Analyzing Heterogeneous Complexity in Complementary and Alternative Medicine Research: A Systems Biology Solution via Parsimony PhylogeneticsAbu-Asab M.a · Koithan M.b,c · Shaver J.b · Amri H.d
aLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, bCollege of Nursing, cDepartment of Family and Community Medicine, The University of Arizona, Tucson, AZ, dDepartment of Biochemistry, Cellular and Molecular Biology, School of Medicine, Georgetown University, Washington, DC, USA
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Systems biology offers cutting-edge tools for the study of complementary and alternative medicine (CAM). The advent of ‘omics’ techniques and the resulting avalanche of scientific data have introduced an unprecedented level of complexity and heterogeneous data to biomedical research, leading to the development of novel research approaches. Statistical averaging has its limitations and is unsuitable for the analysis of heterogeneity, as it masks diversity by homogenizing otherwise heterogeneous populations. Unfortunately, most researchers are unaware of alternative methods of analysis capable of accounting for individual variability. This paper describes a systems biology solution to data complexity through the application of parsimony phylogenetic analysis. Maximum parsimony (MP) provides a data-based modeling paradigm that will permit a priori stratification of the study cohort(s), better assessment of early diagnosis, prognosis, and treatment efficacy within each stratum, and a method that could be used to explore, identify and describe complex human patterning.
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