Table 2 Means of Systems Medicine in the literature

combines systems biology and pathophysiological approaches to translational research, integrating various bio-medical tools and using the power of computational and mathematical modelling

using molecular and dynamic parameters

inferred models

incorporating genomic information (genomic medicine) along with appropriate biological and computational tools for data interpretation

leverages systems biology for clinical application

information and communication technologies, and the conceptual framework of complex system studies

shedding light in multiple research scenarios, ultimately leading to the practical result of uncovering novel dynamic interaction networks that are critical

clinical and molecular know-how

scrutinizing overall molecular network interactions, rather than individual molecules

an implementation of Systems Biology in the Medical disciplines

implies the establishment of a connection between a molecular-centered to a patient-centered world, through an organ-centered intermediate layer. This mapping requires the extensive use of computational tools such as statistical, mathematical and bioinformatical techniques

through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial

is deeply related to complex networks: it involves a systemic view of the organism where the various building elements are considered in their interplay

with all of a patient’s medical data being computationally integrated and accessible to functionally interpret omics and big data incorporating a range of personalized data including genomic, epigenetic, environmental, lifestyle and medical history

To achieve these goals, precision medicine aims to develop computational models that integrate data and knowledge from both clinic and basic research to gain a mechanistic understanding of disease

Systems medicine analyzes the dynamic data cloud that surrounds each patient and uses this

rely on data as the primary modeling material, not knowledge

which purports to design multiscale mathematical disease models

is concerned with the network of molecular interactions that define biological processes. Additionally, disease states are viewed as a perturbation of these molecular networks

amalgamates systems biology techniques with medical treatment decision-making, where information from many biological measurements is combined and analysed for complex patterns of change.

Systems medicine is not simply the application of systems biology in medicine; rather, it is the logical next step and necessary extension of systems biology with more emphasis on clinically relevant applications. Building on the success of systems biology, systems medicine is defined as an emerging discipline that integrates comprehensively computational modeling, ‘omics data, clinical data, and environmental factors

utilizes all types of nonlinear information

where traditional model-driven experiments are informed by data-driven models in an iterative manner

molecular fingerprints resulting from biological networks perturbed by the disease will be used

the use of network-based models of biological process combined with the information on the patient, mainly of molecular origin

integrates physiopathology, network biology and molecular variations

through stratification of patients and diseases

data are collected from all the components of the immune system, analyzed and integrated

embraces this paradigm [Systems Biology]

a) taking advantage and emphasizing information and tools made available by the greatest possible spectrum of scientific disciplines

b) standardization, information, integration, monitoring and personalization

application of systems biology to medical research and practice

analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels

combining omics with bioinformatics, as well as functional and clinical studies

representing all the available knowledge on the disease of interest with a mathematical symbolism allowing generation and testing of hypotheses through computational simulation and experimental validation

integrate a variety of data at all relevant levels of cellular organisation with clinical and patientreported disease markers, using the power of computational and mathematical modelling

applies the perspective of SB [Systems Biology] to the study of disease mechanisms

a) network-based approach to analysis of high-throughput and routine clinical data to predict disease mechanisms to diagnoses and treatments

b) interdisciplinary approach that integrates research data and clinical practice and others view it as fusion of systems biology and bioinformatics with a focus on disease and the clinic

c) high-precision, mathematical model of variables from different genomic layers that relate to clinical outcomes such as treatment response

a) interdisciplinary approach that integrates data from basic research and clinical practice

b) close integration of data generation with mathematical modeling

c) development of concepts, methods and tools that support the integration of organizational levels

a) interdisciplinary effort

b) applies the tools and concepts from systems biology and addresses complexity in two key ways. First, systems medicine uses molecular diagnostics to stratify patients and diseases

c) applying a network-level view of disease

d) identifying important functional and regulatory modules within these networks

e) by analyzing and targeting hubs—the most highly interconnected nodes—within these regulatory networks, and enzymatic activity in metabolic networks

a) iterative and reciprocal feedback between data-driven computational and mathematical models as well as model-driven translational and clinical investigations

b) specific but large and static data sets acquired across multiple modalities are used

based on theoretical methods and high-throughput “omics” data

a) statistical and computational analysis of metabolic, phenotypic, and physiological data

b) application of computational and statistical approaches to support clinical decisions

a) tools for data integration

b) sophisticated measurement of molecular moieties

united genomics and genetics through family genomics

different specific complex factors are important in disease management and that these factors need to be incorporated in some meaningful way

standardization of data

integrating experiments in iterative cycles with computational modeling, simulation, and theory

a) identifying all the components of a system, establishing their interactions and assessing their dynamics – both temporal and spatial – as related to their functions

b) utilizes all types of biological information – DNA, RNA, protein, metabolites, small molecules, interactions, cells, organs, individuals, social networks and external environmental signals – integrating them

the fully implementation of which requires marrying basic and clinical researches through advanced systems thinking and the employment of high-throughput technologies in genomics, proteomics, nanofluidics, single-cell analysis, and computation strategies in a highly-orchestrated discipline

using the power of computational and mathematical modeling

using knowledge of their molecular components must exploit more limited data sets, arising from multiple open-ended investigations upon highly heterogeneous patient populations in conjunction with vast amounts of poorly correlated published results. Hence, systems medicine must proceed on the basis of existing, highly heterogeneous data and not on the basis of homogeneous datasets arising from specifically targeted investigations.

companion molecular diagnostics for personalized therapy the mounting influx of global quantitative data from both wellness and diseases,

which requires new strategies, both scientific and organizational

by determining the links between genotypes, phenotypes and environmental factors (e.g. diet and exposure to toxins)

by analysing its different constituents

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

Systems medicine represents a mosaic of distinct and interconnected micro-systems

originated by a variety of information sources and consequently characterized.

leverages complex computational tools and high-dimensional data

the effective use of petabytes of data, which necessitates the development of both new types of tools and a new type of physician—one with a grasp of modern computational sciences, “omics” technologies, and a systems approach to the practice of medicine systems biology

This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches

Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes.

based on a robust and extensive knowledge management infrastructure that contains individual patient information.

It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease

It uses the power of computational and mathematical modeling].

takes a holistic view of health and disease through integrated care using multidisciplinary and teamwork approaches centered in primary and community

Understanding the unique events in an individual’s life as influencing the development of illness and disease appears to be the key to what is emerging under the names of ‘personalized medicine’ and ‘systems medicine’.

Personalized medicine presupposes systems biology and complexity sciences, […]

Systems biology and medicine focuses on deciphering mechanisms at multiple levels, reconstructing networks in cells, tissues and organs, measuring and predicting phenotypes, building quantitative models that describe and simulate normal and pathological physiological functions, and then testing the validity of these models and predictions experimentally.

exploration of tumor microenvironment2,15 and of a more global approach to link individual tumors with their multiple host variables,including heritable causal mutations, environmental exposures and lifestyle,

the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence

applicable methodology tool, systems biology.

Systems medicine, the translational science counterpart to basic science’s systems biology, is the interface at which these tools may be constructed

[…] systems medicine is the coupling of systems science with medical treatment decision-making.

systems medicine approaches focus on the dynamic interactions among multiple factors that affect complex diseases, such as diabetes, coronary artery disease and cancers1. The increasing availability of powerful high-throughput technologies, computational tools and integrated knowledge bases, has made it possible to establish new links between genes, biologic functions and human diseases, providing the hallmarks of systems medicine, including signatures of pathology biology, and links to clinical research and drug discovery.

Holistic systems biology methodologies

through the construction of integrated biomolecular networks.

The knowledge of network dynamics through in vitro experimental perturbation and modeling allows us to determine the state of the networks, to identify molecular correlates, and. The transformation in biology through systems biology

The central premise of systems medicine is that clinically detectable molecular fingerprints resulting from disease-perturbed biological networks will be used to detect and stratify various pathological conditions. Disease associated molecular fingerprints will eventually be used to group individuals into sub-populations based on variations in genetic makeup of the population that affects disease progression. The key to this revolution lies in harnessing the power of network models of core biological processes learned through systems biology methods, combined with vast amounts of diverse molecular information generated from patient samples.

depends on our ability to: 1) precisely infer network state from the results of assessing the levels of a panel of informative, diagnostic biomarkers in the blood, and 2) specifically manipulate a network to avoid or revert the pathology.

the application of our understanding of the integrated dynamical responses of various molecular networks that determine the critical states of the body.

the therapeutic component of systems medicine then, in which we infer network states from biomarker measurements

the application of systems biology

incorporates the complex biochemical, physiological, and environmental interactions that sustain living organisms.

incorporates interactions between all components of health and disease.

A key feature of systems medicine is that existing networks, through dynamic (time-dependent) interactions, manifest “emergent properties” that define the whole and that these properties are not simply the sum of the features of its component parts.

by integrating all levels of quantitative functional, structural, and morphological information into a coherent model.

It investigates the physiological network of diseases from gene to organ systems

via an integrative approach that includes clinical examinations, experimental modeling and in-silico simulation.

by integrating all levels of quantitative functional, structural and morphological information into a coherent model.

Systems medicine is an emerging concept that acknowledges the complexity of a multitude of non-linear interactions among molecular and physiological variables.

Under this new paradigm, rather than a collection of symptoms, diseases are seen as the product of deviations from a robust steady state compatible with life.

the incorporation of mathematics and physics to the more classical arsenal of physiology and molecular biology with which physicians are trained today.