An integrated approach to computational Systems Biology:

1. An integrated approach to computational Systems Biology: from bimolecular interactions to the behavior of multi-cellular systems Martin Meier-Schellersheim Computational Biology Group Program in Systems Immunology and Infectious Disease Modeling NIAID, NIH Systems Biology Summit, Richmond VA 2007

2. Introduction computer chip capable of infinite number of operations per second common cold remains elusive particle accelerator to circle Australia

3. Introduction Biology, because of its large amounts of data and large networks of interacting components seems to be the ideal field of application for computer science: Data processing and visualization, abstraction and usage of the tool repertoire of mathematics and computer science. Computing in biology has been very successful... …but mainly as a tool to organize data according to previously conceived hypotheses and much less as a tool to develop new large-scale quantitative models that allow us to predict biological behavior.

4. Computational Biology Computational biology has not yet become a ‘mainstream’ tool among biologists. Why?

5. difficult to read. • interaction equations have to be constructed ‘by hand’. • as equations: unaccessible to non-mathematicians. • difficult to modify. Ga SBML file? Gbg PIP3 PTEN set of equations? PIP2 cAMP cAMP cAMP complicated interaction graph? Bridging the gap between biology and computer science How do you describe molecular reaction networks? conventional approaches • difficult to capture / represent spatial aspects. • difficult to bridge scales (from molecules to cell populations). • no representation of multi-molecular complexes.

6. multicellular systems intracellular organization reaction networks molecular interactions Developing computational models from molecular interactions to multi-cellular systems

7. Application chemotaxis / from molecular interactions to signaling network dynamics A new kind of modeling approach: instead of using scripts or differential equations we use graphical symbols representing molecules, molecule complexes and their interactions: • Based on the user input of bimolecular interactions our software automatically builds the resulting signaling networks and their mathematical representations. • Molecules and interactions can be added or removed easily. The software adapts the signaling network automatically.

8. multicellular systems intracellular organization reaction networks molecular interactions Developing computational models from molecular interactions to multi-cellular systems

9. Application chemotaxis / from molecular interactions to signaling network dynamics Visualization of the dynamics of the automatically generated intracellular signaling networks:

10. multicellular systems intracellular organization reaction networks molecular interactions Developing computational models from molecular interactions to multi-cellular systems

11. Simulation technology: how do you capture biologically relevant effects depending on specific cellular morphologies and inhomogeneous molecular concentrations? A T-cell makes contact with an Antigen Presenting Cell (blue). CD43 (green) is excluded from the contact area (immunological synapse). (Delon, Jérôme, Stoll, Sabine & Germain, Ronald N. Imaging of T-cell interactions with antigen presenting cells in culture and in intact lymphoid tissue. Immunological Reviews189 (1), 2002) From molecular reactions to cellular morphology

12. From molecular reaction to cellular morphology Turning a confocal microscopy z-stack into a computerized representation of the cell… microscope

13. From molecular reaction to cellular morphology Turning a confocal microscopy z-stack into a computerized representation of the cell… APC (B cell)

14. multicellular systems intracellular organization reaction networks molecular interactions Developing computational models from molecular interactions to multi-cellular systems

15. From single cells to cellular populations Sub-cutaneous microscopy of neutrophils migrating towards a site of tissue damage and Leishmania infection:

16. From single cells to cellular populations Patterns of neutrophil chemotaxis in different fields of chemoattractants: E.F. Foxman, J. Campbell, E.C. Butcher, JCB 139, 1997

17. movement along gradient of receptor occupation response movement stimulus From single cells to cellular populations Simmune allows the modeler to couple detailed cellular biochemistry to higher level ‘stimulus-response’ rules: (Collaboration with E. Butcher and E. Kunkel, Stanford) Cells express 10000 receptors for each chemokine. Ligation of CRs by CKs results in formation of F-actin. Cells move along an intracellular gradient of polymerized actin at 20 mm / min. Engaged receptors are internalized and stripped of CK, then returned to the cell surface with a 6 minute delay.

20. From single cells to cellular populations: cellular stimulus-response mechanisms

24. Application: chemotaxis Chemotaxis of Dictyostelium D. in a gradient of cAMP.

25. Application: chemotaxis Chemotaxis of Dictyostelium D. in a gradient of cAMP: Modeling the underlying chemosensing signaling processes requires spatially resolved computational representations of quite complex intracellular reaction -diffusion networks. X. Xu and T. Jin, LIG, NIAID, NIH

26. Application: chemotaxis Fundamental biochemistry of eukaryotic chemotaxis: Comer and Parent, Cell 2002

27. Application: chemotaxis Signal-dependent regulation of PI3K activity: recruitment of local regulators

28. Application: chemotaxis Distribution of signaling components in chemotacting Dictyostelium (PIP3 reporter) Comer and Parent, Cell 2002

29. Application chemotaxis The simulation tool allows modelers to place molecules and cells at any time into 3D spatial simulations of multi-cellular systems. 2D cut through 3D simulated cell with intracellular reaction-diffusion The intracellular biochemistry can be investigated for each single cell.

30. prediction from standard model experimental data Application: chemotaxis Simulated behavior of PIP3, membrane bound PTEN and active PI3K at the front of a cell responding to a cAMP gradient:

31. a b c d f e mean conc: 1 mm mean conc: 100 nm mean conc: 10 nm computational prediction experimental confirmation Figure 3: Predicted [a, b, c] and measured [d, e, f] time courses of PIP3 change (as measured by GFP-PH localization in live cells) at the back (blue) and front (pink) of the cells. Mean cAMP concentrations at cell surface: a,d) 1 mmol; b,e) 100 nmol; c,f) 10 nmol. The curve in e) has been used to adjust model parameters to obtain b). a) and c) are predictions.

32. Application: chemotaxis Distribution of signaling components in chemotacting Dictyostelium (PIP3 reporter) Comer and Parent, Cell 2002

33. Ga Gbg PTEN PTEN PTEN ? PIP2 cAMP cAMP cAMP cAMP cAMP Application: chemotaxis What regulates the localization of PTEN? PIP3 PIP3 PIP2 PIP3 PIP2 PIP3 PIP2 PIP2 PIP3 Comer and Parent, Cell 2002 (PIP3 added for presentation.)

34. Application: chemotaxis Signal-dependent regulation of PTEN localization and activity:

35. experimental data prediction from ‘standard model’ Application: chemotaxis Simulated behavior of PIP3, membrane bound PTEN and active PI3K at the front of a cell responding to a cAMP gradient:

36. c a b d e f mean conc: 1 mm mean conc: 100 nm mean conc: 10 nm computational prediction experimental confirmation Figure 4: Predicted [a, b, c] and measured [d, e, f] time courses of changes in membrane-bound PTEN at the back (green) and front (red) of the cells. Mean cAMP concentrations at cell surface: a,d) 1 mmol; b,e) 100 nmol; c,f) 10 nmol.

37. intracellular organization reaction networks molecular interactions Developing computational models from molecular interactions to multi-cellular systems multicellular systems

38. Outlook Challenges ahead: Realistic biological models require large amounts of quantitative data. We need incentives and standards (reproducibility) for quantitative experimental data.

39. 3D reconstruction of a lymph node Outlook Sub-cutaneous microscopy of neutrophils migrating towards a site of tissue damage and Leishmania infection:

40. Outlook Challenges ahead: Realistic biological models require large amounts of quantitative data. We need incentives and standards (reproducibility) for quantitative experimental data. Necessity to understand cellular function in vivo – how closely can our computational models capture in vivo morphology?

41. Acknowledgments Program in Systems Immunology and Infectious Disease Modeling (PSIIM), NIAID Ron Germain, director Frederick Klauschen, Bastian Angermann, Alison Wise, Tony Zhang LBS, LI, NIAID Jackson Egen LIG, NIAID Tian Jin, Xuehua Xu Stanford University Eugene Butcher, Eric Kunkel Moscow University Gennady Bocharov National Institute for Allergy and Infectious Diseases