Modeling the Interferon Signaling Process of the Immune Response

Jeffrey SuhalimDr. Jiayu Liao and Dr. V. G. J. Rodgers

BRITE

Introduction

Foreign Substance (i.e. Virus)

IFN

AV

• 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

MATLAB

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”