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Analysis and verification of the HMGB1 signaling pathway.

Gong H, Zuliani P, Komuravelli A, Faeder JR, Clarke EM - BMC Bioinformatics (2010)

Bottom Line: Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic.Discrete, stochastic simulations show that p53 and MDM2 oscillations continue even after 10 hours, as observed by experiments.This property is not exhibited by the deterministic ODE simulation, for the chosen parameters.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu

ABSTRACT

Background: Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic.

Results: We have developed a rule-based model of crosstalk between the HMGB1 signaling pathway and other key cancer signaling pathways. The model has been simulated using both ordinary differential equations (ODEs) and discrete stochastic simulation. We have applied an automated verification technique, Statistical Model Checking, to validate interesting temporal properties of our model.

Conclusions: Our simulations show that, if HMGB1 is overexpressed, then the oncoproteins CyclinD/E, which regulate cell proliferation, are overexpressed, while tumor suppressor proteins that regulate cell apoptosis (programmed cell death), such as p53, are repressed. Discrete, stochastic simulations show that p53 and MDM2 oscillations continue even after 10 hours, as observed by experiments. This property is not exhibited by the deterministic ODE simulation, for the chosen parameters. Moreover, the models also predict that mutations of RAS, ARF and P21 in the context of HMGB1 signaling can influence the cancer cell's fate - apoptosis or survival - through the crosstalk of different pathways.

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Related in: MedlinePlus

Statistical Model Checking Algorithm. The algorithm for Statistical Model Checking is based on Bayesian Hypothesis Testing.
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Figure 2: Statistical Model Checking Algorithm. The algorithm for Statistical Model Checking is based on Bayesian Hypothesis Testing.

Mentions: Therefore, B can be interpreted as a measure of evidence (given by the data d) in favor of H0. Now, fix a threshold T > 1. The algorithm iteratively draws independent and identically distributed (iid) sample traces in the form of BioNetGen stochastic simulations, and checks whether they satisfy ϕ (Note that BioNetGen ensures by construction that each simulation, or trace, is actually iid.) After each trace, the algorithm computes the Bayes Factor B to check if it has obtained conclusive evidence. The algorithm accepts H0 if B >T, and rejects H0 (accepting H1) if . Otherwise , it continues drawing iid samples. The statistical Model Checking algorithm is shown in Figure 2.


Analysis and verification of the HMGB1 signaling pathway.

Gong H, Zuliani P, Komuravelli A, Faeder JR, Clarke EM - BMC Bioinformatics (2010)

Statistical Model Checking Algorithm. The algorithm for Statistical Model Checking is based on Bayesian Hypothesis Testing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2957678&req=5

Figure 2: Statistical Model Checking Algorithm. The algorithm for Statistical Model Checking is based on Bayesian Hypothesis Testing.
Mentions: Therefore, B can be interpreted as a measure of evidence (given by the data d) in favor of H0. Now, fix a threshold T > 1. The algorithm iteratively draws independent and identically distributed (iid) sample traces in the form of BioNetGen stochastic simulations, and checks whether they satisfy ϕ (Note that BioNetGen ensures by construction that each simulation, or trace, is actually iid.) After each trace, the algorithm computes the Bayes Factor B to check if it has obtained conclusive evidence. The algorithm accepts H0 if B >T, and rejects H0 (accepting H1) if . Otherwise , it continues drawing iid samples. The statistical Model Checking algorithm is shown in Figure 2.

Bottom Line: Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic.Discrete, stochastic simulations show that p53 and MDM2 oscillations continue even after 10 hours, as observed by experiments.This property is not exhibited by the deterministic ODE simulation, for the chosen parameters.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu

ABSTRACT

Background: Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic.

Results: We have developed a rule-based model of crosstalk between the HMGB1 signaling pathway and other key cancer signaling pathways. The model has been simulated using both ordinary differential equations (ODEs) and discrete stochastic simulation. We have applied an automated verification technique, Statistical Model Checking, to validate interesting temporal properties of our model.

Conclusions: Our simulations show that, if HMGB1 is overexpressed, then the oncoproteins CyclinD/E, which regulate cell proliferation, are overexpressed, while tumor suppressor proteins that regulate cell apoptosis (programmed cell death), such as p53, are repressed. Discrete, stochastic simulations show that p53 and MDM2 oscillations continue even after 10 hours, as observed by experiments. This property is not exhibited by the deterministic ODE simulation, for the chosen parameters. Moreover, the models also predict that mutations of RAS, ARF and P21 in the context of HMGB1 signaling can influence the cancer cell's fate - apoptosis or survival - through the crosstalk of different pathways.

Show MeSH
Related in: MedlinePlus