Modeling the Interferon Signaling Process of the Immune Response
Jeffrey SuhalimDr. Jiayu Liao and Dr. V. G. J. Rodgers
BRITE
• 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
• 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
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 <http://vcell.ndsu.nodak.edu/~christjo/vcell/animationSite/translation/elongation.html>
[1]
[2]
Internal Control Mechanisms
JAK Inhibition by SOCS reduce the production of STAT dimer
[1] Adapted from Danielle L. Krebs and Douglas J. HiltonSOCS 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]
Internal Control Mechanisms
SUMOylation by PIASreduce 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 LiuRegulation 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)
Lumped Parameter Model
3 compartments:
Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)
nconsumptioproducedoutin MMMMdt
dM
Model
Nucleus
Cytoplasm
Near Surface Region
Ordinary Differential Equation
Constant Flow
C1 C2
k(C1- C2)
C2
Mass Transfer
Figure 1 is adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)
C1
F . C1
Cytoplasm
Cytoplasm
Nucleus
Nucleus
Mass Transfer Coefficient
Cytoplasm
Nucleus
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”
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”
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”
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”
Lumped Parameter ModelCytoplasm (16 total equations)
[1]
Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)
Lumped Parameter ModelNucleus (8 total equations)
Fig. 1 Adapted from David E Levy and J. E. Darnell Jr. STATs: Transcriptional Control and Biological Impact (2002)
[1]
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
Acknowledgement
• Dr. Jiayu Liao and Dr. V. G. J. Rodgers
• Dr. Liao’s lab group
• Dr. Rodgers’ B2K group
• BRITE
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
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
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>
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
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
SENPSTAT*sumo
“peptide bond”
Adapted from Ken Shuai and Ben LiuRegulation of Gene Activation Pathways by PIAS Proteins in the immune system (2005)
activation
Ligation conjugation
“Isopeptide Bond”