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

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

3. 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 Objective

4. 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 Objective

5. Interferon Signaling Pathway Antivirus mRNA Ribosome [1] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) [2] Adapted from Virtual Cell [1] [2]

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

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] Adapted from Ken Shuai and Ben Liu Regulation of Gene Activation Pathways by PIAS Proteins in the immune system (2005) [2][1] [2] Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)

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

9. Constant Flow C1C1 C2C2 k(C 1 - C 2 ) C2C2 Mass Transfer Figure 1 is adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) C1C1 F. C 1 Cytoplasm Nucleus Mass Transfer Coefficient Cytoplasm Nucleus

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

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

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

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

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) Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002) [1]

16. Current Project 1.Determine values or estimates of unknown parameters 2.Experimentally acquire significant unknown parameters 3.Develop computational methods for simulation 4.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

27. SUMOylation by PIAS Sumoylation = Post Translational Modification to a protein (STAT dimers) 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 Small Ubiquitin related Modifier (SUMO) Ubiquitin = mark protein for destruction *http://en.wikipedia.org/wiki/Peptide_bond STAT* PIAS SENP STAT* sumo “peptide bond” Adapted from Ken Shuai and Ben Liu Regulation of Gene Activation Pathways by PIAS Proteins in the immune system (2005) activation Ligation conjugation “Isopeptide Bond”