Ten Challenges for Systems Medicine

Systems Medicine (Auffray et al., 2009, 2010) emphasizes the role of systems biology in medical/clinical applications. With the advent of new technologies, the “omics” explosion (i.e., next generation sequencing) and the induced changes from data-poor to data-rich applications (for instance related to high-content imaging, physiology, and structural biology) have established the necessity of a systems approach (Noble, 2008) not to be caught in the data deluge. The accumulation and variety of high-throughput evidences and studies have generated hypothesis-driven models and validations at a previously inconceivable scale. Correspondingly, the assembly of models tackling all the implied complexities suggests challenges for which no standard (e.g., specified according to assumptions) inference approaches currently exist. In response to such problems and uncertainties, both data-driven intensive applications and model-free or agnostic (non-parametric) inferences are re-defining bioinformatics/statistics pipelines and network model architectures. Computational tools will be designed to satisfy criteria of: (1) Efficiency in processing, mining, and analyzing sequencing data; in particular, parallel architectures and high performance computing will be necessary to address the current data volumes and complexities; (2) Flexibility in synergizing the “omics” fields with clinical, biological, and environmental information whose integrative nature will require network-centric knowledge representation systems (Pawson and Linding, 2008; Zanzoni et al., 2008; Barabasi et al., 2011) will be very important; (3) Accuracy in data post-processing by exploiting model checking through robust feature selection and accurate output annotation, including clinical samples and patients’ follow up information. The tasks required to satisfy such criteria are highly specific and technical, but show interrelationships that lead to systems approaches. From one hand, the components that need to be considered in such systems have heterogeneous features due to sample diversity acquired at data-poor (patients) and data-rich (cellular, imaging) resolutions, and require normalization to exploit complementary evidences (experimental, clinical, epidemiological, computational, simulation-based) and measurements (quantitative, environmental, perturbation-based). From another hand, a consensus concerning data collection and annotation is needed for comparative evaluations and assessment of data consistencies among studies and experiments. Systems medicine represents a mosaic of distinct and interconnected micro-systems allowing to infer the macro-systems dynamics and produce elements of synthesis such as signatures (Hood and Friend, 2011; Sung et al., 2012) and profiles originated by a variety of information sources and consequently characterized. For instance, disease networks have been discussed by Barabasi et al. (2011), while pathway analysis beyond “canonical pathways” (Califano et al., 2012) and conceived for monitoring and assessing the mechanisms of action of drugs by the identification of targets and biomarkers, could involve multiple differential conditions to evaluate responses at system’s level or at global network scale (protein–protein interaction, gene regulatory, microRNA-target etc.), including deviation from equilibrium and/or stability. In response to crucial bottlenecks in Systems Medicine, our contribution aims to point out 10 challenges that are going to characterize the field, and for which Figure ​Figure11 provides an ensemble view. Figure 1 Links between Challenges. A modularization of the Bioinformatics Infrastructure embedding integratively and significantly validated inferences will lead to a Systems Medicine Paradigm Shift.