AbstractPurposeThe complex interactions between genetic machinery of HIV-1 and host immune cells mediate dynamic adaptive responses leading to Autoimmune Deficiency Syndrome. These interactions are captured as Biological Regulatory Network (BRN) which acts to maintain the viability of host cell machinery through feedback control mechanism which is a characteristic of complex adaptive systems. In this study, the BRN of immune response against HIV-1 infection is modeled to investigate the role of NF-κB and TNF-α in disease transmission using qualitative (discrete) and hybrid modeling formalisms.MethodsQualitative and Hybrid modeling approaches are used to model the BRN for the dynamic analysis. The qualitative model is based on the logical parameters while the hybrid model is based on the time delay parameters.ResultsThe qualitative model gives useful insights about the physiological condition observed as the homeostasis of all the entities of the BRN as well as pathophysiological behaviors representing high expression levels of NF-κB, TNF-α and HIV. Since the qualitative model is time abstracted, so a hybrid model is developed to analyze the behavior of the BRN by associating activation and inhibition time delays with each entity. HyTech tool synthesizes time delay constraints for the existence of homeostasis. ConclusionHybrid model reveals various viability constraints that characterize the conditional existence of cyclic states (homeostasis). The resultant relations suggest larger cycle period of HIV-1 than the cycle periods of the other two entities (NF-κB and TNF-α) to maintain a homeostatic expressions of these entities.
On the modeling and analysis of the biological regulatory network of NF-$${\kappa }$$κB activation in HIV-1 infection
Zurah Bibi,Jamil Ahmad,Amjad Ali,Amnah Siddiqa,S. Shahzad,Samar H. K. Tareen,H. A. Janjua,Shah Khusro
Published 2016 in Complex Adaptive Systems Modeling
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- Publication year
2016
- Venue
Complex Adaptive Systems Modeling
- Publication date
2016-01-08
- Fields of study
Biology, Medicine, Computer Science
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