Fracture healing is critically dependent upon an adequate vascular supply. The normal rate for fracture delayed or non-union is estimated to be between 10 and 15%, and annual fracture numbers are approximately 15 million cases per year. However, when there is decreased vascular perfusion to the fracture, incidence of impaired healing rises dramatically to 46%. Reduction in the blood supply to the fracture can be the result of traumatic injuries that physically disrupt the vasculature and damage supportive soft tissue, the result of anatomical location , or attributed to physiological conditions such as age, diabetes, or smoking. The role of the vasculature during repair is multifaceted and changes during the course of healing. In this article, we review recent insights into the role of the vasculature during fracture repair. Taken together these data highlight the need for an updated model for endochondral repair to facilitate improved therapeutic approaches to promote bone healing. Bones are highly vascularized tissues with a diverse and central role in normal physiology; normal functions include, regulating systemic homeostasis, enabling bone turnover, and providing a niche for hematopoiesis. As such, it is necessary for bone regeneration to reestablish both the embedded osseous vasculature and the bone marrow cavity in order to restore full functionality to the tissue. Delay or compromise to the angiogenic process during fracture repair has a significant effect on the progression of proper bone healing. Ischemia at the fracture site dramatically reduces the rate of fracture repair and is a major risk factor for non-healing fractures, which are clinically termed “non-union” , 2. WithFracture non-union is a clinical diagnosis made when a patient has clinical symptoms that include pain, instability, and poor radiographic bone healing at a time point after which healing should be expected. Classically, three major types of non-unions have been described – hypertrophic, atrophic, and oligotrophic – and are diagnosed based on the radiological evidence of bone formation. Atrophic non-unions are classically associated with a poor biological response, including an insufficient blood supply , and a mBone grafting is one technology used to stimulate bone regeneration in cases of fracture non-union, or for a number of other clinical indications that require bone formation be augmented  . AutograDevitalized bone allografts have been developed to address some of the clinical shortcomings of autografts. Unfortunately, failure rates associated with allograft procedures are estimated between 16 and 35% . The undThe necessity of the bone regenerate to revascularize for proper function is the end-result of a cascade of carefully coordinated events that require the vasculature. This review provides an overview of how the vasculature contributes throughout the process of fracture healing to create a hematoma, deliver cells, modulate oxygen tension, and deliver growth factors involved in the healing process. Here, we focus only on the process of indirect bone healing, where bone is formed following a cartilage intermediate in a process called endochondral ossification. The evidence presented from this compilation of data illustrates an explicit need to update the existing model of endochondral bone repair, and emphasizes the potential opportunity for novel therapeutic treatments for bone repair that focus on the chondrogenic and angiogenic stages of repair.The essential role of the vasculature in bone healing becomes evident at the onset of the injury as a fracture typically exposes the bone marrow cavity and disrupts blood vessels within the bone and surrounding soft tissues. Formation of a hematoma around the trauma is a critical first step in fracture healing and its absence will delay healing . The hemWhile activation of the fracture repair process seems dependent on an adequate pro-inflammatory response, resolution of this inflammatory state is critical to progression of healing. The anti-inflammatory state is the due largely to phenotypic modulation of the macrophages to the M2  population in the fracture callus. These anti-inflammatory macrophages secrete a battery of cytokines and growth factors to promote tissue repair and angiogenesis, such as, IL-10, PDGF, VEGF, transforming growth factor-β (TGFβ), epidermal growth factor (EGF), and arginase . During Following the hematoma and early inflammation stage, fracture healing can proceed through two different pathways: intramembranous or endochondral ossification. Intramembranous ossification is the process of direct bone formation, where osteochondral progenitors differentiate directly into osteoblasts. Osteogenesis is mediated by the co-expression of the runt-related transcription factor 2 (RUNX2) –32 and βEndochondral ossification is the process of indirect bone formation in which bone is formed through a cartilage intermediate. The cartilage portion of the fracture callus results from chondrogenic differentiation of periosteal progenitor cells. Chondrogenesis is initiated by the expression of SOX9, a member of the SRY-related high-mobility group box transcription factor family required for chondrogenesis . SOX9 reDuring fracture healing, both intramembranous and endochondral ossification typically contribute in part to the healing process, but dominance of healing by one pathway versus the other is influenced largely by stability at the fracture site. Reduction, or realignment, of the fracture is a primary goal in successfully treating the bone to promote healing. Full reduction, which results in complete stability through the application of fixators such as bone plates, will promote healing by intramembranous ossification. More commonly, fractures are reduced to enable some motion, such as those treated with an intramedullary nail. This promotes a more robust healing reaction in which endochondral ossification is the primary healing response within the fracture gap Figure B.In addition to the mechanical stability of the fracture, the origin of the progenitor cells that contribute to the fracture callus also influences the bone healing pathway by providing cells with distinct differentiation potentials. The osteochondral progenitors that give rise to the fracture callus are recruited locally from the endosteum and periosteum of the fractured bone . The celThe molecular and cellular mechanisms that control endochondral ossification are more complex than intramembranous ossification. In this pathway, SOX9 expression promotes condensation and commitment of the osteochondral progenitors toward the chondrogenic lineage. After specification, subsequent SOX9 activity is necessary to maintain cell morphology and the chondrogenic phenotype through maturation to hypertrophy. In a process that resembles the well-described process of endochondral ossification in the growth plate , cartilaSimilar to growth plate chondrogenesis, chondrocytes within the fracture callus also undergo a controlled and progressive matruation. Hypertrophic maturation of the chondrocyte is marked by a strong expression of collagen X in the area where cartilage transitions to bone. These hypertrophic chondrocytes are highly bioactive to promote mineralization and vascular invasion of the cartilage matrix through potent expression of BMP , MMP-13 The conversion of cartilage to bone in the fracture callus occurs adjacent to the invading vasculature in a domain that we will refer to as the “transition zone.” The cartilage in this transition zone becomes calcified and the extracellular matrix stains as bone by classic Trichrome or Direct Red histology techniques, yet the cells retains their hypertrophic morphology Figures A,B. ThesSox9 expression in the condensed mesenchymal cells to promote chondrogenesis, enable chondrocyte survival, and enhances extracellular matrix synthesis , insulin, and Vitamin D. Importantly, vascular invasion also corresponds with calcification of the cartilage matrix and transition to bone. The precise molecular mechanisms, and location of signaling, which facilitate mineralization of the cartilage at the fracture callus is not clear. Changes in calcium concentration are sufficient to induce mineralization of these hypertropic chondrocytes, yet it remains unclear what the source of calcium is and which cells sense these changes. Mineralization of the cartilage matrix is also initiated by osteoinductive signals, such as BMP, secreted by both the chondrocytes themselves , and by Conversion of calcified cartilage to bone requires that the cartilage matrix is degraded and replaced by bone matrix. Major differences in the extracellular matrix composition include a conversion of collagen II in cartilage, to collagen I in bone, and degradation of the glycosaminoglycans (GAGs) in cartilage. It remains debated how the extracellular matrix is remodeled during this conversion. Hypertrophic chondrocytes make MMP-13, which is one of the major enzymes responsible for degrading both collagen II and aggrecan, the major GAG in cartilage. Furthermore, the vascular endothelial cells secrete MMP-9, one of the gelatinases with a high specificity for degraded collagens, thereby accelerating cartilage degradation upon vascular invasion. Alternatively, a cellular degradation of the cartilage matrix may be occurring through the action of osteoclasts that are delivered to the cartilage matrix through the vasculature. Osteoclasts are recruited to calcified cartilage both by production of the receptor activator of NF-κB ligand (RANKL) , 71 in tThe commitment and progression of the chondrocyte during endochondral ossification is a long-standing question that is not fully understood. In the proliferating and pre-hypertrophic chondrocytes, SOX9 [and associated co-factors SOX5/6 ] establiTraditionally, it was believed that the hypertrophic chondrocytes exits the cell cycle  and reprIn contrast to the model described above, an alternative mechanism for bone formation directly from the chondrocyte has been reported. Recently, we published a paper demonstrating that cartilage can transform into bone during endochondral bone repair using a number of different lineage tracing strategies . This woThe mechanism by which chondrocytes transform into osteoblasts remains unclear. One possible mechanism is that chondrocytes mature directly into osteoblasts, and that the expression of canonical bone markers in the hypertrophic chondrocyte represents the natural phenotypic progression of endochondral chondrocytes , 103–108Another possibility is that the chondrocyte can “de-differentiate” into a progenitor-like state before becoming bone , 110. SuThe transition zone is always immediately adjacent to the invading vasculature Figures  and 2. GImproving our knowledge of the basic mechanisms underlying normal and delayed fracture repair are central to developing improved therapies for bone healing. The information presented here suggests that the traditional view of endochondral bone repair needs to be updated in light of new data Figure . SpecifiDuring endochondral bone repair, formation of a soft cartilaginous callus is the next milestone in fracture healing. The cartilage itself is a highly angiogenic tissue that may be the primary mechanism for recruiting and establishing a functional vasculature and marrow space in the new bone. In turn, the invading vasculature is central in coordinating the conversion of the cartilage matrix to bone, both by providing osteoinductive signals , and by bringing osteoclasts in to facilitate remodeling of the tissue. Classically, it is also believed that the vasculature was the source of the osteoblasts that form the new bone. Newer data suggest that chondrocytes themselves directly contribute to the new bone. While this finding does not exclude the possibility that cells from the invading vasculature do not contribute to endochondral bone formation , it doesAfter the cartilage is converted to bone the tissue is highly trabeculated. Remodeling is the final phase of healing: it allows for complete bridging of the fracture site, structural integrity, and the maintenance of the marrow cavity for hematopoiesis. This process ultimately produces a cortical bone structure that is nearly identical to the original bone in both form and function through a tightly regulated coupling of osteoblast and osteoclast activity.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity. Many approaches have been proposed for solving this problem [Multi-target tracking involves estimating the current state  theory has been proposed to solve the problem of multi-target tracking . Mahler The PHD filter for multiple extended target tracking has drawn considerable attention ,9. HowevThe reminder of this paper is organized as follows: in i-th cell of the partition Measurement partitioning is a matter of importance for extended target tracking, since the target potentially generates more measurements per scan. The purpose of measurement partitioning is to collect measurements from one target into the same partition. In a dense target and clutter environment, it is hard to partition the measurements efficiently and accurately. In order to illustrate the process of measurement partitioning, let us assume there are three measurements in the measurement set All the potential partitions are required to update the current states of targets in the original ET-PHD filter. As the cardinality of the measurement set grows, the number of potential partitions grows very quickly. In a dense target and clutter environment, this makes the ET-PHD filter computationally intractable.As a crucial problem, measurement partitioning has drawn considerable attention in multiple extended target tracking studies. In  GranstroIn  Zhang prIn  Yang proAlthough the measurement set can be partitioned by the state-of-the-art methods, they are not efficient and accurate enough. In the Distance Partitioning algorithm, each distance threshold only can produce a unique partition. In order to get the correct partition, lots of distance thresholds are needed to generate enough partitions, and the distance thresholds are empirical values. As the number of targets increases, this makes the extended target tracking process computationally intractable. Although the fuzzy ART model and spectral clustering method reduce the computational burden, the partitioning results would become rather bad in a cluttered dense scene, and the results are also dependent on the selection of the cluster parameters. In order to tackle the problem of the efficiency and accuracy of partitioning method, a novel measurement partitioning algorithm is proposed based on affinity propagation clustering. Compared with the state-of-the-art partitioning methods, affinity propagation clustering retrieves the number of partition iteratively, so we do not have to set clustering parameters, and it always achieves better partitioning results with less computational cost.Mahler expanded the PHD filter for extended target tracking in , which ii-th target generated is given as:i-th extended target at time step i-th target will not generate extended measurements.More measurements are potentially generated by the extended target per scan. Assuming the number of extended measurements is a Poisson distribution, the probability of at least one measurement generated by the i-th target is detected with the probability The The target state ribution , and theAssuming that The update formulas for the ET-GM-PHD filter are given by:In the multiple extended targets environment, the sensor not only receives measurements which are generated by the targets, but also receives a set of clutter measurements, which are not generated by any target. The clutter is modeled as a Poisson distribution with intensity i-th measurement at time step j-th predicted measurement.The elliptical gating defined by a validation region on the basis of the set of predicted measurements is used to remove clutter measurements. The residual error vector is defined as:The covariance matrix of residual error is:In the case of linear Gaussian system, the elliptical gate is the maximum likelihood gate , and it If ents see b.Affinity propagation is a clustering algorithm, messages between data points are exchanged to find a subset of exemplar points that best describe the date points , a set oLet the measurements in The similarity matrix All the measurements have the same opportunity to be chosen as an exemplar measurement in the affinity propagation clustering algorithm, that is, it does not have to set the initial cluster centers, so the preference parameters The affinity propagation algorithm exchanges two messages between measurements. According to the above-mentioned procedure, the messages exchanged between measurements are updated until an appropriately set of exemplars and partitions.(1)Assuming that (2)The prediction of the extended target intensity (3)Remove the clutter measurements:Given a threshold The meaurements falling in the elliptical region can be obtained by:(4)Measurements partitioning:Calculate the similarity matrix All the target-generated measurements have the same opportunity to be chosen as an exemplar. In our study, the preferernce parameters are set to the mean value of the similarities, given by:All the resonsibilities (5)The predicted intensity (6)The Gaussian component merging and pruning process are similar to the GM-PHD filter for standard targets tracking described in , and theThe proposed algorithm of affinity propagation clustering for extended target tracking using ET-GM-PHD filter can be summarized as following steps:In order to analysis the computational complexity of the partitioning algorithm, we define For the distance partitioning method, in order to obtain the correct partition, lots of partitions are generated, so its complexity is much greater than O(Nk4) .For the ART partitioning method, the computational complexity involves creating the normalized measurement vectors, computing a category choice, and updating the weight vectors. The worst case complexity is approximated as O(Nk2) . HoweverFor the spectral clustering partitioning method, the computational complexity involves creating the similarity matrix, calculating the Laplacian matrix, normalizing the similarity matrix and a K-means clustering. The worst case complexity is approximated as In order to present the performance improvements achieved with the proposed algorithm, we consider the case in which four targets with crossing a certain region of the The dynamics model and measurement model are described as linear Gaussian models, the probability of target survival The RFS of spontaneous birth target is the Poisson distribution with intensity The elliptical gating is equipped with In order to evaluate the performance of the proposed algorithm, 100 Monte Carlo numerical simulations are performed and two metrics are used, one is the estimate of the target number, the other is the optimal sub-pattern assignment (OPSA) distance .The affinity propagation clustering is introduced into measurement partitioning for extended target tracking for the first time, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering being capable of partitioning the measurement in a densely cluttered environment with high accuracy. The numerical results show a considerable performance improvement of the proposed algorithm both in the state estimate accuracy and the computational cost compared to the conventional methods.


Some of the diagnostic, pathogenic and histological issues of gluten related disorders are still controverted. There are numerous guidelines and algorithms in the current literature, but when facing a scenario like presentation with mildly abnormal duodenal histology with positive or negative serology, most of clinician have difficulties in making a confident diagnosis. This book  is one oIn this book celiac disease, dermatitis herpetiformis and a wide variety of other conditions like gluten ataxia, gluten allergy and non-celiac gluten sensitivity have been discussed. Because each chapter is written by a different specialist, there is, as in most multiauthored books, a heterogeneity in the topics that are covered. The first section of this book is allocated to the basic science reviewing the current knowledge on environmental, oral tolerance, treatment, genetic and immunological factors involved in celiac disease. It discusses the widely-accepted model of the pathogenesis of celiac disease which focuses on the stimulation of gluten-reactive CD4+ T cells by TG2-deamidated gluten peptides presented by HLA-DQ2/DQ8 molecules, and other non-HLA susceptibility loci that may contribute with small effects in production of inflammatory response in susceptible individuals. This section is a true enlightenment, stimulating and skilfully simplifies the matters for students, scientists in medicine, immunology and pathology.The same section reviews the fascinating association between celiac disease and changes in the composition of intestinal microbiota that may be associated with the HLA-DQ genotype, with important implications in the pathogenesis of GRD. The imbalances in gut microbiota seem to be related to the inflammation in active phase of CD and they could also be a predisposing factor for disease development. With other word the composition of the gut microbiota could influence the release of pro-inflammatory cytokines triggered by gluten peptides. Interestingly the microbiota is not completely restored after a gluten-free diet. Bifidobacterium, B. longum and Lactobacillus), and increased potentially unhealthy bacteria like E. coli and total Enterobacteriaceae. The negative effect has been attributed to the fact that polysaccharides usually reach the distal part of the colon partially undigested, and constitute one of the main energy sources for commensal components of the gut microbiota. Olivares and Sanz eloquently suggest a disruption of the delicate balance between the host and its intestinal microbiota (dysbiosis) would favour the overgrowth of opportunistic pathogens and weaken the host defences against infection and chronic inflammation via possible alterations in mucosal immunity.Recently, there have been some concerns raised on the gluten free products  and the The second section revises the advances in understanding the clinical spectrum of these disorders. In this sections the topic like clinical presentation, diagnostic criteria, the role of histology, extra-intestinal manifestations and the issues related to the quality of life of GRD patients have been discussed. Using γδ+ IEL are not an option available in current practice and in the same section the role of γδ+ IEL count, the detection of subepithelial tissue transglutaminase antibodies, the whole blood cytokine release assays (ELISPOT), and the tetramer test are highlighted.The authors discuss the challenges concerned seronegative villous atrophy. Despite the current believe it might be this condition in fact doesn’t exist. What we have been calling seronegative coeliac disease might in fact be a feature of non-coeliac gluten sensitivity (NCGS). This is a new condition and despite the fact that most of patients present with minimal enteropathy , there iThe third section explores the evolution of gluten in particular bread products that are most widely consumed in the western world. It also describes the great challenge of the elaboration of high quality gluten-free products but less expensive than the products at present available. This section on evolution of gluten-free foods will be of outmost interest to dietitians, nutritionists and patient. We would like to congratulate the authors and editors of this eminently practical book which is well suited to the needs of all scientists and clinicians who are likely to deal with patients in whom intra or extra gastrointestinal disorders develop in conjunction to gluten related disorders.


An unresolved issue in patient-specific models of cardiac mechanics is the choice of an appropriate constitutive law, able to accurately capture the passive behavior of the myocardium, while still having uniquely identifiable parameters tunable from available clinical data. In this paper, we aim to facilitate this choice by examining the practical identifiability and model fidelity of constitutive laws often used in cardiac mechanics. Our analysis focuses on the use of novel 3D tagged MRI, providing detailed displacement information in three dimensions. The practical identifiability of each law is examined by generating synthetic 3D tags from in silico simulations, allowing mapping of the objective function landscape over parameter space and comparison of minimizing parameter values with original ground truth values. Model fidelity was tested by comparing these laws with the more complex transversely isotropic Guccione law, by characterizing their passive end-diastolic pressure–volume relation behavior, as well as by considering the in vivo case of a healthy volunteer. These results show that a reduced form of the Holzapfel–Ogden law provides the best balance between identifiability and model fidelity across the tests considered. Cardiac imaging provides a powerful tool for assessing cardiac function and pathology, offering valuable insight into the kinematics and tissue characteristics of the heart. Its joint use with multiscale mathematical models of the heart , cardiac magnetic resonance imaging (MRI) have been successfully employed to accurately capture epicardial and endocardial motion, thus providing important bulk metrics such as ejection fraction and cavity volumes. In addition, the development of speckle tracking echocardiography Meunier  and cardstructural identifiability for the various constitutive laws, i.e., whether it is possible to uniquely determine parameter values, given infinite well-defined noise-free data . Supposing we have To understand the behavior of the minimization problem, we first aim to better understand the behavior of the model and its parameters. Suppose we consider mentclass2pt{minimmentclass2pt{minimUsing this notation, our quasi-static mechanics problem is .In data-based parameter estimation procedures, we often rely on some objective function to guide the choice of parameters. Since the parameters are not observed in most cases, we rely instead on comparing states with observations. In these cases, it is necessary that the objective function Using This is, however, unlikely to occur in a real context due to two dominant factors: (1) data noise and resolution, (2) model fidelity. The introduction of noise, or degradation in data due to image resolution, introduces offsets which make the likelihood of Supposing that the model is a good representation of the tissue in vivo, we can then write The offset in Eq. Characterizing the behavior of the objective function Coupling can also be deduced by the Hessian matrix of the objective function at the obtained minimum. Using the Taylor expansion of For each of the considered constitutive laws, the behavior of CHeart—a multi-physics software tool based on , proposed by Klotz et al.  is an important determinant of cardiac function; therefore, the considered constitutive laws were compared with respect to their ability to reproduce a physiological EDPVR. For this comparison, the empirical EDPVR proposed by Klotz et al.  was chosFigure The in silico tests performed in Sects. In this section the reduced Holzapfel–Ogden law is employed in an in vivo case of a healthy volunteer. The behavior of the objective function The extracted 3D displacement field was compared with passive inflation simulation results to provide parameter estimates for the reduced Holzapfel–Ogden law. As the LV pressure trace was not part of the available data, we were not able to obtain unique values for each parameter separately. However, taking advantage of the linear dependence of the law on the parameters and accordingly their proportionality to inflating pressure, we were able to uniquely estimate the ratio Figure The similarity in behavior of A relatively coarse, lower order mesh was used for the in silico example. This choice was based on the small computational time per simulation, which allowed for the large number of simulations performed to provide the landscape of the objective function over the parameter space. Nevertheless, as illustrated in Appendix Further, only one objective function has been considered, even though other objective functions might be able to elucidate other characteristics for the behavior of the various laws. Other objective functions are not considered here, as such a study would require proving that a potential objective function is discerning. If non-discerning, proving that unique parameter identification is achievable becomes challenging as it then must rely on the observations not exposing non-uniqueness of the objective function. Nevertheless, the chosen Ten diastolic frames were used as observations in the in silico test. This choice was based on the number of diastolic frames in the available 3D tagged MRI data . However, the number of diastolic image frames used in other studies is variable with authors considering all or part of diastole. This variability is due to assumptions on residual active tension, the presence of which is confirmed by decreasing cavity pressures even after the opening of the mitral valve (Pasipoularides et al. Even though the effect of unbiased noise is examined, several aspects of the process or the data that may have a biased influence are not considered. For instance, the resolution and number of tagged lines in the data along with the tracking algorithm used are likely to incorporate consistent error in the parameter estimation process. The boundary conditions used in the simulations are also likely to influence the identifiability and model fidelity results. Understanding these attributes is important for patient-specific personalization; therefore, further work is required to clarify the influence of these factors on the landscape of the objective function and the estimation process in general.The reference configuration is assumed to be known and correspond to a specific frame of diastole in the in vivo example. Even though this is a common approach, we have verified that the identifiability of model parameters is not sensitive to the frame used as a reference configuration for the in silico case (see Appendix Finally, only one in vivo case is considered; thus, further work is needed to examine effects in vivo, such as noisy or low-resolution data, or diseased cases where the cardiac deformation is likely to differ significantly. However, the in silico tests performed provide a standard, giving the “best case scenario,” which can be anticipated when using real data. Further, the in vivo and in silico model fidelity of the reduced Holzapfel–Ogden law encourage the application of the law to diseased cases as well.In this paper, we examine the practical identifiability and model fidelity of a range of cardiac constitutive laws using 3D tagged MRI as the available data. In order to investigate the practical identifiability of the laws considered and examine the potential of using 3D tags in parameter estimation applications, we generate synthetic 3D tags directly from simulation results and assess the behavior of the objective function over the parameter space through parameter sweeps. The laws considered are also compared with respect to their ability to represent physiological cardiac motion and EDPVR, elucidating the primary components that should guide the choice of an appropriate cardiac constitutive law—namely reliable parameters and adequate representation of cardiac deformation and function.Our results verify the reported coupling of the transversely isotropic Guccione law, suggesting the need for a law with better identifiability characteristics that would allow for reliable parameter estimates. The Neo-Hookean law is shown to have good identifiability characteristics, due to linear parameter dependence. The stiffness parameter is identifiable, provided adequate deformation is present in the available data. However, due to its isotropy, Neo-Hookean deformation misses key characteristic deformation modes, mainly long-axis elongation and twist. Further, it cannot reproduce physiological pressure–volume response.Building on the Neo-Hookean model, the Neo-fiber law maintains the good identifiability characteristics, while reproducing physiological cardiac deformation. Both parameters are identifiable, even though sufficient deformation is required to allow identifiability of the fiber parameter due to the structure of the constitutive law. However, using the Neo-fiber law leads to inaccurate pressure–volume response, which cannot match the empirical Klotz curve.The reduced Holzapfel–Ogden law, however, combines all important attributes considered, offering a balance between practical identifiability and adequate representation of cardiac deformation and EDPVR. Its use in an in vivo case where good identifiability characteristics are maintained supports the conclusion that the reduced Holzapfel–Ogden law offers a sensible choice in patient-specific applications with 3D tagged data.


Understanding channel structures that lead to active sites or traverse the molecule is important in the study of molecular functions such as ion, ligand, and small molecule transport. Efficient methods for extracting, storing, and analyzing protein channels are required to support such studies. Further, there is a need for an integrated framework that supports computation of the channels, interactive exploration of their structure, and detailed visual analysis of their properties.ChExVis. We apply the method on a number of known channel containing proteins to extract pore features. Results from these experiments on several proteins show that ChExVis performance is comparable to, and in some cases, better than existing channel extraction techniques. Using several case studies, we demonstrate how ChExVis can be used to study channels, extract their properties and gain insights into molecular function.We describe a method for molecular channel extraction based on the alpha complex representation. The method computes geometrically feasible channels, stores both the volume occupied by the channel and its centerline in a unified representation, and reports significant channels. The representation also supports efficient computation of channel profiles that help understand channel properties. We describe methods for effective visualization of the channels and their profiles. These methods and the visual analysis framework are implemented in a software tool, ChExVis supports the visual exploration of multiple channels together with their geometric and physico-chemical properties thereby enabling the understanding of the basic biology of transport through protein channels. The ChExVis web-server is freely available at http://vgl.serc.iisc.ernet.in/chexvis/. The web-server is supported on all modern browsers with latest Java plug-in.The online version of this article (doi:10.1186/s12859-015-0545-9) contains supplementary material, which is available to authorized users. The selectivity of the channels in permitting access to specific types of molecules and the micro-environment that it provides is crucial to the nature of the molecule that it transports a was fir, where d is the length of the channel.We propose the computation and visualization of We compute the variation of the square root of power distance along the channel. We call this the radius profile because it is the radius of the orthogonal sphere to the three closest atoms. This profile provides a good estimate of the width of the channel, and is therefore useful for gaining information about the potential bottlenecks along the channel.Electrostatic potential is computed for the whole molecule using the finite element method APBS . The comWe compute the average of different physico-chemical quantities over the four atoms of each tetrahedron in the channel. Important physico-chemical profiles include hydophobicity , charge,Conservation captures the tendency of an amino acid residue to remain unchanged over long periods of evolution. Conserved residues may be of functional significance. We use the ConSurf server , which ub within the resulting region, see Figure Channel profiles are visualized as a simple XY-plot that shows the variation of the property along the channel. To capture the correlation of the radius profile with a second channel profile, we compute a 2D projection of the channel using the radius profile. The second profile is shown using color-mappingnode box containing four smaller atom boxes, which correspond to the four atoms at the vertices of the tetrahedron. The channel is shown as a sequence of node boxes. Adjacent node boxes are connected by three edges that correspond to the triangle shared by the two adjacent tetrahedra. Figure MOLE to show variation of physico-chemical properties across a channel.We design a novel visualization metaphor for channels based on tetrahedral representation of channels. A channel is visualized using the 2D projection. We use the correspondence between the channel network nodes and the weighted Delaunay triangulation tetrahedra, and between the channel network edges and the weighted Delaunay triangulation triangles to effectively visualize different properties along the channel. Each node is displayed as a The channel is also visualized in 3D within the context of the molecule. We support visualizing channels as tetrahedra, union of balls, or as a surface of the union of balls. The union of balls representation of the channel is obtained by computing the orthogonal sphere for each channel tetrahedron. The skin surface is a smooth surface that wraps around the given set of spheres . We compCHEXVIS. A web-server [The channel extraction and visualization methods presented in this paper are implemented into a software tool called b-server  has beenCHEXVIS web-server supports submission of jobs by specifying the PDB ID or uploading a PDB file. Biological assemblies of proteins can also be uploaded. While the default values for the parameters often produce required results, the user may optionally tune multiple parameters. Further, the server also computes transmembrane pores similar to existing tools like POREWALKER that exclusively detect such channels. The unique and novel 2D representation of the channel enables a detailed analysis of its physico-chemical properties at a level of detail not supported by other existing software. The extracted channels and their properties are displayed within the browser using Java/JMol based applets. PyMOL [The s. PyMOL  scripts CHEXVIS web-server with four channel extraction tools viz.MOLE 2.0 [CAVER Analyst 1.0 [MOLAXIS [POREWALKER [CHEXVIS is the advantage of extensive visual analysis of channels using channel profiles along with information-rich 2D representation of channels. However, we also claim that the proposed channel extraction method is significantly better than the other tools. To support our claim, we compared the results of these methods for a set of 29 diverse protein structures. The channels detected in the seven enzymes which are a part of our dataset, are summarized in Additional file We compare MOLE 2.0 , CAVER Alyst 1.0 , MOLAXIS[MOLAXIS  and POREREWALKER . Table 1CHEXVIS computes three types of outputs viz. channels leading to active sites, pores and transmembrane pores. MOLE computes channels leading to active sitesc as well as pores, while CAVER computes only the former. In addition to computing channels leading to a site in the molecule, MOLAXIS can also extract transmembrane pore if the axis and a constraint sphere are provided. POREWALKER computes a single transmembrane pore. We compare CHEXVIS channels with the channels computed by MOLE, CAVER and MOLAXIS. CHEXVIS pores are compared with MOLE pores. The top transmembrane pore computed by CHEXVIS is compared with the transmembrane pore computed by MOLAXIS and POREWALKER.For comparison, we used the default parameters for channel extraction provided by the respective tool. The qualitative comparison of results produced by different tools using default input parameters are summarized in Additional files MOLE provides excellent stand-alone and web-based interactive interface for extraction of multiple channels. MOLE utilizes the CSA database [CHEXVIS web-server. As evident from results in Additional file CHEXVIS, MOLE reports more tunnels leading to active sites. This is due to the fact that CHEXVIS reports a single best channel from each active site, while MOLE extracts multiple tunnels and clusters the extracted channels to reduce the number of reported channels. The option to extract multiple channels from a single point of origin is available in a stand-alone version of CHEXVIS and will be included into the web-server soon.database  and HETAMOLE does not support extraction of transmembrane pores. We used its pore extraction option to determine pores and compared the results against pores extracted by CHEXVIS. Among the 22 transmembrane proteins, using default parameters MOLE fails to identify correct transmembrane pore in 17 structures. In comparison, CHEXVIS identifies correct transmembrane pores in 19 structures and partially correct pores in the remaining 3 structures  the changes in the diameter of the channel, b) accessible solvent area/volume of the channel, c) residues lining the channel, and, d) physicochemical properties of residues lining the channel. As seen in the profile views, in both states, pore-interfacing residues from all the five chains lie in fairly equivalent positions. The 2D profile plots of the residue contributions to the channel show that the side chain atoms of the channel-lining residues point into the channel lumen  channel and lists residues lying on the outer pore, central canal, and selectivity filter. As seen in Figure CHEXVIS while projections of the channel neighbourhood capture other basic residues that are typical of the family . The plots also show that the constricted regions are contributed by loops and lined by residues that are poorly conserved in related homologues except for regions in the immediate vicinity of the constriction. Pore size can dictate the size of the substrate that can pass through the channel [CHEXVIS that capture various views and properties of the residues lining the channel will add to knowledge on channel specificity and residue layout within the pore and a better appreciation of the differences between seemingly related molecules.The outer membrane proteins of several gram negative bacteria enable uptake of nutrients either through non-specific channels/porins and therefore, at high substrate concentrations, or in a substrate-specific manner through outer membrane carboxylic acid channels (Occ family). Since the Occ channel proteins are dedicated to the uptake of wide variety of substrates, we derived the pore features for different members of this family  and compared them to determine if the visual capture of the channel properties through 2D and 3D views could distinguish their various sub-types. Comparisons of the two OM channels, OccD1 (3SY7) and OccK1 (3SYS) shows that both channels form monomeric barrels around a central channel that is constricted at a region lined predominantly by basic residues such as arginine and lysine (see Figures  channel . The dif channel . Contras channel . We anti+, Na+ and Ca2+ ions in response to changes in membrane voltage. The KcsA potassium channel contains two trans-membrane segments and a P-loop signature that harbours a selectivity filter, which selects for the conductance of K+ over Na+ ions. Residues with a role as the filter from the four subunits, project into the channel centre to form a narrow pore. The K+ ion-binding sites formed by residues G79, Y78, G77, V76 and T75, that form the ion binding site and square anti-prism around K+ [+ ion through its carbonyl oxygen. Replacement of T75 with C75 in the mutant channel [+ conductivity because of the absence of crucial oxygen atoms. This minor but critical change is captured by CHEXVIS visualizations (Figure Voltage-activated cation channels are membrane proteins that selectively conduct Kround K+ , are con channel  also ress Figure .Figure CHEXVIS, that is available as a web server. The method is automated and accepts 3D co-ordinates of the structure or its PDB-ID as input. Channel location in the protein may be guided using bound ligand information, CSA, or be specified by the user. Here, channels are represented as a set of connected tetrahedra derived from the alpha complex. This representation enables efficient computation and interactive visualization of the channels together with different geometric and physico-chemical profiles.We developed a new method for the extraction, visualization, and visual exploration of channels in biomolecules through a software tool, CHEXVIS that can identify multiple channels will be useful to understand the properties of the multiple channels detected in the protein family of interest and the multiple routes that might be available for regulation of protein function in the family. The method is shown to perform well using a number of examples. As with all channel visualisation tools, manual intervention and intuition are vital to assess the importance of the reported channels. Examples of its application in understanding the biological function of the protein at various levels such as in the comparison of open and closed states of the protein, or in appreciating the wide substrate-specificities of the channel or comparison of channels with homologues have already been shown here. Further, the method lists the top channels for the protein and also reports all channels observed in the protein. Such studies coupled with information on conservation within the protein family and mutability of residues lining the channel will further the understanding of the basic biology of transport through membrane proteins.The availability of visualization tools such as a We use the term channel extraction because we view the problem as a special case of the feature extraction problem. The features of interest are channels and the data is a biomolecule represented in PDB format.b We use blue to red diverging color map, where blue corresponds to low values while red represents high values.cMOLE and CAVER use the term tunnel to refer to a channel that leads to a site inside the molecule, i.e., tunnels have one end point incident on a group of atoms buried inside the molecule while the other end point is close to the molecular exterior.


A simple three-component negative feedback loop is a recurring motif in biochemical oscillators. This motif oscillates as it has the three necessary ingredients for oscillations: a three-step delay, negative feedback, and nonlinearity in the loop. However, to oscillate, this motif under the common Goodwin formulation requires a high degree of cooperativity (a measure of nonlinearity) in the feedback that is biologically “unlikely.” Moreover, this recurring negative feedback motif is commonly observed augmented by positive feedback interactions. Here we show that these positive feedback interactions promote oscillation at lower degrees of cooperativity, and we can thus unify several common kinetic mechanisms that facilitate oscillations, such as self-activation and Michaelis-Menten degradation. The positive feedback loops are most beneficial when acting on the shortest lived component, where they function by balancing the lifetimes of the different components. The benefits of multiple positive feedback interactions are cumulative for a majority of situations considered, when benefits are measured by the reduction in the cooperativity required to oscillate. These positive feedback motifs also allow oscillations with longer periods than that determined by the lifetimes of the components alone. We can therefore conjecture that these positive feedback loops have evolved to facilitate oscillations at lower, kinetically achievable, degrees of cooperativity. Finally, we discuss the implications of our conclusions on the mammalian molecular clock, a system modeled extensively based on the three-component negative feedback loop. The identification of motifs within biological networks and assignment of function to those motifs has been a key undertaking of Systems Biology We use a minimal well-studied formulation of this three-component negative-feedback loop motif called the ‘Goodwin oscillator’ One long-recognized limitation of this negative feedback only motif is the need for a high degree of cooperativity (a Hill coefficient of at least 8) to produce oscillations We consider the three-component negative feedback loop motif represented by the Goodwin oscillator with various auxiliary loops listed in We first study the minimum degree of cooperativity required to produce oscillations in all motifs in For some parameter choices, positive feedback loops are not beneficial but rather detrimental, i.e., they increase the cooperativity requirement. In MM motifs, the detrimental parameter choices correspond to positive feedback on the most stable (longest lifetime) component. In this situation, the effective lifetime of the most stable component is lengthened further leading to increased mismatch between the lifetimes of the three components. This mismatch between component lifetimes is known to drastically increase the degree of cooperativity needed for oscillations even in the core motif c in motifs .The positive feedback strength These observations can also be derived by the theoretical analyses presented in the Analysis section.We showed in the previous section that the addition of positive feedback loops reduces the burden of nonlinearities in the system. We next evaluate which positive feedback produces the largest reduction in the cooperative requirement within a class of motifs for a given choice of parameters. In other words, for which positive feedback is the largest payoff obtained?For all three classes of motifs considered, positive feedback on the fastest dynamic  component in the three-component core motif produces the largest reduction in the cooperativity requirement ; for theThe CA-R motif, in up to 39% of the cases, had a smaller cooperativity requirement than a motif with positive feedback between the fastest two steps. In the CA-R motif, since the positive and negative feedback act between the same components, the reduction of the mismatch cost is confounded by the degree of cooperativity (see (12)). Therefore, there is no clear trend in the benefit of CA-R motif across different lifetimes of the components.The period of oscillations of the core motif is dependent only on the lifetimes of the three components in the loop. Thus, biological oscillators with the core motif can oscillate only with periods in the same order of magnitude as the component degradation rates. However, autonomous circadian oscillators have a period of 24h, despite constituent mRNA and protein half-lives being in the order of at most few hours. The positive feedback motifs, we consider, have consistently longer periods than the core motif for the same choice of parameters see . In otheOur results indicate that positive feedback loops reduce the cooperativity requirement with a likelihood and extent that is dependent on the particular motif and its parameterization. In many instances of biological oscillators, multiple feedback loops are observed in combination. We tested whether combinations of motifs in In this paper, we illustrated the ability of three classes of positive feedback loops to facilitate oscillations in a generic delayed negative feedback motif. We were primarily motivated by the dual observations that several oscillatory negative feedback motifs are accompanied by positive feedback loops, and models of simple oscillators require high degrees of cooperativity rarely observed in experiments. We showed in the classic three-component Goodwin core motif Tsai et al. Comparing the robustness of ten different oscillator models, Wolf et al. Kholodenko per and cry, that are transcribed, translated, and finally inhibit their own transcription  to be capable of sustained oscillations While this core negative feedback loop involving only per and cry transcription via the transcriptional activator BMAL1. This double negative interaction is one direct positive feedback loop in the scheme of the cross-activating motif CA-R. The degradation of the clock proteins  is an enzymatic process and thus is likely governed by MM kinetics (motif MM2). Another feature of the molecular clock is that PER and CRY enter the nucleus and repress transcription not as monomers, but as a heterodimer. In other words, there are two parallel Goodwin-like loops that are coupled via dimerization.The protein product of the gene cry transcription; in effect a positive feedback loop. By the symmetry of the system, a similar argument holds from the perspective of PER as well.Such a complex formation can be considered a positive feedback interaction as well. Looking from the perspective of CRY, increased CRY repressor reduces PER protein, which in turn up-regulates This complex formation also adds another step in the feedback loop, which also eases the cooperativity requirement. The PER:CRY complex also shuttles across the nuclear membrane. This adds another step to the loop, since the reversible step behaves like a cross-activating positive feedback loop (CA-I) as long as there is asymmetry between the forward and backward steps.As we have shown, the benefits of different positive feedback loops do add up to ease the cooperativity requirement. Here we have highlighted a few important positive feedback interactions in the mammalian circadian clock, but the state-of-the art network of interactions is very complex encompassing additional positive and negative feedback motifs.Quantitative modelers of the molecular circadian oscillator have long recognized the need for ‘modifications’ to the core Goodwin motif model Griffith From the analysis of a delayed negative feedback oscillator Thron 2+ oscillations. Significantly, they identify that positive feedback generated by product-activated enzyme along with MM kinetics in the substrate removal can produce oscillations without need for cooperativity. Although the models they consider for these two systems do not need cooperativity, they include other nonlinearities, such as multiplicative terms and competitive inhibition.Self-activation, or product activation as it is also known, is also encountered in several biological oscillator motifs. Goldbeter and Dupont The cross-activating motifs  have been shown to produce oscillations under the alternative name of amplified negative feedback in As is evident from our theoretical analysis and discussed earlier by Thron It is well known that nonlinearity is critical to generating oscillatory phenomena Classical models of biological pattern formation While we have focused here on understanding the role of positive feedback and suggesting its evolutionary purpose in easing oscillations by reducing the cooperativity requirements, our results can be also viewed in a prescriptive light. Given a model of an oscillatory biological phenomenon, unreasonably high cooperativity values within the model would suggest that one or several important positive feedback mechanisms have been overlooked in model construction. This might also direct further experimental work if evidence of such positive feedback is lacking.It has been also suggested that positive feedback provides robustness to the system We have elaborated on the qualitative role of positive feedback in reducing the nonlinearity necessary to produce oscillations using the simple Goodwin core motif. This three-component negative feedback is a common motif in several biochemical oscillators The original Goodwin oscillator represents a mechanism involving a three-component negative feedback loop:The Jacobian of this system has the structure:It is clear that the right hand side of (4) is minimized by having balanced degradation at the critical point, i.e., In the core motif, solving for the critical point turns (4) intoIt is evident from this expression that if degradation rates in the system are balanced, then The three self-activating motifs we consider are of the form:An inspection of (6) reveals that this system has the same Jacobian structure (3) as the core motif. Then, (4) turns intoh) than (4) due to (a) the multiplicative factor on the left hand side greater than 1 (b) an increase in the repressor levels at the critical point h. Despite these two favorable factors, the altered effective degradation rate at the critical point might increase the right hand side due to increased mismatch between the degradation rates. It appears from numerical simulations, that the former two factors outweigh the latter and make self-activation always favorable from the viewpoint of reducing cooperativity.The condition in (7) is more easily satisfied  and therefore we reach a similar criterion for oscillations:(9) is easily seen to be exactly the same as the criterion for oscillations in the self-activation motif (7) with only one key difference. With MM degradation the effective degradation rate of the component with the positive feedback is reduced much more than with self-activation. This results in parameter sets with significant increases in the right hand side of (9) due to mismatches in degradation. Thus, in the Monte-Carlo simulations presented in the Results section, the MM motif oscillates with a smaller cooperativity if the MM degradation does not significantly increase the mismatch between the effective degradation rates (thereby increasing the right hand side of (9)).The three CA motifs are implemented as:The CA motifs have a different Jacobian structure than the MM and SA motifs. The Jacobian for CA motifs has an additional non-zero element h), the mismatch costs can be reduced significantly more than the CA-A and CA-I motifs, where all parts of the added term are constants.While the criterion for oscillations with cross-acting feedback (12) is similar to the core motif (4), the added term arising from the feedback reduces the effect of the mismatches in degradation rates of the components. Alternatively, the added term can be viewed as reinforcing the loop gain of the system (represented by the left hand side). Thus, we expect the CA motifs to oscillate with smaller Hill coefficients. Moreover, the reduction of the right hand side by the feedback (12) is maximized when the degradation rates of the components involved in the feedback are the largest . However, the CA-R motif behaves differently, since the added term is also proportional to feedback repression metric We have shown that positive feedback promotes oscillations in the three-component negative feedback motif. In order to recognize such features in oscillator motifs, a systematic approach to identifying feedback loops is necessary. One common approach, that we adopt here, is to identify feedback loops using the system Jacobian that we computed above.gain of the A feedback loop is the causal effect of a component on itself often mediated by other system components. Given the Jacobian with elements In the core motif with Jacobian in (3), we find one three-component feedback from The SA and MM class of motifs have the same Jacobian structure as the core motif and hence have the same three-component negative feedback loop and three one-component degradation-related feedbacks. However, as shown in (7) and (9), the gain of one of the one-component negative feedback loops is reduced by the positive feedback (compare effective degradation rates between the two equations and (4)). In other words, the positive feedbacks in the MM and SA motifs shown in Figure S1Distribution of the amount of reduction in cooperativity with positive feedback. The data in (TIF)Click here for additional data file.Figure S2Relationship between the strength of positive feedback (measured by the parameter ) and required degree of cooperativity. As the cooperativity is increased the system starts oscillating after undergoing a Hopf bifurcation. Thus, the Hopf bifurcation lines shown represent the boundary between non-oscillatory and oscillatory regimes. Small cooperativity and weak positive feedback never lead to oscillations. For 50 different random choices of degradation rates of the three components, the boundary between the two regimes for each of the positive feedback motifs in (TIF)Click here for additional data file.


Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application. Though physicians have always considered the individual characteristics of each of their patients, the term personalized medicine appeared recently to account for our new abilities to characterize each person biologically with genomic analysis, and to use this information to guide medical decision-making and deliver the best treatment to each patient. This concept is also referred to as genomic medicine, and other terms such as stratified medicine or targeted medicine are sometimes used interchangeably. A few years ago, the concept of P4 medicine was introduced with the idea of managing the patient’s health instead of the patient’s disease . As a maThe term precision medicine (PM) is also frequently encountered in the literature to denote similar ideas, and generally refers to delivering the right drug at the right time to the right patient, by targeting specifically the molecular events that are responsible for the disease. We will use in this article the terminology PM defined as a customization of healthcare that takes into account individual differences among patients from prevention, diagnosis, prognosis, choice of the treatment and follow-up. PM combines the knowledge of the patient’s characteristics with traditional medical records and environmental information to optimize health. PM does not only rely on genomic medicine but also integrates any other relevant information such as non-genomic biological data, clinical data, environmental parameters and the patient’s lifestyle.BRCA and PARP genes, which is exploited by using PARP inhibitors for treating BRCA deficient breast cancer tumors. Both oncogene addiction and synthetic lethality are typical situations where targeted therapy should be the solution of choice.In a special issue, the Journal of Clinical Oncology has focused on PM in oncology  showing ERBB2 receptor. This gene is amplified in 15–20% of patients with breast adenocarcinoma. Treating patients with locally advanced disease with trastuzumab for a year decreases by 50% the risk of recurrence  with another MTA, imatinib, in patients with chronic myelogenous leukemia has dramatically improved their outcome (BRAF(V600E) mutation is frequently associated with melanoma, where it seems to play a critical role in the malignancy process and can be effectively treated using vemurafenib (BRAF(V600E) mutation has been also identified in multiple forms of advanced cancers such as colorectal or thyroid cancer  such as tyrosine-kinase inhibitors have promoted the development of PM in oncology. MTAs have proven their efficacy in some cancer subtypes and they provide new opportunities to treat the disease  needs to be collected along a complex healthcare pathway. As the disease evolves, new experiments such as high-throughput screens (with microarray or NGS technologies for example) or biomarkers detection by immunohistochemistry (IHC) have to be performed to measure relevant biological information required to choose the best therapy. During the process, physicians , biologists, pharmacists, bioinformaticians, computational biologists, biostatisticians, informaticians, biobank managers, biotechnological platform managers, clinical research associates, and the technical staff will offer their expertise for the benefit of the patient. Different actors and cultures and a variety of miscellaneous constraints, including meeting the deadlines for results delivery, render the application of PM in daily clinical practice extremely challenging. Organizational aspects are therefore essential for the success of PM  the technical level to develop a powerful computational architecture (software / hardware), (ii) the organizational and management levels to define the procedures to collect data with highest confidence, quality and traceability, and (iii) the scientific level to create sophisticated mathematical models to predict the evolution of the disease and risks to the patient. Obviously, an efficient informatics and bioinformatics architecture is definitely needed to support PM in order to record, manage and analyze all the information collected. The architecture must also permit the query and the easy retrieval of any data that might be useful for therapeutic decision in real-time thus allowing clinicians to propose the tailored therapy to the patient in the shortest delay. Therefore, bioinformatics is among the most important bottlenecks towards the routine application of PM and several challenges need to be faced to make it a reality  in oncology can be formulated as follows: a large  reality . First, If many recent publications point out the key role of the bioinformatics for PM today see , G-DOC  or the cIn the next section, we will focus on the bioinformatics solutions implemented in order to tackle these challenges in the Institut Curie Bioinformatics platform in the context of the SHIVA clinical trial  initiate1  the MAP kinase pathway. They include predictive biomarkers of efficacy of the MTAs as well as known biomarkers of resistance . These predictive biomarkers had either been validated in the clinic  for the detection of somatic mutations. IHC is used for the assessment of hormone receptor status, including estrogen, progesterone and androgen receptors, as well as for the validation of focal gene amplifications detected with Cytoscan HD in the following genes: ALK, BRAF, EGFR, ERBB2, KIT, MET, PDGFRA, PDGFRB and PTEN. Only samples which contain more than 30% of tumor cells are processed to control at best sample heterogeneity. Patients from seven hospitals in France can be included in the study. The establishment of the molecular profiles follows the process and the timelines described in Figure 2B. All the bioinformatics steps including data management and integration, molecular profile analyses and data coherence checking, are centralized at the Institut Curie bioinformatics platform. This centralization permits the analysis of the molecular data from the different hospitals using the same parameters therefore ensuring the reproducibility of results. The whole process was set up in real-time in order to have less than four weeks elapsed between the biopsy and the randomization, including 4 days for the bioinformatics treatment . Thus, this trial represents a concrete application of PM. It highlights the real challenges and difficulties about the feasibility of such project in real-time. A committee of expert named the Molecular Biology Board (MBB) has been appointed. It consists of biologists, bioinformaticians and medical oncologists of each hospital. The MBB meets each week to decide what the best therapy is for each patient. Based on its scientific expertise and a literature review, the MBB has defined a set of rules taking into account the relevant molecular abnormalities identified in the tumor to decide which MTAs to choose (among a list of 11 drugs) to treat the patient. MTAs allowed in trial are only drugs that are approved for clinical use in France. In the next section, we will describe the bioinformatics solutions we have developed at Institut Curie to manage the data workflow for the SHIVA clinical trial.SHIVA is a randomized proof-of-concept phase II trial comparing molecularly targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancerFigure 3. The KDI system ensures information sharing, cross-software interoperability, automatic data extraction, and secure data transfer. In the context of the SHIVA clinical trial, high-throughput and IHC data are sent by the different biotechnological platforms to the bioinformatics platform using standardized procedures for transfer and synchronization. Data are then integrated into the KDI system within ad-hoc repositories and databases. Metadata describing the data are stored in the KDI core database such as the patient identifier, the type of data  and the technology used . Each type of data is then processed by dedicated bioinformatics pipelines in order to extract the relevant biological information such as the list of mutations and the list of amplifications/deletions. Therefore, the KDI core database acts as a hub allowing referencing all data through the use of web services. The KDI core database knows exhaustively which data is available for a given patient and where the raw and processed data are physically stored. It thus offers the possibility for clinicians to make queries through a web application and to extract the list of available information for a given patient. In addition, the system is also used to manage and perform automatic integrative analysis required for the therapeutic decision.Precision medicine relies on a tight connection between many different stakeholders. As the choice of the therapy is based on a combination of different information levels including clinical data, high-throughput profiles  and IHC data, all this information related to a given patient needs to be gathered in a seamless information system. Data integration is definitely required and bioinformatics plays a central role in setting up this infrastructure. To tackle this challenge, we have developed a seamless information system named KDI (Knowledge and Data Integration) described in Figure 3B).From a technical point of view, the KDI system consists of different modules dedicated to the storage, processing, analysis and visualization of each type of data . High modularity associated with an efficient interoperability makes our system able to retrieve any relevant information. To facilitate the developments of these modules, we have retained a classical n-tiers architecture implemented with the JAVA/J2EE language. The core of each module of the KDI system can be presented as the association of different layers  pattern. This conceptual modeling provides a data model plasticity required to handle the heterogeneity and the scalability of the variables of interest. Therefore, with EAV modeling, same concepts managed by different projects (with specific requirements by project) can be stored in a unique database without any modification of the data model. MySQL has been chosen as database provider for all web applications of the system. Complementary solutions such as NoSQL databases are currently evaluated for particular requirements .Data access is supported by the DAO (Data Access Object) pattern. By using HibernateDaoSupport superclass provided by Spring Framework, we promote the standardization of database access for all standard queries . Moreover, Hibernate mapping through JPA annotations associated with use of Hibernate Criteria provides a homogeneous frame for this critical layer. Database sessions and transactional aspects are also delegated to Spring Framework.Business core of our web applications has two main objectives: (i) provide structured data for presentation layer, and (ii) make data available for remote and secured access by other applications and technical users. Standard services are developed using core functionalities of Spring framework . Web services are published (server side) and invoked (client side) through Apache CXF framework. To respect Web Services Security (WS-Security) standards, we use the Apache WSS4J project provided by CXF (with interceptors chain process) to set up a username token authentication on each web application in the system.Presentation layer is based on JSF (Java Server Faces) which is a component oriented framework for building user interfaces for web applications. To enrich the basic component set provided by JSF, we use additional component libraries such as Apache Trinidad and Primefaces. By this systematic approach for each user interface, we aim to build a visual identity, ergonomic, easily usable, for the whole information system. All data available within KDI can be browsed and retrieved from a user-friendly bioinformatics web portal.This layer represents the web browser through which end-users access KDI system.Figure 4A. Raw data are normalized with the Affymetrix Power Tools software package2. Then, the log R ratio is segmented in order to detect breakpoints and assign copy number status using Colibri . The best mapping score for each read is used to detect misalignment. The standalone package of the Torrent Variant Caller (v2.2 Life Technologies) is then used to call variants (SNVs and indels) from the mapped reads. In the context of clinical trial, variants have to be filtered to promote a high specificity, in order to avoid any false positive mutations. Thus, detected variants are filtered according to their frequency (≥4% for SNVs and 5% for indels), strand ratio (≥0.2), and reads coverage (≥30X for SNVs and 100X for indels). In addition, SNVs and mainly insertions and deletions detected in the context of a repeated region or a homopolymer are double checked. Homopolymer and repeated regions are prone to contain recurrent false positive, because of the limitation of the Ion TorrentTM PGM technology. In most cases, the variant is discarded if also detected in other patients from the same sequencing run. Otherwise, variations specific to a sample, even within a repeat context, are reported. To facilitate the interpretation of individual patient data for clinical trials, the filtered list of variants is then annotated using the ANNOVAR software . This first section provides the MBB with a rapid overview of all detected alterations. If needed, the MBB can also have access to more detailed results, with graphical views of the copy number profiles for each gene, as well as the list of mutations with detailed annotation as previously described . This name-blinded technical report is sent to the members of the MBB for scientific validation and prioritization of the identified molecular abnormalities.The last step of the bioinformatics workflow is the production a technical report for the MBB. This task is crucial and must be complete and precise on one hand, and summarized on the other to allow a quick decision of the board. To answer this need, a report is generated for each patient. This report first presents the clinical information of the patient and the overall molecular profiles per gene, with the DNA copy number alterations, LOH status, and number of mutations . This step is performed weekly and updates the system by creating the patients recently included in the trial into the KDI core database. At the same time, an anonymous identifier is generated by the system for each new patient. Conversion between the different patient identifiers is guaranteed by the KDI core database and is accessible through the Bioinfo-Portal web application. Once available, the raw data generated by the biotechnological platforms are transferred to the bioinformatics platform for analysis (using rsync system). Bioinformatics pipelines (mutation and DNA copy number pipelines) process each molecular profile and are responsible for raw data storage and traceability within the KDI core database. The summarized results are structured in the BIRD  application. The last step of the data integration workflow is the generation of the bioinformatics reports. Two reports are required in the context of the SHIVA clinical trial at two different time points. A first report is generated by the system after the processing of the DNA copy number profile in order to request an IHC validation if needed. A second report is generated by the system and sent to the MBB for the final therapeutic decision. All reports, data and analysis results for each patient are gathered within the KDI modules . All the information are available under controlled access for any member of the project through the KDI Bioinfo-Portal. In order to supervise the patients’ process at each step of the whole bioinformatics workflow, an additional module of the system named the Bioinfo-Board application  has been developed. This web application aims to controlling, monitoring and checking the evolution and status of each SHIVA patient in real-time.In the context of the SHIVA trial, the clinical data needed for the MBB are first imported in a dedicated module of the KDI system, named Offering a high quality service is most required in the context of a clinical application. The availability of all KDI’s component and the reproducibility of the analyses is thus mandatory. To this aim, we have promoted a set of good practices for the software building process. First, the software development phase follows a strict frame with positive technical constraints, and a common methodology known and shared by each data manager and software developer. The configuration management is delegated to a SVN repository where all the source codes of KDI system are regularly committed. The unit testing is strongly recommended for all programming languages involved in the system (X-Unit) and part of our continuous integration server based on Jenkins software. This system allows to check weekly that all the tests parameterized for all applications are successfully passed. This control ensures that the analysis pipelines provide the expected results, identical to a reference analysis which is considered as a gold standard. Second, we pay attention to the availability of KDI system. All web applications are monitored with Nagios software in order to be able to detect in real-time any disorder on the system and therefore take immediately all necessary actions  to restore initial and nominal state if needed.In order to reach this high quality service expectations, three different informatics environments  have been set up. An update on any environment is always linked with a SVN revision. The first environment is the development (D-env) which is the place of the version currently in development. Each developer, after doing unit testing on his local workspace is allowed to install a new version of his components on this environment. It results that the D-env can be temporarily unstable and this is assumed. The second environment is the validation (V-env) which must be stable at every time. Integration testing is performed on this environment to validate the candidate release of the KDI system. The V-env can be seen as a pre-production environment. Only the persons in charge of the final installation are allowed to update the V-env. The third environment is the production (P-env) which is the instance of KDI system really used by the end-users. The updates of the P-env have to be planned, secured, and widely announced to avoid any inconvenience. During delivery periods, the environments of validation and production must be identical. These three environments permit to secure our delivery process with a high reliability and traceability.The presented bioinformatics environment is in use since October 2012 to manage and analyze the molecular profiles of the patients included in the SHIVA trial.Results of the feasibility part of the project, focused on the first 100 patients were recently published . Among tThe solution we have developed to manage the SHIVA clinical trial provides a first step towards the routine application for PM. However, many challenges still need to be tackled and will require a lot of mutualization and harmonization efforts within the scientific community. The main on-going challenges are listed in what follows.TM PGM with AmpliSeqTM) has been used and can be processed with relatively moderate computing resources. However, as sequencing cost keeps on decreasing, whole-exome or even whole-genome might be used soon thus requiring HPC infrastructure. According to Moore’s law, Kryder’s law and Butter’s law, costs are halved every 18, 12, and 9 months for processor, storage and data transfer, respectively (3). Thus, the difference between biotechnological and informatics capacities grows exponentially. Entering the era of big data in cancer research implies a breakthrough at the informatics level. First, the scalability of the infrastrure (Input/Ouput performance and computing power) is required to allow the management and analysis of ever-growing data. Second, bioinformaticians must be trained to the use of low-level programming languages for parallel computing such as Message Passing Interface (MPI), Open MultiProcessing (OpenMP), or MapReduce  infrastructure for data storage, transfer, computation and access control. So far, mainly targeted sequencing on a limited panel of genes  are necessary to cover different entities of interest such as the gene (Gene Ontology), the disease (Disease Ontology) and the sequence (Sequence Ontology). Semantic Web standards promoted by World Wide Web Consortium (W3C) make it possible to link knowledge and data together so they can be queried and retrieved. To this aim, the Resource Description Framework (RDF) data format along with SPARQL query language provide the technical framework to describe, share, interact and query semantic data. While the technical solutions exist to support data exchange and linking, the definition of ontology, their choice and their use in practice for healthcare and biomedical data is still an issue. In order to tackle these challenges and to promote the use of standards and ontologies in the biomedical field, many European initiatives supported by the European Community (FP6 and FP7 programs) are involved in the definition and harmonization of standards:The large heterogeneity of the data that are collected along the healthcare pathway hampered their exchange and their comparison. Therefore, it is crucial to describe all the data that are generated with controlled vocabularies also called ontologies. Ontologies offer a formal representation of knowledge with definition of the relevant semantic attributes, their hierarchy and their relationship using a well-defined logic. Importantly, not only one single ontology can pretend to describe all the knowledge in a field but different ontologies  models to accelerate PM for the benefit of the patient.Moreover, the European effort BioMedBridges supported by the European Strategy Forum on Research Infrastructures (ESFRI) aim to construct the data and service bridges needed to connect emerging biomedical sciences research infrastructures. Among the infrastructures concerned let us mention:1. the European Infrastructure for translational medicine: EATRIS supports the development of biomedical discoveries for novel preventive, diagnostic or therapeutic products up to clinical proof of concept.2. the Biobanking and Biomolecular Resources Research Infrastructure: BBMRI will form an interface between biological specimens and data and top-level biological and medical research.3. the European Clinical Research Infrastructures Network: ECRIN supports multinational clinical research projects in Europe.4. ELIXIR:  mutation while colon cancer patients harboring the same BRAF(V600E) mutation have a very limited response to this drug. They found that BRAF normally exerts a negative feedback regulation of EGFR. Therefore BRAF inhibition causes a rapid feedback activation of EGFR, which enhances cell proliferation. As melanoma cells express low levels of EGFR they are not subject to this feedback activation in contrast to colon cancer. Thus, they propose that these patients might benefit from combined therapy consisting of BRAF and EGFR inhibitors. This example highlights the fact that considering interactions between biomarkers and combining different therapies together can dramatically strengthen the efficiency of PM. Also it clearly shows that elucidating the reasons behind treatment escape and proposing backup therapeutic strategies would benefit greatly from the knowledge and modeling of the cell regulatory network rewiring. Therefore, computational systems biology approaches, based on mathematical models of the cell regulatory network rewiring, are definitely needed to deepen our understanding of the cancer cell and to improve current decision rules. Systems biology and systems medicine are two disciplines which open the road to PM. Machine learning techniques will also be very useful to develop prediction rules to predict outcome and response to treatment. We can imagine that online machine learning techniques could be used to refine and optimize decision rules as long as new data and knowledge are generated. The key defining characteristic of online learning is that soon after the prediction is made, the true label of the instance is discovered. This information can then be used to refine the prediction hypothesis used by the algorithm. In the case of cancer, every day, for several patients, information is collected: survival, response to therapy, molecular profiles, pathological complete response, etc. This information could be used to retrain the classifier on the available data. In addition to these data-driven approaches, knowledge-based approaches must be developed to capitalize on the large amount of knowledge that is present in the scientific and medical literature to build efficient decision rules. IBM has developed a supercomputer named Watson (the name of IBM’s founder) able to understand question in natural language and to extract relevant information from the literature. Watson supercomputer is currently used at the Memorial Sloan-Kettering  to help for diagnosis in lung cancer.Clinical trials for PM rely so far on a very limited number of biomarkers used for the therapeutic decision  and also pathway databases for gene regulatory network, signaling pathway, metabolic pathway, Protein-Protein Interaction network and protein-compound network . The TCGA has initiated a pan-cancer analysis project  and non-cancer applications (MAARS - Microbes in Allergy and Autoimmunity Related to the Skin). This demonstrates the potentiality and flexibility of our system to support PM covering all its requirements ranging from data management, data traceability, data analysis, query, and visualization.The evolution of sequencing technologies has expanded the frontiers of genomics in both biology and clinical environments. The sequencing field will continue to evolve rapidly, offering lower costs and increased speeds. On-going developments in the sequencing technology, such as an ultrafast sequencer like nanopore technology, will improve performance and miniaturization, thus offering new tools to improve prevention, diagnosis, prognosis, choice of the treatment and follow-up for patients in oncology. To promote PM in daily clinical routine, flexible bioinformatics systems like KDI are definitely required for enabling efficient sharing of information in real-time, and rapid data processing needed for therapeutic decisions. KDI also provides the infrastructure for developing and integrating into the clinical decision process new integrative analysis methods with sophisticated mathematical models, representing the multidimensional nature of cancer to propose new biomarkers and to develop new therapies to fight cancer.TM PGM. Pierre Gestraud, Philippe Hupé, Georges Lucotte, and Tatiana Popova developed the DNA copy number pipeline. Pierre Gestraud and Fanny Coffin developed the bioinformatics report for the molecular biology board. Philippe Hupé, Gérôme Jules-Clément, Florent Yvon, Patrick Poullet, Stéphane Liva, Alban Lermine, Stéphane Liva, Stuart Pook, Georges Lucotte, Philippe La Rosa, Camille Barette, and Julien Roméjon developed the seamless information system KDI. Camille Barette, François Prud’homme, Jean-Gabriel Dick managed the informatics infrastructure. Christophe Le Tourneau is heading the Phase I Program as well as the Head and Neck Clinic at the Institut Curie . Christophe Le Tourneau is the principal investigator of the SHIVA randomized personalized medicine trial. Maud Kamal is the scientific coordinator of the SHIVA trial. Nicolas Servant, Philippe Hupé, Emmanuel Barillot, Pierre Gestraud, Maud Kamal, Christophe Le Tourneau and Julien Roméjon wrote the article.Nicolas Servant and Philippe Hupé coordinated the bioinformatics developments to support the SHIVA clinical trial. Philippe Hupé and Emmanuel Barillot coordinated the development for the seamless information system. Julien Roméjon, Philippe La Rosa, Georges Lucotte, Stéphane Liva, Alban Lermine, Virginie Bernard, Nicolas Servant managed the data and developed the bioinformatics pipelines for the SHIVA clinical trial. Virginie Bernard and Bruno Zeitouni developed the mutation pipeline for Ion TorrentThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.