances Brightman, Simon Thomas and David Fell http://bms-mudshark.brookes.ac.uk/frances/fabweb5.htm 29 species

25. Functional Motif Identification Computer simulation of EGF signal transductionPC12 cells. Frances Brightman, Simon Thomas and David Fell http://bms-mudshark.brookes.ac.uk/frances/fabweb5.htm

26. Functional Motif Identification 27 components

27. Functional Motif Identification

28. Functional Motif Identification Amplifier Demodulator Resonance Detector

29. Functional Motif Identification Filter Power Amplifier Feedback Amplifier Pre-Amplifier Feedback Rectifier Audio Filter Carrier Filter Amplifier Demodulator

30. How Intel Engineers Cope Complex man-made devices are modeled and designed on multiple levels, each level may use different modeling techniques: Transistor Characteristics Basic Logic Gates Small Gate Modules Hierarchy of functional modules Top Level Module

31. How Intel Engineers Cope Complex man-made devices are modeled and designed on multiple levels, each level may use different modeling techniques: Transistor Characteristics Fundamental Protein Chemistry Basic Logic Gates Basic Enzyme Rate Characteristics Small Gate Modules Small Enzyme Motifs Hierarchy of functional modules Hierarchy of functional modules Top Level Module Top Level Module

32. Functional Motif Identification Negative Feedback in the MAPK Pathway yi At high amplifier gain (A k > 1): A k yo

33. Functional Motif Identification Negative Feedback in the MAPK Pathway At high amplifier gain (A k > 1): Linearization of the amplifier response. Without Feedback With Feedback

34. Functional Motif Identification E. coli Chemotaxis Signaling network reset Run Tumble Motor

35. Software Tools and Resources: • Software Infrastructure • Interchange Formats • Analysis Algorithms • Model Editors • Visualization • Model Databases Theoretical Foundation

36. Databases for Systems Biology • Kinetic Data • Network Information

37. Systems Biology Models

38. Systems Biology Models Simple first-order reaction kinetics Power Law

39. Systems Biology Models Simple irreversible Michaelis-Menten

40. Systems Biology Models Reversible Michaelis-Menten

41. Systems Biology Models Irreversible Allosteric Mechanism

42. Databases for Systems Biology The oldest known metabolic pathway is Yeast Glycolysis http://www.utoronto.ca/greenblattlab/yeast.htm http://www.utc.edu/Faculty/Becky-Bell/210-outline05.html

43. Databases for Systems Biology Hexokinase 2.7.1.1

44. Databases for Systems Biology Hexokinase 2.7.1.1 Glucose + ATP = G6P + ADP Km None available Specific Activity: 512 M/min/mg

45. Databases for Systems Biology Phosphofructokinase 2.7.1.11

46. Databases for Systems Biology Phosphofructokinase 2.7.1.11 ATP + F6P = ADP + FBP Km None available Specific Activity: 180 M/min/mg 148 M/min/mg 114 M/min/mg

47. Databases for Systems Biology Pyruvate Kinase 2.7.1.40

48. Databases for Systems Biology Pyruvate Kinase 2.7.1.40 PEP + ADP = Pyruvate + ATP Km ADP : 0.16 mM (+ FBP) Specific Activity: None available

49. Databases for Systems Biology • Kinetic equations • Values for kinetic constants plus standard errors • Conditions under which enzyme was characterized

50. Networks Network information is mainly Inaccessible in convenient formats, much work has to be done by the user to extract the desired information. without much work. The need for a model or network exchange format.