Modeling of cell signaling pathways in macrophages by semantic networks.
BACKGROUND
Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.


RESULTS
We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages.


CONCLUSIONS
We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.
Hsing
Michael
M
CIHR/MSFHR Strategic Training Program in Bioinformatics, Genetics Graduate Program, Faculty of Graduate Studies, University of British Columbia, Vancouver, British Columbia, V5Z 3J5, Canada. mhsing@interchange.ubc.ca <mhsing@interchange.ubc.ca>
Bellenson
Joel L
JL
Shankey
Conor
C
Cherkasov
Artem
A
D003198:Computer Simulation; D006801:Humans; D042541:Intracellular Space / Q000145:classification / Q000382:microbiology; D008264:Macrophages / Q000145:classification* / Q000276:immunology / Q000382:microbiology; D008957:Models, Genetic; D046912:Multiprotein Complexes / Q000145:classification / Q000502:physiology; D009169:Mycobacterium tuberculosis / Q000276:immunology; D010455:Peptides / Q000145:classification / Q000502:physiology; D017434:Protein Structure, Tertiary; D015398:Signal Transduction / Q000276:immunology / Q000502:physiology; D014376:Tuberculosis / Q000517:prevention & control
D046912:Multiprotein Complexes; D010455:Peptides