Modeling the Interferon Signaling Process of the Immune Response

1. Modeling the Interferon Signaling Process of the Immune Response Jeffrey Suhalim Dr. Jiayu Liao and Dr. V. G. J. Rodgers BRITE

2. Introduction IFN Foreign Substance (i.e. Virus) AV

3. Objective • Develop a mathematical model to describe the interferon signaling process • Use the model to predict the antivirus activity response quantitatively • Significance: novel medical treatment for viral infection

4. Objective • Develop a mathematical model to describe the interferon signaling process • Use the model to predict the antivirus activity response quantitatively • Significance: novel medical treatment for viral infection

5. [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) [2] Adapted from Virtual Cell <http://vcell.ndsu.nodak.edu/~christjo/vcell/animationSite/translation/elongation.html> Interferon Signaling Pathway Antivirus mRNA [2] Ribosome [1]

6. Internal Control Mechanisms [2] JAK Inhibition by SOCS reduce the production of STAT dimer [1] [2] [1] Adapted from Danielle L. Krebs and Douglas J. Hilton SOCS Proteins: Negative Regulators of Cytokine Signaling (2001) [2] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

7. Internal Control Mechanisms SUMOylation by PIAS reduce the number of active STAT dimer in the nucleus SUMOylation is modification to a substrate by conjugating SUMO-protein PIAS (Protein Inhibitor of Activated STATs) provides specificity to assist SUMO conjugation to the substrate [1] [2] [1] Adapted from Ken Shuai and Ben Liu Regulation of Gene Activation Pathways by PIAS Proteins in the immune system (2005) [2] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

8. Model Lumped Parameter Model 3 compartments: Near Surface Region Ordinary Differential Equation Cytoplasm Nucleus Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

9. C1 C2 C2 k(C1- C2) Mass Transfer Constant Flow C1 F . C1 Cytoplasm Cytoplasm Nucleus Mass Transfer Coefficient Nucleus Figure 1 is adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) Cytoplasm Nucleus

10. Antivirus mRNA [1] Model Development “Near Surface Region” [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

11. Model Development “Near Surface Region” Antivirus mRNA [1] [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

12. Model Development “Near Surface Region” Antivirus PIAS mRNA [1] [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

13. Lumped Parameter Model Near Surface Region (3 total equations) [1] “Near Surface Region” Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

14. Lumped Parameter Model Cytoplasm (16 total equations) [1] Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

15. Lumped Parameter Model Nucleus (8 total equations) [1] Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

16. Current Project • Determine values or estimates of unknown parameters • Experimentally acquire significant unknown parameters • Develop computational methods for simulation • Predict the antivirus activity quantitatively

17. Acknowledgement • Dr. Jiayu Liao and Dr. V. G. J. Rodgers • Dr. Liao’s lab group • Dr. Rodgers’ B2K group • BRITE

18. References • Johnson, Erica S.. 2004. ”Protein Modification by SUMO.”. Annu. Rev. Biochem..73, 355–82 • Krebs, Danielle L. and Hilton, Douglas J.. 2001. “SOCS Proteins: Negative Regulators of Cytokine Signaling.” Stem Cells. 19: pp. 378-387 • Levy, David E., and Darnell Jr, J. E.. 2002. “STATs: Transcriptional Control and Biological Impact” Nature Reviews: Molecular Biology. Volume III. • Liao, Jiayu. 1999. Ph.D. thesis, UCLA. • Rawlings, Jason S., Rosler, Kristin M., and Harrison, Douglas A.. 2004. The JAK/STAT signaling Pathway. Journal of Cell Science 117 (8):1281-1283 • Shuai, Ken and Liu, Ben.. 2005. “Regulation of Gene Activation Pathways by PIAS Proteins in the immune system.” Nature Reviews: Immunology. • Wormald, Samuel and Hilton, Douglas J. “Inhibitors of Cytokine Signal Transduction.” 2004. The Journal of Biological Chemistry. Vol. 279, No. 2, Issue of January 9, pp. 821–824

24. MATLAB

25. Background Information • Innate immune response • Cytokine • Interferon • JAK-STAT pathway • STAT dimer  PKR  inhibit ribosome  Apotosis • Potential application: • Induce the activity of p53 • PCD only when the cell is infected  replace chemotherapy drugs

26. JAK-STAT Pathway [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) [2] Adapted from Virtual Cell <http://vcell.ndsu.nodak.edu/~christjo/vcell/animationSite/translation/elongation.html>

27. SUMOylation by PIAS The activation and conjugation process will be assumed to react spontaneously and and thus, “SUMO-UBC9” complex is always available in the system. The only reaction described in the model is the ligation The only reaction described in the model is the ligation Small Ubiquitin related Modifier (SUMO) Ubiquitin = mark protein for destruction activation “Isopeptide Bond” conjugation Ligation Adapted from Ken Shuai and Ben Liu Regulation of Gene Activation Pathways by PIAS Proteins in the immune system (2005) Sumoylation = Post Translational Modification to a protein (STAT dimers) PIAS STAT* STAT*sumo “peptide bond” SENP *http://en.wikipedia.org/wiki/Peptide_bond