Connective molecular pathways of experimental bladder inflammation.
Inflammation is an inherent response of the organism that permits its survival despite constant environmental challenges. The process normally leads to recovery from injury and to healing. However, if targeted destruction and assisted repair are not properly phased, chronic inflammation can result in persistent tissue damage. To better understand the inflammatory process, we recently introduced a profiling methodology to identify common genes involved in bladder inflammation. The method represents a complementation to the classic quantification of inflammation and provides information regarding the early, intermediate, and late events in gene regulation. However, gene profiling fails to describe the molecular pathways and their interconnections involved in the particular inflammatory response. The present work introduces a new statistical technique for inferring functional interconnections between inflammatory pathways underlying classic models of bladder inflammation and permits the modeling of the inflammatory network. This new statistical method is based on variants of cluster analysis, Boolean networking, differential equations, Bayesian networking, and partial correlation. By applying partial correlation analysis, we developed mosaics of gene expression that permitted a global visualization of common and unique pathways elicited by different stimuli. The significance of these processes was tested from both biological and statistical viewpoints. We propose that connective mosaic may represent the necessary simplification step to visualize cDNA array results.
Dozmorov
Igor
I
Oklahoma Medical Research Foundation, Arthritis and Immunology Research Program, Microarray Core Facility, The University Oklahoma Health Sciences Center, Oklahoma City, Oklahoma 73104, USA. .
Saban
Marcia R
MR
Knowlton
Nicholas
N
Centola
Michael
M
Saban
Ricardo
R
D000818:Animals; D016000:Cluster Analysis; D003556:Cystitis / Q000139:chemically induced / Q000235:genetics*; D004140:Dinitrophenols / Q000276:immunology / Q000494:pharmacology; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D005786:Gene Expression Regulation / Q000187:drug effects / Q000235:genetics; D014644:Genetic Variation; D008070:Lipopolysaccharides / Q000009:adverse effects / Q000494:pharmacology; D051379:Mice; D008810:Mice, Inbred C57BL; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data; D010047:Ovalbumin / Q000276:immunology / Q000494:pharmacology; D012709:Serum Albumin / Q000276:immunology / Q000494:pharmacology; D013373:Substance P / Q000009:adverse effects / Q000494:pharmacology
D004140:Dinitrophenols; D008070:Lipopolysaccharides; D012709:Serum Albumin; C045521:dinitrophenol-ovalbumin; D013373:Substance P; D010047:Ovalbumin