Biomarkers in the age of omics: time for a systems biology approach

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics

Background

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.

Methodology

The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.

Conclusions

We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Methodological implications of nonlinear dynamical systems models for whole systems of complementary and alternative medicine

This paper focuses on the worldview hypotheses and research design approaches from nonlinear dynamical complex systems (NDS) science that can inform future studies of whole systems of complementary and alternative medicine (WS-CAM), e.g., Ayurveda, traditional Chinese medicine, and homeopathy. The worldview hypotheses that underlie NDS and WS-CAM (contextual, organismic, interactive-integrative - Pepper, 1942) overlap with each other, but differ fundamentally from those of biomedicine (formistic, mechanistic). Differing views on the nature of causality itself lead to different types of study designs. Biomedical efficacy studies assume a simple direct mechanistic cause-effect relationship between a specific intervention and a specific bodily outcome, an assumption less relevant to WS-CAM outcomes. WS-CAM practitioners do not necessarily treat a symptom directly. Rather, they intervene to modulate an intrinsic central imbalance of the person as a system and to create a more favorable environmental context for the emergence of health, e.g., with dietary changes compatible with the constitutional type. The rebalancing of the system thereby fosters the emergence of indirect, diffuse, complex effects throughout the person and the person's interactions with his/her environment. NDS theory-driven study designs thus have the potential for greater external and model validity than biomedically driven efficacy studies (e.g., clinical trials) for evaluating the indirect effects of WS-CAM practices. Potential applications of NDS analytic techniques to WS-CAM include characterizing different constitutional types and documenting the evolution and dynamics of whole-person healing and well-being over time. Furthermore, NDS provides models and methods for examining interactions across organizational scales, from genomic/proteomic/metabolomic networks to individuals and social groups.

Identifying pathological biomarkers: histochemistry still ranks high in the omics era

In recent years, omic analyses have been proposed as possible approaches to diagnosis, in particular for tumours, as they should be able to provide quantitative tools to detect and measure abnormalities in gene and protein expression, through the evaluation of transcription and translation products in the abnormal vs normal tissues. Unfortunately, this approach proved to be much less powerful than expected, due to both intrinsic technical limits and the nature itself of the pathological tissues to be investigated, the heterogeneity deriving from polyclonality and tissue phenotype variability between patients being a major limiting factor in the search for unique omic biomarkers. Especially in the last few years, the application of refined techniques for investigating gene expression in situ has greatly increased the diagnostic/prognostic potential of histochemistry, while the progress in light microscopy technology and in the methods for imaging molecules in vivo have provided valuable tools for elucidating the molecular events and the basic mechanisms leading to a pathological condition. Histochemical techniques thus remain irreplaceable in pathologist's armamentarium, and it may be expected that even in the future histochemistry will keep a leading position among the methodological approaches for clinical pathology.