Ensemble View Research Articles

Therapeutic phosphorodiamidate morpholino oligonucleotides: Physical properties, solution structures, and folding thermodynamics

Elucidating the structure-function relationships for therapeutic RNA mimicking phosphorodiamidate morpholino oligonucleotides (PMOs) is challenging due to the lack of information about their structures. While PMOs have been approved by the US Food and Drug Administration for treatment of Duchenne muscular dystrophy, no structural information on these unique, charge-neutral, and stable molecules is available. We performed circular dichroism and solution viscosity measurements combined with molecular dynamics simulations and machine learning to resolve solution structures of 22-mer, 25-mer, and 30-mer length PMOs. The PMO conformational dynamics are defined by the competition between non-polar nucleobases and uncharged phosphorodiamidate groups for shielding from solvent exposure. PMO molecules form non-canonical, partially helical, stable folded structures with a small 1.4- to 1.7-nm radius of gyration, low count of three to six base pairs and six to nine base stacks, characterized by -34 to -51kcal/mol free energy, -57 to -103kcal/mol enthalpy, and -23 to -53kcal/mol entropy for folding. The 4.5- to 6.2-cm3/g intrinsic viscosity and Huggins constant of 4.5-9.9 are indicative of extended and aggregating systems. The results obtained highlight the importance of the conformational ensemble view of PMO solution structures, thermodynamic stability of their non-canonical structures, and concentration-dependent viscosity properties. These principles form a paradigm to understand the structure-properties-function relationship for therapeutic PMOs to advance the design of new RNA-mimic-based drugs.

Dynamics and mechanistic underpinnings to pharmacology of class A GPCRs: an NMR perspective.

One-third of current pharmaceuticals target G protein-coupled receptors (GPCRs), the largest receptor superfamily in humans and mediators of diverse physiological processes. This review summarizes the recent progress in GPCR structural dynamics, focusing on class A receptors and insights derived from nuclear magnetic resonance (NMR) and other spectroscopic techniques. We describe the structural aspects of GPCR activation and the various pharmacological models that capture aspects of receptor signaling behavior. Spectroscopic studies revealed that receptors and their signaling complexes are dynamic allosteric systems that sample multiple functional states under basal conditions. The distribution of states within the conformational ensemble and the kinetics of transitions between states are regulated through the binding of ligands, allosteric modulators, and the membrane environment. This ensemble view of GPCRs provides a mechanistic framework for understanding many of the pharmacological phenomena associated with receptor signaling, such as basal activity, efficacy, and functional bias.

Climate change in mechanical systems: the snapshot view of parallel dynamical evolutions

We argue that typical mechanical systems subjected to a monotonous parameter drift whose timescale is comparable to that of the internal dynamics can be considered to undergo their own climate change. Because of their chaotic dynamics, there are many permitted states at any instant, and their time dependence can be followed—in analogy with the real climate—by monitoring parallel dynamical evolutions originating from different initial conditions. To this end an ensemble view is needed, enabling one to compute ensemble averages characterizing the instantaneous state of the system. We illustrate this on the examples of (i) driven dissipative and (ii) Hamiltonian systems and of (iii) non-driven dissipative ones. We show that in order to find the most transparent view, attention should be paid to the choice of the initial ensemble. While the choice of this ensemble is arbitrary in the case of driven dissipative systems (i), in the Hamiltonian case (ii) either KAM tori or chaotic seas should be taken, and in the third class (iii) the best choice is the KAM tori of the dissipation-free limit. In all cases, the time evolution of the chosen ensemble on snapshots illustrates nicely the geometrical changes occurring in the phase space, including the strengthening, weakening or disappearance of chaos. Furthermore, we show that a Smale horseshoe (a chaotic saddle) that is changing in time is present in all cases. Its disappearance is a geometrical sign of the vanishing of chaos. The so-called ensemble-averaged pairwise distance is found to provide an easily accessible quantitative measure for the strength of chaos in the ensemble. Its slope can be considered as an instantaneous Lyapunov exponent whose zero value signals the vanishing of chaos. Paradigmatic low-dimensional bistable systems are used as illustrative examples whose driving in (i, ii) is chosen to decay in time in order to maintain an analogy with case (iii) where the total energy decreases all the time.

Evolutionary Shallowing Deep Neural Networks at Block Levels.

Neural networks have been demonstrated to be trainable even with hundreds of layers, which exhibit remarkable improvement on expressive power and provide significant performance gains in a variety of tasks. However, the prohibitive computational cost has become a severe challenge for deploying them on resource-constrained platforms. Meanwhile, widely adopted deep neural network architectures, for example, ResNets or DenseNets, are manually crafted on benchmark datasets, which hamper their generalization ability to other domains. To cope with these issues, we propose an evolutionary algorithm-based method for shallowing deep neural networks (DNNs) at block levels, which is termed as ESNB. Different from existing studies, ESNB utilizes the ensemble view of block-wise DNNs and employs the multiobjective optimization paradigm to reduce the number of blocks while avoiding performance degradation. It automatically discovers shallower network architectures by pruning less informative blocks, and employs knowledge distillation to recover the performance. Moreover, a novel prior knowledge incorporation strategy is proposed to improve the exploration ability of the evolutionary search process, and a correctness-aware knowledge distillation strategy is designed for better knowledge transferring. Experimental results show that the proposed method can effectively accelerate the inference of DNNs while achieving superior performance when compared with the state-of-the-art competing methods.

Sixty years of research on technology and human resource management: Looking back and looking forward

AbstractTechnology has changed the way we work and how companies manage their employees. This article reviews 60 years of research on the relationship between technology and human resource management, as represented in Human Resource Management. Based on 154 articles, we identify recurring and evolving patterns of research on technology across three time periods (separated by the advent of the personal computer in 1977 and by the popularization of consumer internet services in 1997), three perspectives on technology (tool, proxy, and ensemble view of technology), and three thematic streams (the impact of technology on jobs and organizations, the utilization of technology in HR activities, and the management of technology workers). Drawing on patterns of research that emerged in the past, we provide suggestions for future HR research on newly arriving technology.

Investigating ENSO and its teleconnections under climate change in an ensemble view – a new perspective

Abstract. The changes in the El Niño–Southern Oscillation (ENSO) phenomenon and its precipitation-related teleconnections over the globe under climate change are investigated in the Community Earth System Model Large Ensemble from 1950 to 2100. For the investigation, a recently developed ensemble-based method, the snapshot empirical orthogonal function (SEOF) analysis, is used. The instantaneous ENSO pattern is defined as the leading mode of the SEOF analysis carried out at a given time instant over the ensemble. The corresponding principal components (PC1s) characterize the ENSO phases. By considering sea surface temperature (SST) regression maps, we find that the largest changes in the typical amplitude of SST fluctuations occur in the June–July–August–September (JJAS) season, in the Niño3–Niño3.4 (5∘ N–5∘ S, 170–90∘ W; NOAA Climate Prediction Center) region, and the western part of the Pacific Ocean; however, the increase is also considerable along the Equator in December–January–February (DJF). The Niño3 amplitude also shows an increase of about 20 % and 10 % in JJAS and DJF, respectively. The strength of the precipitation-related teleconnections of the ENSO is found to be nonstationary, as well. For example, the anticorrelation with precipitation in Australia in JJAS and the positive correlation in central and northern Africa in DJF are predicted to be more pronounced by the end of the 21th century. Half-year-lagged correlations, aiming to predict precipitation conditions from ENSO phases, are also studied. The Australian and Indonesian precipitation and that of the eastern part of Africa in both JJAS and DJF seem to be well predictable based on the ENSO phase, while the southern Indian precipitation relates to the half-year previous ENSO phase only in DJF. The strength of these connections increases, especially from the African region to the Arabian Peninsula.

Protein ensembles link genotype to phenotype.

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype–phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

A Simulation and Visualization Environment for Spatiotemporal Disaster Risk Assessments of Network Infrastructures

Emerging methodologies for risk assessments of civil infrastructure networks require the coupling of several spatiotemporal models that need to be executed multiple times with varying parametrizations to account for model uncertainty and to investigate “what-if” scenarios. These requirements led to the development of a software environment to support the simulation process and the visual analysis of its results. The simulation engine component of the environment makes it possible to define, couple, and execute models. An embedded infrastructure model facilitates the development of functionality to estimate and aggregate capacity measures of single objects affected by multiple hazards. The simulation manager component can be used to execute multiple instances of the simulation engine conveniently with varying parametrizations. The included visualization tool provides two complementary views. The ensemble view can be used to analyze the data at a highly aggregated level with information visualization techniques and the simulation view can be used to investigate simulations in greater detail via an interactive map window and a state dependency graph. The software environment is used in a risk assessment for the region of Chur, Switzerland, which comprises the simulation of multiple natural hazard scenarios that lead to impaired transport infrastructure capacities and thus to disrupted traffic flows.

What’s in an Average? An Ensemble View of Phosphorylation Effects

The number of examples of functional post-translational modulation of disordered proteins rapidly grows. In a recent issue of Structure, Jie etal. (2017) show that phosphorylation of dynein intermediate chain alters partner binding but retains its dynamic, disorder character. An ensemble model offers an organizingframework to relate function, conformation, and phosphorylation in disordered and ordered proteins alike.

Ensemble View of RNAs and Proteins: Loops, Knots, Territories, and Evolution

Thereisanoldjokeaboutalazydoctorwho, instead of looking at each patientindividually, requested informationabout average body temperature of allpatients in the hospital. The joke pre-tends to be funny because an ensembleview of patients is absurd. But in someother cases, including in the life sci-ences, an ensemble view is not onlynot absurd, but the right instrument,and Liu and Hyeon (1) in this issueof the Biophysical Journal offer anice example: not only do they lookat ensembles, they compare twodifferent ones—ensemble of proteinsand ensemble of RNAs. Their findingsare remarkably thought-provoking.Their main technical tool is thestudyofcontactprobability—theprob-ability that two monomers of a specificpolymer chain, separated by a contourdistance of s monomers, are found incontact in three dimensions (Fig. 1).In polymer physics, a similarlydefined quantity P(s) is widely usedto distinguish between differentphases of a polymer chain. In partic-ular, for various fractal conformations,P(s)decaysas apower lawPðsÞ s

A multi-model ensemble view of winter heat flux dynamics and the dipole mode in the Mediterranean Sea

Changes in surface heat fluxes affect several climate processes controlling the Mediterranean climate. These include the winter formation of deep waters, which is the primary driver of the Mediterranean Sea overturning circulation. Previous studies that characterize the spatial and temporal variability of surface heat flux anomalies over the basin reveal the existence of two statistically dominant patterns of variability: a monopole of uniform sign and an east–west dipole of opposite signs. In this work, we use the 12 regional climate model ensemble from the EU-FP6 ENSEMBLES project to diagnose the large-scale atmospheric processes that control the variability of heat fluxes over the Mediterranean Sea from interannual to decadal timescales (here defined as timescales > 6 year). Our findings suggest that while the monopole structure captures variability in the winter-to-winter domain-average net heat flux, the dipole pattern tracks changes in the Mediterranean climate that are connected to the East Atlantic/Western Russia (EA/WR) atmospheric teleconnection pattern. Furthermore, while the monopole exhibits significant differences in the spatial structure across the multi-model ensemble, the dipole pattern is very robust and more clearly identifiable in the anomaly maps of individual years. A heat budget analysis of the dipole pattern reveals that changes in winds associated with the EA/WR pattern exert dominant control through both a direct effect on the latent heat flux (i.e., wind speed) and an indirect effect through specific humidity (e.g., wind advection). A simple reconstruction of the heat flux variability over the deep-water formation regions of the Gulf of Lion and the Aegean Sea reveals that the combination of the monopole and dipole time series explains over 90 % of the heat flux variance in these regions. Given the important role that surface heat flux anomalies play in deep-water formation and the regional climate, improving our knowledge on the dynamics controlling the leading modes of heat flux variability may enhance our predictability of the climate of the Mediterranean area.

Role of Electrostatic Interactions in Binding of Peptides and Intrinsically Disordered Proteins to Their Folded Targets: 2. The Model of Encounter Complex Involving the Double Mutant of the c-Crk N-SH3 Domain and Peptide Sos.

In the first part of this work (paper 1, Xue, Y. et al. Biochemistry 2014 , 53 , 6473 ), we have studied the complex between the 10-residue peptide Sos and N-terminal SH3 domain from adaptor protein c-Crk. In the second part (this paper), we designed the double mutant of the c-Crk N-SH3 domain, W169F/Y186L, with the intention to eliminate the interactions responsible for tight peptide-protein binding, while retaining the interactions that create the initial electrostatic encounter complex. The resulting system was characterized experimentally by measuring the backbone and side-chain (15)N relaxation rates, as well as binding shifts and (1)H(N) temperature coefficients. In addition, it was also modeled via a series of ∼5 μs molecular dynamics (MD) simulations recorded in a large water box under an Amber ff99SB*-ILDN force field. Similar to paper 1, we have found that the strength of arginine-aspartate and arginine-glutamate salt bridges is overestimated in the original force field. To address this problem we have applied the empirical force-field correction described in paper 1. Specifically, the Lennard-Jones equilibrium distance for the nitrogen-oxygen pair across Arg-to-Asp/Glu salt bridges has been increased by 3%. This modification led to MD models in good agreement with the experimental data. The emerging picture is that of a fuzzy complex, where the peptide "dances" over the surface of the protein, making transient contacts via salt-bridge interactions. Every once in a while the peptide assumes a certain more stable binding pose, assisted by a number of adventitious polar and nonpolar contacts. On the other hand, occasionally Sos flies off the protein surface; it is then guided by electrostatic steering to quickly reconnect with the protein. The dynamic interaction between Sos and the double mutant of c-Crk N-SH3 gives rise to only small binding shifts. The peptide retains a high degree of conformational mobility, although it is appreciably slowed down due to its (loose) association with the protein. Note that spin relaxation data are indispensable in determining the dynamic status of the peptide. Such data can be properly modeled only on a basis of bona fide MD simulations, as shown in our study. We anticipate that in future the field will move away from the ensemble view of protein disorder and toward more sophisticated MD models. This will require further optimization of force fields, aimed specifically at disordered systems. Efforts in this direction have been recently initiated by several research groups; the empirical salt-bridge correction proposed in our work falls in the same category. MD models obtained with the help of suitably refined force fields and guided by experimental NMR data will provide a powerful insight into an intricate world of disordered biomolecules.

Markov state models provide insights into dynamic modulation of protein function.

ConspectusProtein function is inextricably linked to protein dynamics. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational paradigms are required for observing and understanding conformational dynamics of proteins and its functional implications. In principle, molecular dynamics simulations can provide the time evolution of atomistic models of proteins, but the long time scales associated with functional dynamics make it difficult to observe rare dynamical transitions. The issue of extracting essential functional components of protein dynamics from noisy simulation data presents another set of challenges in obtaining an unbiased understanding of protein motions. Therefore, a methodology that provides a statistical framework for efficient sampling and a human-readable view of the key aspects of functional dynamics from data analysis is required. The Markov state model (MSM), which has recently become popular worldwide for studying protein dynamics, is an example of such a framework.In this Account, we review the use of Markov state models for efficient sampling of the hierarchy of time scales associated with protein dynamics, automatic identification of key conformational states, and the degrees of freedom associated with slow dynamical processes. Applications of MSMs for studying long time scale phenomena such as activation mechanisms of cellular signaling proteins has yielded novel insights into protein function. In particular, from MSMs built using large-scale simulations of GPCRs and kinases, we have shown that complex conformational changes in proteins can be described in terms of structural changes in key structural motifs or “molecular switches” within the protein, the transitions between functionally active and inactive states of proteins proceed via multiple pathways, and ligand or substrate binding modulates the flux through these pathways. Finally, MSMs also provide a theoretical toolbox for studying the effect of nonequilibrium perturbations on conformational dynamics. Considering that protein dynamics in vivo occur under nonequilibrium conditions, MSMs coupled with nonequilibrium statistical mechanics provide a way to connect cellular components to their functional environments. Nonequilibrium perturbations of protein folding MSMs reveal the presence of dynamically frozen glass-like states in their conformational landscape. These frozen states are also observed to be rich in β-sheets, which indicates their possible role in the nucleation of β-sheet rich aggregates such as those observed in amyloid-fibril formation. Finally, we describe how MSMs have been used to understand the dynamical behavior of intrinsically disordered proteins such as amyloid-β, human islet amyloid polypeptide, and p53. While certainly not a panacea for studying functional dynamics, MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions.

The Ongoing Quest for the it Artifact: Looking Back, Moving Forward

More than 10 years ago, Orlikowski and Iacono (2001) examined the conceptualization of Information Technology (IT) in Information Systems Research (ISR) articles published in the 1990s. Their main conclusion was that the majority of these articles did not properly conceptualize the IT artifact. They recommended that IS researchers start to theorize about the IT artifact and employ rich conceptualizations of IT. The Orlikowski and Iacono paper provides a strong anchor point from which to analyze the evolution of the IS discipline. In order to obtain an up-to-date image of contemporary IS research, and to assess how the IS field has evolved since the 1990s, we carried out a similar analysis on a more recent and broader set of articles, that is, the full set (N = 644) of papers published between 2006 and 2009 by six top North American (ISR, MISQ, JAIS) and European (JIT, ISJ, EJIS) journals. The statistics in our results reveal no drastic advance in terms of deeper engagement with the IT artifact; more than 39% of the articles in our set are virtually mute about the artifact, and less than 16% employ an ensemble view of IT. Moreover, we note differences among the North American and European journals. Implications of the findings for two perspectives central to the IS research legitimacy debate are discussed.

How Can a Ligand be a Positive and Negative Allosteric Effector for the Same Protein?

For several transcription factors, the same ligand can act as a positive and negative allosteric effector in a context dependent manner. The structural and molecular bases of such effects are unknown. Here it is shown that modulation of conformational fluctuations within allosteric systems can be used as a mechanism to tune protein ensembles such that a given ligand can act as both a positive and negative allosteric effector. Importantly, this mechanism can be robustly encoded in the ensemble, and does not require that the interactions between the ligand and the protein differ when it is acting either as a positive or negative effector. Instead, the effect is due to the relative probabilities of states prior to the addition of the ligand and is encoded in the thermodynamic coupling architectures between protein domains. The ensemble view of allostery that is illuminated by these studies suggests that rather than being seen as switches with fixed responses to allosteric activation, ensembles can evolve to be “functionally pluripotent”, with the capacity to up or down regulate activity in response to a stimulus. This result not only helps to explain the prevalence of intrinsic disorder in transcription factors and other cell signaling proteins, it provides important insights about the energetic ground rules governing site-to-site communication in all allosteric systems.Relevant citationMotlagh HN, Hilser VJ: Agonism/antagonism switching in allosteric ensembles. Proceedings of the National Academy of Sciences of the United States of America (2012) 109(11):4134-4139.

Interplay between allostery and intrinsic disorder in an ensemble

Allostery is a biological phenomenon of critical importance in metabolic regulation and cell signalling. The fundamental premise of classical models that describe allostery is that structure mediates 'action at a distance'. Recently, this paradigm has been challenged by the enrichment of IDPs (intrinsically disordered proteins) or ID (intrinsically disordered) segments in transcription factors and signalling pathways of higher organisms, where an allosteric response from external signals is requisite for regulated function. This observation strongly suggests that IDPs elicit the capacity for finely tunable allosteric regulation. Is there a set of transferable ground rules that reconcile these disparate allosteric phenomena? We focus on findings from the human GR (glucocorticoid receptor) which is a nuclear transcription factor in the SHR (steroid hormone receptor) family. GR contains an intrinsically disordered NTD (N-terminal domain) that is obligatory for transcription activity. Different GR translational isoforms have various lengths of NTD and by studying these isoforms we found that the full-length ID NTD consists of two thermodynamically distinct coupled regions. The data are interpreted in the context of an EAM (ensemble allosteric model) that considers only the intrinsic and measurable energetics of allosteric systems. Expansion of the EAM is able to reconcile the paradox that ligands for SHRs can be agonists and antagonists in a cell-context-dependent manner. These findings suggest a mechanism by which SHRs in particular, and IDPs in general, may have evolved to couple thermodynamically distinct ID segments. The ensemble view of allostery that is illuminated provides organizing principles to unify the description of all allosteric systems and insight into 'how' allostery works.

An ensemble view of thrombin allostery.

Thrombin is the central protease of the coagulation cascade. Its activity is tightly regulated to ensure rapid blood clotting while preventing uncontrolled thrombosis. Thrombin interacts with multiple substrates and cofactors and is critically involved in both pro- and anticoagulant pathways of the coagulation network. Its allosteric regulation, especially by the monovalent cation Na(+), has been the focus of research for more than 30 years. It is believed that thrombin can adopt an anticoagulant ('slow') conformation and, after Na(+) binding, a structurally distinct procoagulant ('fast') state. In the past few years, however, the general view of allostery has evolved from one of rigid structural changes towards thermodynamic ensembles of conformational states. With this background, the view of the allosteric regulation of thrombin has also changed. The static view of the two-state model has been dismissed in favor of a more dynamic view of thrombin allostery. Herein, we review recent data that demonstrate that apo-thrombin is zymogen-like and exists as an ensemble of conformations. Furthermore, we describe how ligand binding to thrombin allosterically stabilizes conformations on the continuum from zymogen to protease.

Structural and Energetic Basis of Allostery

Allostery is a biological phenomenon of fundamental importance in regulation and signaling, and efforts to understand this process have led to the development of numerous models. In spite of individual successes in understanding the structural determinants of allostery in well-documented systems, much less success has been achieved in identifying a set of quantitative and transferable ground rules that provide an understanding of how allostery works. Are there organizing principles that allow us to relate structurally different proteins, or are the determinants of allostery unique to each system? Using an ensemble-based model, we show that allosteric phenomena can be formulated in terms of conformational free energies of the cooperative elements in a protein and the coupling interactions between them. Interestingly, the resulting allosteric ground rules provide a framework to reconcile observations that challenge purely structural models of site-to-site coupling, including (a) allostery in the absence of pathways of structural distortions, (b) allostery in the absence of any structural change, and (c) the ability of allosteric ligands to act as agonists under some circumstances and antagonists under others. The ensemble view of allostery that emerges provides insights into the energetic prerequisites of site-to-site coupling and thus into how allostery works.

Agonism/antagonism switching in allosteric ensembles

Ligands for several transcription factors can act as agonists under some conditions and antagonists under others. The structural and molecular bases of such effects are unknown. Previously, we demonstrated how the folding of intrinsically disordered (ID) protein sequences, in particular, and population shifts, in general, could be used to mediate allosteric coupling between different functional domains, a model that has subsequently been validated in several systems. Here it is shown that population redistribution within allosteric systems can be used as a mechanism to tune protein ensembles such that a given ligand can act as both an agonist and an antagonist. Importantly, this mechanism can be robustly encoded in the ensemble, and does not require that the interactions between the ligand and the protein differ when it is acting either as an agonist or an antagonist. Instead, the effect is due to the relative probabilities of states prior to the addition of the ligand. The ensemble view of allostery that is illuminated by these studies suggests that rather than being seen as switches with fixed responses to allosteric activation, ensembles can evolve to be "functionally pluripotent," with the capacity to up or down regulate activity in response to a stimulus. This result not only helps to explain the prevalence of intrinsic disorder in transcription factors and other cell signaling proteins, it provides important insights about the energetic ground rules governing site-to-site communication in all allosteric systems.

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.