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Modeling of cell signaling pathways in macrophages by semantic networks

University of British Columbia

Macrophages are essential components of human immune system that engulf and digest pathogens using the molecular mechanisms of phagocytosis and phagosome maturation. These processes are regulated by an essential enzyme - phosphoinositide-3-kinase (PI3K), a key initiator of signalling cascades in many cellular processes. Importantly, experimental studies demonstrate that some pathogenic bacteria, such as Mycobacterium tuberculosis (MTB), can interfere with PI3K pathways in order to survive within host macrophages. Based on the diverse roles of PI3Ks, it is reasonable to hypothesize that MTB effects upon PI3K signaling could impact macrophages in numerous ways, more than what are currently studied. It is anticipated that greater understanding of PBK signaling mechanisms in macrophages and bacterial interference could provide insights for developing effective strategies against MTB. The complexity of POK pathways makes the analysis of MTB-macrophage interactions a challenging task. Although a vast amount of knowledge on the pathways has been accumulated in literature and databases, the information is encoded in static diagrams that are difficult to study. While it is necessary to analyze complex systems computationally, the tools for modeling pathways are inadequate. To address current limitation on pathway manipulation, we applied an artificial intelligence method called Semantic Networks (SN) to model MTB interference with PI3K signalling pathways in macrophages. The advantage of SN is in its capacity to represent abstract concepts in machine friendly formats termed "semantic agents" and "relationships". In SN, the behaviour of agents is not fixed, but instead emerges from their relationships. This characteristic makes SN well suited for modeling biological systems. Using the SN methods, a model has been created to describe PI3K participation in macrophage signaling. The model encompassed a large amount of information extracted from scientific literature and pertained such complex micro-events as formation of protein complexes, chemical modifications of proteins, allosteric regulation, and changes in intracellular localization by the agents. The data integration in the SN-environment allowed us to reconstruct the molecular mechanisms of macrophage pathogenic invasion, and the model predicted previously unobserved macrophage responses. The results will be used to guide and interpret upcoming gene and protein expression studies.

Text

2009-12-11

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http://hdl.handle.net/2429/16540

Genetics

University of British Columbia

UBCV

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