Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

Abstract

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

The new holism: P4 systems medicine and the medicalization of health and life itself

“…These measurements in turn have two aspects that will reappear in what we call holistic medicalization : (a) Spatially, the measurements are multi - dimensional in that they pertain to all levels of biological organization. (b) Temporally, the technologies enable repeated or continual measurements through time that represent the dynamism of health and disease in a way that is new to biomedicine (Emmert-Streib and Dehmer 2013 ). The envisioned end result is a dynamic data cloud that reflects the whole life process in all four dimensions, consisting of ‘ billions of data points ’ (Bousquet et al 2011 ).…”

“…Systems biology is technologically strongly focused on molecular parts (De Backer et al 2010 ). As a corollary the wholes that P4SM models are also mostly molecular wholes (Mardinoglu and Nielsen 2012 ; Emmert-Streib and Dehmer 2013 ; Ayers and Day 2015 ; Wang et al 2015 ). As illustrated by talk of ‘ molecular level (…) processes that define and drive physiology ’ and provide ‘ deep understanding of causality ’ (Flores et al 2013 ), the molecular is central to its philosophy of causation and epistemology.…”

Building SuperModels: emerging patient avatars for use in precision and systems medicine

“…Precision medicine tailors prevention, diagnosis, therapeutics, and prognosis for each patient (Garay and Gray, 2012 ; Highnam and Mittelman, 2012 ; Mirnezami et al, 2012 ). Related to precision medicine is systems medicine (Auffray et al, 2009 ; Capobianco, 2012 ; Emmert-Streib and Dehmer, 2013 ; Wolkenhauer, 2013 ), which leverages systems biology (Noble, 2008 ) for clinical application, with resulting data termed “systems medicine data” (Brown et al, 2015b ). Systems biology studies the behavior of organisms or cells as whole systems, and uses various advances in biotechnology, including genomics, transcriptomics, proteomics, metabolomics, methylomics, microbiomics, and elucidation of cellular interaction networks by network biology (Figure 1 , top left panel labeled X).…”

Returning findings within longitudinal cohort studies: the 1958 birth cohort as an exemplar

“…As well, while markers, such as blood pressure levels will be different at various times, a person’s genotype is static and predictive throughout life. But even though someone’s genes may not change, there may be new signals, environmental influences or epigenetic conditions that will influence how a mutation will manifest itself phenotypically [ 6 ]. Therefore, even though a mutation is present, a person may not suffer from the condition or suffer in the same way or at the same point in life as others.…”