This article reviews the questions regarding the image evaluation of angiogeneic histological samples, particularly the ovarian epithelial cancer. Review is focused on the principles of image analysis in the field of histology and pathology. The definition, classification, pathogenesis and angiogenesis regulation in the ovaries are also briefly discussed. It is hoped that the complex image analysis together with the patient’s clinical parameters will allow an acquiring of a clear pathogenic picture of the disease, extension of the differential diagnosis and become a useful tool for the evaluation of drug effects. The challenge of the assessment of angiogenesis activity is the heterogeneity of several objects: parameters derived from patient’s anamnesis as well as of pathology samples. The other unresolved problems are the subjectivity of the region of interest selection and performance of the whole slide scanning.Angiogenesis; Image processing; Microvessel density; Cancer; Pathology The aim of this paper is to provide the reader with a brief understandable description of angiogenesis research facts and concepts, especially the histology sample image analysis strategies and its problems.Angiogenesis, the development of the new capillaries from the already existing vessels, is an important process of the normal physiologic activities such as the cyclic ovarian function, wound healing and embryonic growth. Another definition: Angiogenesisis is the stimulation of the new endothelial cell growth and the new blood vessel development . AngiogeOvarian cancer is one of the major causes of female oncologic death worldwide . It causAll ovarian tumors are divided into the following categories : (1) SurCurrent concepts regarding the origins and molecular pathology of ovarian cancer suggest that the dysfunction of K-ras, b-raf, BRCA1, p53 genes and several others often occur in these patients. Among the predisposing diseases are endometriosis, initially benign cysts and cystadenomas .By establishing a correlation between angiogenesis and cancer development and progression we can arrive at a more complete understanding regarding angiogenesis, cancer and ultimately individual recurrence and survival .Increased vascularity and angiogenesis occur in support of actively proliferating tumor cells and thus blood vessel parameters may have a potential application as diagnostic and prognostic indicators .That is our opinion that angiogenesis activity is especially dynamic and unstable in the ovaries and it is probably almost impossible to estimate all the microenvironment effects influencing the angiogenesis expansion in any organ or tissue system. What we can see on the histological image are only the tubular structures (blood and lymphatic capillaries) and the surrounding cells within a connective tissue matrix .Angiogenesis is a key aspect of normal cyclical ovarian function. Follicular growth and the development of the corpus luteum (CL) are dependent on the proliferation of new capillary vessels. The process of selection of a dominant follicle in monovular species has been also associated with angiogenesis, as there is evidence that selected follicles possess more elaborate microvascular networks than other follicles. After blood vessel growth, the blood vessels regress, suggesting the coordinated action of inducers as well as inhibitors of angiogenesis in the course of the ovarian cycle .Apparently, angiogenesis can be investigated on the level of the entire organism, as on the levels of an organ system, a separate organ, a tissue and cells. Angiogenesis on the histology sample section is assessed by the eye of the trained pathologist , using the graticule, via Chalkey stereologic method and applying the image processing software.Two main types of angiogenesis have been described: sprouting, the expansive growth of the vascular network; and remodeling, the rebuild of the vessel net. It is considered that the first type predominates in malignant tumor growth. However, Fox and colleagues (2007) do not include the remodeling in classification of the angiogenesis. They have suggested the classification of tumor neovascularization with 5 types. (1) Angiogenesis: the generation of new blood vessels from the existing vasculature; (2) Vasculogenesis, the de novo generation of blood vessels from endothelial cell progenitors, as occurs in the embryo; (3) Vascular remodeling: intussusceptive vascular growth, referring to vascular network formation by insertion of interstitial tissue columns into the vascular lumen and subsequent growth of these columns resulting in partitioning of the vessel lumen, endothelial cell division is not required for this form of vascular remodeling ; (4) GloVEGF/VPF (now termed VEGF-A) was first identified in 1993 by Senger and colleagues in ascites fluid of tumors in rodents. Years later, VEGF was found in the malignant effusions of several human tumors. VEGF is a heparin binding glycoprotein that occurs in at least four molecular isoforms as the result of alternative VEGF mRNA splicing [The VEGF family is an increasingly important set of factors in ovarian cancer. VEGF has been established experimentally as one of the major inducers of ascites and its expression can be at least in part a consequence of a positive feedback loop where the ovarian cancer cells produce LPA, which in turn induces VEGF, causing ascites with high circulating concentrations of LPA. Several studies demonstrated the importance of other members of the VEGF family, including VEGF-C and VEGF-D, which induce de novo formation of lymphatic capillaries (lymphangiogenesis). These factors have also been shown to promote metastasis via the lymphatics. Further, the short isoform of the alternatively spliced VEGF receptor-3, the receptor tyrosine kinase receptor for VEGFs-C and -D, is related to development of lymph node metastasis in breast cancer. VEGF-C is induced by hypoxia, making it also likely to be involved in the development of ovarian cancer or the ascites of ovarian cancer. The role of the angiopoietins and the other VEGF family members has not yet been examined in detail in ovarian cancer .VEGF receptors family is not the only molecules expressed differently in tumor and normal endothelial cells, and angiogenesis is not the only way of the cancer expansion, however its predominant pathogenic role is clearly established. The mentioned molecular group includes: (1) VEGF receptors; (2) Integrins ; (3) Adhesion molecules ; (4) Glycoproteins (Prostate-specific antigen); (5) Proteases.Three main approaches to targeting angiogenesis in the treatment of ovarian cancer have been described. The first has been to target VEGF itself, the second to block the VEGF binding site on its cell surface receptors, and the third to inhibit tyrosine kinase activation and downstream signaling with small molecules at the intracellular level .The extensive description of ovarian cancer pathology and treatment is beyond the scope of the article.The most commonly used antibodies to highlight tumor blood vessels are those against Factor VIII related antigen, CD31/PECAM-1, and CD34. Factor VIII related antigen is a part of the von Willebrand factor complex and plays a role in the coagulation cascade. The platelet endothelial cell adhesion molecule CD31/PECAM-1 is a transmembrane glycoprotein involved in cell adhesion. CD34 is a surface glycoprotein of unknown function ,38,39, .The process of angiogenesis is regulated by the cytokines, growth factors, the interaction between the endothelial cells and components of the extracellular matrix and the surrounding cells, including macrophages, smooth muscle cells, fibroblasts. The initiation, further development and fading of the angiogenesis activity are dependent on the balance of the angiogenic and antiangiogenic factors in the endothelial cell microenvironment. Angiogenesis can be represented as the consequence of stages. For the malignant tumor it is a process starting from the single dormant endothelial cell which can grow to the magnificent vascular network providing the multi faceted homeostasis. The critical moment of the latent endothelial cell transformation to an active dividing cell is called the angiogenic switch. Ovarian function is dependent on the establishment and continual remodeling of a complex vascular system. This enables the follicle and/or CL to receive the required supply of nutrients, oxygen and hormonal support as well as facilitating the release of steroids. Moreover, the inhibition of angiogenesis results in the attenuation of follicular growth, disruption of ovulation and drastic effects on the development and function of the CL. It appears that the production and action of vascular endothelial growth factor A (VEGFA) is necessary to all these stages of development. The extensive vascularization of the CL enables it to receive one of the highest blood flows per unit tissue mass. Luteal blood flow remains at pre-ovulation levels in the collapsed follicle, but thereafter gradually increases in parallel with increases in luteal volume and coincides with increases in progesterone. The controlled, physiological angiogenesis that accompanies folliculogenesis, ovulation and luteal development requires the coordinated activity of multiple cell types and different angiogenic factors. It appears that VEGFA regulates angiogenesis by stimulating endothelial proliferation, migration and survival and is required at all stages from a secondary follicle right through to the mature CL. However, the often overlooked FGF2 plays a more dynamic role and is likely to be critical during the follicle- luteal transition .The same factors that control angiogenesis in other organs also control angiogenesis in the endometrium. It is believed that VEGF by interacting with its receptor VEGFR-1 and VEGFR-2 plays a major role in controlling endometrial angiogenesis. However, there is no clear cyclic pattern of VEGF or VEGF receptor expression as it for example occurs in the ovary. Many of the other angiogenesis-regulating cytokines have also been detected in the endometrium including the positive angiogenesis regulators bFGF, TGF-β, TNF-α, and IL-8 and the negative regulator thrombospondin .Interestingly, the three investigated gene polymorphisms did not correlate with any of the investigated clinico-pathological parameters. In univariate and multivariate models, only FIGO stage and patient's age at diagnosis, but not any polymorphism or any haplotype, were correlated with patients' overall survival. In this large multi-center study, the investigated VEGF gene polymorphisms were not associated with prognosis in patients with ovarian cancer .In the normal cycling ovary of the mare the most intensive expression of the angiogenic factors VEGF A, VEGF B, Ang1, Ang2 and their receptors VEGF-R1, VEGF-R2 and Tie2 can be detected in granulosa, lutein and theca interna cells as well as in thecal vessels during the periovulatory period. In this context, especially VEGF A, Ang2, VEGF-R2 and Tie2 seem to be important for angiogenesis during follicular and luteal development, while Ang1 serves for vessel stabilization. VEGF B and VEGF-R1 are probably only of secondary importance. In contrast, during luteal regression and follicular atresia, the ?ndings in luteal regression and follicular atresia show that, in the absence of VEGF A, factor Ang2 and its receptor Tie2 contribute substantially to vessel regression .This includes: (1) existing vessel endothelial cell basal membrane degradation and fragmentation influenced by the action of metalloproteases; (2) endothelial cell migration to the stromal tissue and extracellular matrix proteolytic degradation; (3) endothelial cell proliferation, new capillary tube formation, fusion of the formed vessels and its network development; (4) endothelial cell proliferation and migration suppression and fate under the influence of the angiogenic factors .Computers help health care professionals make robust decisions in various spheres of medicine, and in the realm of pathology image processing the opportunities of the software packages are of a special value. A perfect program should do anything what a human can and at the same time requires a minimum user intervention and conditions and performs as fast as possible. Unfortunately, not all the phenomena we can observe and investigate could be transformed in digits and this is the dilemma of the qualitative and quantitative analysis in research. The limitation of the image analysis software designed for the processing of angiogenesis samples is the problem of estimating the hypervascular areas which could be relatively easily assessed by an experienced pathologist. Computer can tell the scientist (this is the-output) what is the area of pixels had ascribed to the structures defined by the programmer who had put the computer language code . More complex features of an image such as the fractals could also be calculated by software. The obtained image features can be correlated with a spectrum of qualitative and quantitative parameters  and the diagnosis parameters are derived eventually. The fact that only few conducted studies have a weak statistic power, the meta-analysis of similar trials can serve as the reliable source of knowledge to make decisions about the right treatment and follow-up for patients.Good software can calculate the relationship between the target objects on the screen and the areas occupying the recognized structures. Pathologists can not estimate and compute the exact numerical values looking at the image, but they can choose the region of interest for the further computations. The ideal software developed to evaluate angiogenesis should choose the necessary regions of interest on the image slide, to recognize correctly all available structures, but these are the unresolved questions of modern medical image analysis software engineering.The first method of the quantitative evaluation of angiogenesis has been proposed and developed by Brem, the target of his research were the brain tumors .At the beginning of research activities, the measure of the angiogenesis process activity was the vessel area, stained by a coloring reactive or via the immunochemical method. The search for the new reliable biomarkers of angiogenesis is ongoing, and along with this branch of investigation the new approaches to the angiogenesis image have been suggested. Since Weidner and colleagues estimated the microvessel density in the most vascularized area (hot spot) in their pioneering work in 1991, the same technique with slight modifications has been used widely to assess the prognostic value of angiogenesis in various types of carcinomas. Afterwards, the Chalkey method based on Chalkey eyepiece graticule was introduced to provide a quicker and more objective procedure for measuring tumor vascularity. Currently, the Chalkey assay with CD34 immunostaining has been suggested as a standard method for angiogenesis quantification in solid tumor sections in an international consensus report, although the basis for the consensus has been questioned by others.2 per field using x20 objective lens and x10 ocular). Any stained endothelial cell or clusters separate from adjacent vessels are calculated as a single microvessel, even in the absence of vessel lumen. Each single count is expressed as the highest number of microvessels identified at the hot spot. Some authors use Chalkey count or computerized image analysis systems, both aimed to minimize the subjectivity in the quantification of MVD. The Chalkey count consists of applying a 25-point eyepiece graticule on several hot spots . The graticule is oriented to allow the maximum number of points to hit on or within the areas of stained microvessel profiles  [Microvessel density assessment is the most commonly used technique to quantify intratumoral angiogenesis. It uses panendothelial immunohistochemical staining of blood microvessels, mainly with Factor VIII antigen (F. VIII Ag or von Willebrands factor), PECAM/CD31, or CD34; rarely with integrin v3, CD105, or type IV collagen. The first step in Weidners approach is the identification by light microscopy of the area of highest neovessel density, the so called "hot spot", by scanning the whole tumoral section at low power. Then, individual microvessels are counted at a higher power (x200 field) in an adequate area  . Quantit196 mm2) .Steps of automated cancer diagnosis include: (1) Preprocessing (noise elimination and segmentation); 2) Feature extraction (feature types determination and selection); (3) Diagnosis  [ Feature In order to obtain the data on microvessel features, digital slide undergoes several stages of the image processing procedures: (1) Color deconvolution; (2) Light and dark staining thresholding; (3) Connecting endothelial cells in regions; (4) Completing vessels procedure; (5) Microvessel analysis .The features of the histopathology sample image are classified according to their nature into the following groups: (1) morphological ; (2) texture ; (3) fractal ; (4) topological ; (5) intensity based (color brightness) .The morphological approach quantifies the size and shape of a cell or its nucleus. The textural approach makes use of spatial inter-relationships for the pixels to extract features and quantifies properties such as the smoothness, regularity, and coarseness of the image. The intensity based approach employs the distribution of the intensity values of pixels to define its features. The topological approach quantifies the spatial distribution of the cells within a tissue. Although these approaches lead to promising results in automated cancer diagnosis, they suffer from one or both of the two problems: (1) the difficulty of determining the exact locations of cells/nuclei in the biopsy image or (2) the noise that arises from its staining process. The cell-graph approach relies on cluster formation in cancerous cells to define their distinctive features. In this method, we identify the cell clusters on a tissue image as the nodes and compute the spatial dependency between every pair of these nodes to probabilistically assign the edges. Unlike the previous demonstrations, the cell-graph approach makes use of the cell clusters instead of the individual cells. Therefore, it eliminates the necessity of determining the exact locations of cells/nuclei on a tissue image, which allows using the low-magnification images. Furthermore, this approach relies on the dependency between the cell clusters rather than the pixels themselves and does not directly use the pixel values in feature extraction. Because of that, it is likely immune to noise inherit in a biopsy image .fractal  applies to objects in space or fluctuations in time that possess a form of self-similarity and cannot be described within a single absolute scale of measurement. Fractals are recurrently irregular in space or time, with themes repeated like the layers of an onion at different levels or scales. Fragments of a fractal object or sequence are exact or statistical copies of the whole and can be made to match the whole by shifting and stretching. Sequential fractal scaling relationships are observed in many physiological processes. Spatial structures of many living systems are fractal. Fractal geometry has evoked a fundamentally new view of how both nonliving and living systems result from the coalescence of spontaneous self-similar fluctuations over many orders of time and how systems are organized into complex recursively nested patterns over multiple levels of space. The fractal models may be used for image segmentation, texture classification, shape from-texture, and the estimation of 3-D roughness from image data. Related algorithms and suitable procedures are already implemented in some image processing software. If the parameters gained by the analysis are taken to supply classification problems where textural information is to be processed, the structures under consideration do not necessarily have to be fractals. In cellular morphometry cells and nuclei can be quantitatively described by measuring their fractal dimension [The term imension .Fractal dimension is a measure of how complicated a self-similar figure is. In a rough sense, it measures how many points lie in a given set. Somehow, though, fractal dimension captures the notion of ‘how large a set is’. Fractal object has a property that more fine structure is revealed as the object is magnified, similarly like morphological complexity means that more fine structure (increased resolution and detail) is revealed with increasing magnification. Fractal dimension measures the rate of addition of structural detail with increasing magnification, scale or resolution. The fractal dimension, therefore, serves as a quantifier of complexity. Fractals that have the same fractal dimension may look very different, they have different ‘texture’, more specifically, different lacunarity. Lacunarity is a counterpart to the fractal dimension that describes the texture of a fractal. It is strongly related with the size distribution of the holes on the fractal and with its deviation from translational invariance. Roughly speaking, if a fractal has large gaps or holes it has high lacunarity; on the other hand, if a fractal is almost translationally invariant it has low lacunarity. Lacunarity  measures structural variation or inhomogenities that may be manifested by texture. In a restrictive sense it is a measure of the lack of rotational or translational invariance. In a more general sense, lacunarity is a measure of non uniformity (heterogeneity) of structure or the degree of structural variance within an object. Lacunarity is usually defined in terms of mass related distribution .Syntactic structure analysis (SSA), a method often used for quantification of tissue architecture . This approach uses a binary representation of the centers of gravity of individual vessels to construct graphs or diagrams of which the characteristics are applied as contextual parameters. Examples of such figures are the Voronoi diagram, the Gabriels graph and the minimum spanning tree, which may give insight into the division of the blood supply inside the tumor and the immediate and distant vascular neighborhood relationships, respectively .The fractal and syntactic analysis recently showed its reliability for the purposes of the research field of angiogenesis micropathology. Several authors have described a broad spectrum of descriptors for this relatively novel method . These dEntropy, correlation and image contrast are an image characteristics derived via the texture analysis. The color analysis, pixel color comparison, histology image texture analysis all have been applied in attempts to find an appropriate robust and reliable approach to the assessment of angiogenesis net ,59.Color is an important factor in defining structures in biological science. The fields of histology and pathology are founded on the use of special dyes and staining procedures that label cells or structures of interest with a defining color. Examples include the use of special stains for specific biochemical groups, e.g., trichrome stain for matrix and cell tissue, and the use of chromogenic substrates in enzyme linked-immunochemistry, e.g., immunoperoxidase labeling, and reporter enzyme constructs in transfected cells, e.g., b-galactosidase-transfected cells. Hence color image analysis (CIA), in which colored objects of interest are isolated or segmented from surrounding structures for subsequent measurement, is becoming an increasingly important tool in modern pathology and cell biology .The methodology for MVD practical assessment is rather simple. It is similar to the original Weidner’s approach described above. The tumor is scanned at low power (x40-100) (center), and the three areas that contain the highest number of discrete microvessels are selected. The three hot spot areas containing the maximum number of discrete microvessels should be identified by scanning the entire tumor at low power (x40 and x100). This is the most subjective step of the procedure. It has been demonstrated that the experience of the observer determines the success of identifying the relevant hot spots. Poor selection will in turn lead to an inability to classify patients into different prognostic groups. Observers spend time in a laboratory where a period of training can be undertaken. Ideally, comparisons between hot spots chosen by an experienced investigator and trainee should be performed and continued on different series until there is more than 90% agreement. Training can be completed by assessing sections from a series that already contain prognostic information. Inexperienced observers tend to be drawn to areas with dilated vascular channels, often within the sclerotic body of the tumor. These central areas together with necrotic tumor should be ignored. Vascular lumina or erythrocytes are not a requirement to be considered a countable vessel; indeed, many of the microvessels have a collapsed configuration. Although the hot spot areas can occur anywhere within the tumor, they are generally at the tumor periphery, making it important to include the normal tumor interface in the representative area to be assessed. Vessels outside the tumor margin by one x200-250 field diameter and immediately adjacent to benign tissues should not be counted. The procedure takes 2-5 min. Once selected, a 25-point Chalkey point eyepiece graticule at x200-250 should then be oriented over each hot spot region so that the maximum number of graticule points are on or within areas of highlighted vessels. Particular care should be taken in the occasional case (<1% breast cancers) for which an intense plasma cell infiltrate can mimic a hot spot and obscure the underlying tumor vasculature. Plasma cells can otherwise be disregarded on morphological grounds. The mean of the three Chalkey counts is then generated for each tumor and used for statistical analysis. The procedure takes 2-3 min. For the intratumoral microvessel density index, any endothelial cell or endothelial cell cluster separate from adjacent microvessel, tumor cells, or matrix elements is considered a countable vessel. Those that appear to be derived from the same vessel if distinct should also be counted. Again, vessel lumens and erythrocytes are not included in the criteria defining a microvessel. There is no cutoff for vessel caliber. The procedure takes 3-6 min .Angiogenesis measured by MVD method correlates with the tumor behavior. There is a vast body of facts showing that the angiogenesis intensity and a higher MVD are associated with the metastases development, poor prognosis and life longevity shortage in breast ,62-64, uMicrovascular density would be a good indicator of therapeutic efficacy, but it has not been as useful for efficacy as it has been for prognosis. Since the early studies, hundreds of reports have examined the prognostic value of microvascular density in several forms of cancer. Nevertheless, despite the initial confirmatory publications, numerous reports appeared in the literature that failed to show a positive association between increasing tumor vascularity and reduced patient outcome, and caution as to the clinical utility of tumor angiogenesis is being urged. However, many of these negative studies may result from significant differences in methodologies .Quantification of tumor angiogenesis by counting microvessels in immunostained tissue sections was ranked by The College of American Pathologists in category III, encompassing “all factors which are not sufficiently studied to demonstrate their prognostic value”. The issues of methodological variation mentioned include: antibody selection, type of ?xative used, methods of counting vessels, calculation of microvessel density, observer variability  and cut-off value for ‘increased’ vascularity . Thus, tThe alternative ways of the quantitative angiogenesis evaluation are the determination of microvessel density in the randomly selected areas or in an intentionally chosen such as the tumor tissue edges. Whether the both methods provide the same biologic information or one is better in terms of some features over another, this question is open for the debates. Besides the MVD density itself, there are several parameters which can be determined and quantified analyzing the image of the angiogenesis network: mean microvessel surface proportions, vascular area fraction, absolute number of vessels, absolute microvessel number and their perimeter, vessel angle, length and squares of the defined capillary categories ,81,82. TWhen the MVD is calculated, the separate object stained for example with CD34 biomarker is registered as 1 vessel. It is assumed that by its nature, the immunochemical vessel staining is not an ideal diagnostic technique, because its important drawback is the fact that the surrounding vessels or the cluster of them can be recognized as 1 object, which will seriously negatively bias the future conclusions of the research ,86.Another interesting research field is the image densitometry applying the Feulgen reaction, which have been widely used for the DNA analysis . The VEGSpectral analysis has been applied for the image processing derived via microscopy for several years but to our knowledge this modality did not gain the wide spread use for the angiogenesis activities investigations ,98. SpecThe problem of angiogenesis in medicine touches such questions as the development and the implementation of the novel methods for a vascularized tissue staining, the elaboration of the reliable, precise and robust methods for the machine recognition and processing of the cell structures visualized on the organ tissue sections. The discovery of the new endothelial biomarkers has allowed developing immunochemical reactives for the clear and concise vascular structures visualization, and the quantitative computer-assisted analysis of this image provides a stable, non emotional unbiased assessment, which serves as the evidence-based fundament for the really scientific concepts and conclusions.The principal problem of angiogenesis research is the heterogeneity of various aspects of the research spheres: the unique genetic package inherited to every patient creates the unique picture of angiogenesis network. The ovarian tumors are classified according to their tissue structure features and in order to conclude a scientifically and, in particular statistically true and valid statements, it is necessary to select a group of patients having similar characteristics. The limitations of many studies are the minor number of similar objects. These obvious rules are necessary to determine the validity of such conclusions as the role of microvessel density and the other features derived from the image analysis of a histology section.Many pathologists prefer to use one representative section taken from the gross tumor specimen, and this section is scanned, then several shots are taken  of the hot spot areas. To our opinion, for the research purposes, the entire slide should be scanned or if the microscope has no a function like this, the pathologist should take as many camera shots as possible to cover the significant surface of the sample at a various magnification rates. This method is appropriate when the researchers aim to investigate the image objects which are not recognizable for a human eye neither with nor without a microscope. The disadvantage of this approach is the problem that this task is excessively time-consuming, and the derived slides from the whole slide surface might lack its representativeness and eventually be a low-yield in terms of the useful scientific information.


In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information  and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database.The neighboring analysis is achieved by evaluating the cosine scores of each bioassay pair and fraction of overlaps among the human-curated neighbors. Our results from the cosine score distribution analysis and assay neighbor clustering analysis on all PubChem bioassays suggest that strong correlations among the bioassays can be identified from their conceptual relevance. A comparison with other existing assay neighboring methods suggests that the text-mining based bioassay neighboring approach provides meaningful linkages among the PubChem bioassays, and complements the existing methods by identifying additional relationships among the bioassay entries.The text-mining based bioassay neighboring analysis is efficient for correlating bioassays and studying different aspects of a biological process, which are otherwise difficult to achieve by existing neighboring procedures due to the lack of specific annotations and structured information. It is suggested that the text-mining based bioassay neighboring analysis can be used as a standalone or as a complementary tool for the PubChem bioassay neighboring process to enable efficient integration of assay results and generate hypotheses for the discovery of bioactivities of the tested reagents. The number of High Throughput Screening (HTS) assays deposited in PubChem  has growOne systematic knowledge driven study of HTS assay data is to understand how they are inter-related. The PubChem database currently provides four methods of identifying bioassay relationships, which are based on 1) target information, 2) commonly tested active compounds, 3) commonly participated biological pathways, and 4) depositor annotations respectively [Text-mining based knowledge discovery has proven to be a complex task due to the ambiguity, complexity and domain specific aspects of the real world . Text-miWith the advances in text-mining techniques, one can identify and extract the desired content and make the document structurally organized, indexed and computationally accessible. For example, using the detection of the protein name and gene names -10, or tThe text-mining approach utilizing the frequency of the selected terms is straightforward, and has proved effective in the analysis of biomedical literature. For example, several independent research groups have succeeded in the mining of gene expressions based on free text -23. The In this work, we propose a new PubChem bioassay neighboring analysis that uses a text-mining based approach. Given our primary interest to find bioassay relationships, this approach utilizes the free bioassay descriptions combined with the Bag-of-words (BOW) approach for the numerical understanding of these text descriptions. Bioassay descriptions can be represented by a collection of lexical features or BOW. Therefore, they can be conceptually compared by the collections of word terms contained, which can be used to both identify the relationships among the bioassays and to measure their relevance. In this article, we show that this text-mining based neighboring analysis provides encouraging results by focusing on the overall conceptual content of the assay descriptions. It has the potential to group biologically relevant bioassays in a broader context, and complements with the existing assay neighboring methods.The Corpora analyzed in this work is the depositor provided assay descriptions retrieved from the PubChem BioAssay database. The unified description usually contains important information . Since the content is very flexible and there is no controlled dictionary that can be used for the conceptual understanding of the assay descriptions, the BOW approach is implemented to mine the collection of words that are meaningful in describing the bioassays. Extracting the unique terms or tokens of interest can be obtained by doing the following: 1) preprocessing the assay descriptions, 2) filtering out punctuation and a pre-collected set of stop words, and 3) removing parentheses and extra spaces. Stop words, such as 'is' and 'this', are usually less meaningful to the concept of the document, therefore are excluded during the tokenizing step and every term token obtained from assay descriptions is stemmed to their unified form for the purpose of accurate comparisons by using the Porter stemming algorithm. For insThe keyword terms of interest can vary in each of the individual assay descriptions. One computational approach to measure the difference is to compute their "occurrence frequency" and "document frequency".The "occurrence frequency" of a certain term usually means "the number of occurrences of the term in one document". And the "document frequency" usually means "the total number of occurrences of a given term in a set of documents".The document frequency was represented by the inverse document frequency (IDF),Mi is the total number of the occurrence of term i, and mi,j is the number of occurrence of term i in the j-th document.where i is the index of the given term, j is the index of the document. Vd is defined as:The document analysis process is designed to iterate each assay description and create a vector of the term occurrences for each unique word. Thus a document vector k reflects the number of unique words of interest that are collected after the pre-processing, j,i vis defined by the combination of the occurrence frequency and document frequency [where requency ,34,35:norm is the normalized occurrence frequency in the document j.where vj,i could be used to reflect the importance of a word term in two ways. First, it will have a higher value if the frequency of a word term in one assay description is higher and secondly it will have a lower value when the word term is co-occurring in more bioassays.Hence, The measurement of similarity between a pair of assay descriptions was calculated by comparing the two document vectors. There are a number of ways to compute the similarities among vectors, such as Euclidean Distance(ED) and Cosine Angle Distance(CAD), which is the cosine of the angle formed by the two document vectors. Both ED and CAD are widely used and work similarly for neighboring analysis of high dimension vectors. In thisV is a document vector.where We performed an unsupervised single linkage clustering analysis for the bioassays within PubChem, which was conducted by simple binning or neighboring the document vectors based on the pairwise similarity. We adopted an unsupervised clustering procedure here as it does not rely on predefined training examples while clustering the data objects. This clustering approach is different from pattern recognition or the areas of statistics known as discriminate analysis and decision analysis, as it was not aimed to maximize a utility function but rather to find similarities among the data objects.Evaluation of such unsupervised clusters extracted from medical text is shown to be problematic and external help (such as expert opinion) is often required . In the To evaluate the results of the text-mining based neighboring method, we first generate the distribution of the cosine scores over all pairs of bioassay descriptions to determine the threshold for suggesting meaningful relationships, and evaluate its sensitivity to the textual content of the bioassay records.For the second type of evaluation, we computed the overlap of the bioassay neighbors result from the text-clustering method against the human-curated neighbor set (as provided by the depositor) in the PubChem BioAssay database. Depositor-specified bioassay neighbors were determined by assay data providers to address various aspects of assay relationships . PubChem allows a depositor to cite assays from other depositors if that helps to illustrate their data or support their conclusion, though most of the depositor-provided assay neighbors were from the same depositor.A total number of 2,322 PubChem bioassays were used in this study. The contents of the descriptions are unstructured texts that usually provide background information about the goal of the screening . Assay descriptions were extracted from the PubChem BioAssay database, for which the BOW analysis was conducted.The distribution of the cosine scores over all pairs of bioassay descriptions is provided in Figure To examine the assay relevance measured by the cosine score, a manual verification and spot checking process was conducted by curating the details of the assay descriptions. The overall verification of the assay pairs reveals that the majorities of the identified assay neighbors with higher cosine score are closely related, and suggests that the cosine score serves as a strong indicator of conceptual relevance among bioassays. In particular, our result shows that the assay pairs with cosine score 0.90 or higher are directly related. For example, Bioassay AID 777 and AID 778 are identified as highly related (cosine score 0.94) in spite of the fact that their studies were on cytochrome P450 enzymes with different metabolic functions. The goal and protocol of these two assays were quite similar, which was to test the ability of the compounds to inhibit members of the P450 enzyme family for the conversion of the substrate luciferin-H EGE to luciferin EGE. By recognizing such related assays, one would be able to combine the results from the counter screenings to evaluate the inhibition specificity of the compounds towards different members of cytochrome P450 families. Assay pairs with a cosine score of 0.8~0.90 can also be highly related.in vitro cytotoxicity, were also clustered together. The potential to cluster such assays with toxicity measurement could be useful to construct an assay panel to systematically analyze the toxicity profiles of the compounds across multiple cell lines or organisms.When looking into the twilight zone with cosine score range of 0.5 ~0.8, it was noticed that identification of assay pairs with a cosine score greater than 0.6 can sometimes also be of interest to reveal their biological relevance. For the set of cellular toxicity HTS assays that the NIH Chemical Genomics Center (NCGC) developed against a number of cell lines, the text-mining based method was able to group a great portion of these assays together. For example, AID 658, AID 659, AID 661 and AID 657 were clustered together as they were all designed for measuring compound cellular toxicity in human cell lines. Meanwhile AID 433, 543,540, which were designed to determine The human-curated bioassay neighbor set refers to the related bioassays annotated by depositors. Depositor-specified annotations were subjected to the examination of the PubChem curators during the bioassay deposition process. Depositor-specified related bioassays address various aspects of assay relationships, such as linking primary, confirmatory and counter screenings of the same assay project. Although the perspective of the depositors may vary, such annotations on bioassay relationships provide a benchmark for evaluating the recognition of bioassay relationships by the text-mining algorithm. The selection of cosine score threshold is critical for identifying significant relationships among the compared assays. There is a trade-off between the precision and the recall for optimizing the threshold. Therefore, it is essential to compare the performance of identifying related assay pairs at a series of cosine score thresholds. The result of this analysis using the depositor provided bioassay relationship as a benchmark is summarized in Table This analysis suggests that cosine score of 0.4 can serve as a reasonable cut-off to balance the precision and recall, which agrees with the earlier analysis about the cosine score distribution. It is noted that precision at a threshold of cosine score of 0.9 is low. This is due to the limitations of the human-curated assay neighboring set where the coverage is low or incomplete. This is the case especially for the assays contributed by the NIH Molecular Libraries Program (MLP), where reports for a specific assay project are often split into many bioassay records, mostly because an assay project including follow-up experiments may take a few months to a couple of years to complete. Data produced at each experimental progress are required to be deposited in a timely manner into the central PubChem repository. Sometimes depositors tend to deposit test results from different compound libraries or from counter screenings under separate records. Thus, tracking the deposited records and providing a comprehensive linkage annotation on the overall assay relationship are burdensome for depositors, which explains one reason for the lack of a complete bioassay linkage annotation from depositor in PubChem.A significant amount of assay relationships can be confirmed by examining the assay pairs identified by the text-mining approach through spot checking. Although these assay pairs were not specified as related at the deposition time, about 99% of the assay pairs identified at this threshold were deposited by the same assay providers. It suggests that the text-mining based method complements human annotations significantly when only a limited bioassay relationship is provided by depositors.In many cases, our analysis also suggests that neighboring relationships from text-mining based bioassays correlate well with intrinsic relationships among bioassays. Moreover, this approach is especially efficient under conditions where other assay-clustering methods encounter limitations to apply. Bioassays AID 454, 455, 456, and 457 are related for screening compounds for enhancing/attenuating TNFa induced VCAM-1 cell surface expression with AID 457 (imaging assay) and 455 (plate reader assay) reporting compounds with augmentation effect, and AID 456 (imaging assay) and 454 (plate reader assay) reporting compounds with inhibition effect. Identifying the significant relationships among these assays would allow one to collect effective chemical reagents for the studied biological process. Unfortunately, such a relationship was not annotated by the assay depositors, and none of the other three automated assay neighboring approaches could detect this relationship due to the lack of target specification or common hits. However, with the aid of the text-mining based approach, the biologically important relevance among this group of assays were successfully identified. As another example, primary assays  searching modulators of post-Golgi transport were first clustered together at a cosine score cut-off of 0.90, then further connected with the related dose response assays at the cosine score cut-off 0.88. This hierarchical clustering result reflects the biological relationships among the assays at three levels: the purpose of the assays, the experiment and project stage. None of these bioassays have protein target information and they have very limited active compounds in common. Thus it is very difficult for the existing automated neighboring methods to discover their relevance.The text-mining approach compares each bioassay in PubChem against all of the rest bioassays irrespective of the data source. While this method is mostly efficient for detecting the relationship among assays from the same depositor, it was observed that nearly 1% of the related assays pairs identified are from different depositors. One such example is the AID 465 and 819 pair. These two assays came from two data sources but were recognized as related assays by the text-mining based method. Both assays were set up to identify chemicals modulating NFkB activities. Neither of them have targets defined, thus again making the existing target based neighboring method not applicable to them.There are different interests and perspectives when the enormous collection of HTS data is interpreted. Currently, there are four approaches in PubChem for neighboring bioassays, with each providing different insights into the bioassay relationship. These approaches include three automated approaches by using common biological pathway, finding sequence homology among protein targets, calculating chemical structure identity among hit compounds, and the one using the annotations from bioassay depositors.For evaluating the new text-mining based approach, comparisons of the four automated neighboring procedures were performed and summarized below using the human annotations as a baseline.Target similarity based bioassay neighboring analysis enables one to identify assays tested against biologically related molecular targets, facilitating the construction of an assay panel for compound selectivity and specificity study. The relevance of bioassays is evaluated by the sequence similarities of their protein targets. This approach is both simple and effective in clustering bioassays. It enables the straightforward retrieval and comparison of the sequences of the assay targets. The BLASTP algorithAn individual HTS assay for small molecules usually measures certain bioactivity properties as well as describes the bioactivity outcome for the tested compounds in a specific biological system. In order to decide whether a follow-up study is worthwhile, a compound may be tested in multiple HTS screenings assays that share common active compounds together would facilitate a comparison across multiple assays. In addition, a common group of compounds that perform similarly among different assays can be a very interesting indicator of the underlying relationship between the biological system used or the biological process monitored in the assays. Therefore the assay relationship identified by checking activity overlap or common hits could be of interest for generating new hypotheses. However, this approach is sensitive to the selection of the compound libraries tested in the assays, and may not be applicable for every assay. Since each assay may test a specific compound library, the overlap among the compound libraries is apparently the first determinant factor for this approach when neighboring assays. In addition, this method is also prone to experimental noise from HTS screenings.In the biosystem based assay neighboring method, common biological pathways of the respective proteins or gene targets are examined. The bioassays are considered as related if their protein or gene targets participate in the same biological pathways by using the National Center for Biotechnology Information BioSystems database . This tyUnlike the other bioassay neighboring methods discussed above, the text-mining based approach does not rely on the availability of specific annotations, but utilizes the free text descriptions. Since there is no specific domain knowledge defined prior to the text-mining, the relevance of bioassays depends on the concept of descriptions. Here the underlying concept of descriptions could be the accumulation of multiple meaningful terms .One of the advantages of the text-mining based neighboring analysis is to discover the relevance that other automated approaches cannot. To provide further evaluation of the text-mining based approach, the assay neighbors identified by this method were compared to those annotated by the bioassay depositors and those suggested by the other three automated methods. Precision and recall values for each automated method were computed using the depositor provided neighbors, which is also known as human-curated assay neighbors, as the benchmark. This dataset is contributed by independent bioassay submitters, which represents expert opinions upon the pairwise bioassay relationships. As this benchmark set does not depend on any particular data elements as required by the automated methods, it provides a way to examine to what extent the automated neighboring methods are in agreement with the human curated dataset and with each other. Annotations of the depositor provided related bioassays are stored in each bioassay record (query assay) in the PubChem database if applicable. In each such annotation, one assay can be denoted to be related to one or more assays (neighbor assays) through the cross-reference data field, resulting in one or more assay pairs for each of such annotation. To construct the benchmark dataset, related assay pairs were extracted from a total of 1747 bioassay records for which the depositor provided annotations are available. These assay pairs were further grouped into 1306 clusters using the unsupervised single linkage clustering procedure. The final list of assay pairs was derived by considering all possible pairwise combinations of the assays deemed related by each clustering method, and the total number of assay pairs were derived accordingly. As a result, the benchmark dataset contained 8802 bioassay pairs, with 41% of the assay pairs containing no target information. The median cluster size was 4 and 216 clusters containing a single assay pair. The F1 score, the harmonic mean of the precision and recall values, was provided for clearer comparison of the overall performance of the methods compared to human curated datasets. In this analysis, the cosine similarity threshold for the text-based method was 0.4. The results are summarized in Table It can be observed from Table i.e there were true predictions but not annotated by the depositors. When looking into the overlap of the predictions among different methods, 62.5% of the assay pairs from the text-mining approach were further confirmed by at least one of the other automated procedures, indicating that these bioassays can be related one way or another. Furthermore, the majority of the novel pairs detected by the text-based method involve assays contained no target specifications, which again demonstrates that the text-based approach may play a critical role in detecting bioassay relationships that are otherwise impossible for the existing methods to recognize.The low precisions, which led to low F1 value, for all four methods were expected. As discussed earlier for the results shown in Table In this study, we proposed a text mining based approach for neighboring PubChem bioassays from their unstructured text descriptions. The conceptual understanding of the bioassays is implemented by the BOW method, which extracts all of the meaningful word terms and their descriptors and represents the contextual importance of the words. The distribution of the similarity, measured by the cosine scores on all PubChem bioassay pairs, is obtained for deriving a meaningful and practical threshold and evaluating the precision and recall of the proposed neighboring analysis approach. This analysis indicates that a cosine score of 0.8 and above would suggest higher relevance for the compared bioassays which is confirmed by manual verification. The analysis on the precision and recall suggests that a threshold at cosine score 0.4 or higher may be used to identify a significant assay relationship. A statistical analysis using this threshold shows an overlap of 86% between the related bioassays detected by the text-mining approach and those specified by depositors. Moreover, this neighboring analysis identifies a significant fraction of additional assay relationships that were missed by depositors. Furthermore, results of the text based approach were compared to those derived by three other automated neighboring procedures based on target similarity, bioactivity overlap and common pathway. This comparison suggested that the text-mining neighboring analysis provides a meaningful approach based on the perspective of descriptive content in the bioassay records. The text-mining based bioassay neighboring analyses has proven to be advantageous and efficient particularly for correlating bioassays for studying different aspects of a biological process, which otherwise are difficult to achieve by the existing neighboring procedures due to the lack of specific data elements required by those neighboring methods. It also allows one to prioritize the assay relationship to facilitate the information retrieval. Overall, the text-mining based bioassay neighboring analysis can be used as a standalone or as a complementary tool to the PubChem bioassay neighboring process to enable efficient integration of the assay results and improve the utility of PubChem.http://pubchem.ncbi.nlm.nih.gov/. A latest web browser with JavaScript enabled is required to use it. The PubChem Bioassay data is also available at ftp://ftp.ncbi.nlm.nih.gov/pubchem/Bioassay/ through FTP. PubChem bioassay neighboring analysis data can be downloaded at ftp://ftp.ncbi.nlm.nih.gov/pubchem/Bioassay/AssayNeighbors/.The PubChem database is available at LH and TS carried out the bioassays neighboring analysis, participated in its design and performed the statistical analysis. YW and SB conceived of the study, and participated in the design, project coordination and data analysis. All authors read and approved the final manuscript.


The mismatch negativity (MMN), an event-related potential (ERP) representing the violation of an acoustic regularity, is considered as a pre-attentive change detection mechanism at the sensory level on the one hand and as a prediction error signal on the other hand, suggesting that bottom-up as well as top-down processes are involved in its generation. Rhythmic and melodic deviations within a musical sequence elicit a MMN in musically trained subjects, indicating that acquired musical expertise leads to better discrimination accuracy of musical material and better predictions about upcoming musical events. Expectation violations to musical material could therefore recruit neural generators that reflect top-down processes that are based on musical knowledge. We describe the neural generators of the musical MMN for rhythmic and melodic material after a short-term sensorimotor-auditory (SA) training. We compare the localization of musical MMN data from two previous MEG studies by applying beamformer analysis. One study focused on the melodic harmonic progression whereas the other study focused on rhythmic progression. The MMN to melodic deviations revealed significant right hemispheric neural activation in the superior temporal gyrus (STG), inferior frontal cortex (IFC), and the superior frontal (SFG) and orbitofrontal (OFG) gyri. IFC and SFG activation was also observed in the left hemisphere. In contrast, beamformer analysis of the data from the rhythm study revealed bilateral activation within the vicinity of auditory cortices and in the inferior parietal lobule (IPL), an area that has recently been implied in temporal processing. We conclude that different cortical networks are activated in the analysis of the temporal and the melodic content of musical material, and discuss these networks in the context of the dual-pathway model of auditory processing. Playing a musical instrument brings together the faculties of the human body in the most intricate way. It involves the translation of visual cues from the notation, fine auditory discrimination, precise control of complex movements, and detailed somatosensory feedback about their execution. It requires a major effort to coordinate the multitude of processing in a manner adequate for pleasing performance. Indeed, musicians have to have talent and have to exercise immensely to achieve good levels of control over their instrument. Many studies have shown that the brains of musicians exhibit structural and functional differences compared to non-musicians  was matched to one of the piano-trained subjects (the sensorimotor-auditory or “SA” group) and listened to all of the training sessions of that subject. During auditory training, subjects of the A-group were seated in front of the piano, but they received no visual information about the keys that had been pressed. Participants of the A-group had to press the right or left foot-pedal to indicate that the sequence they heard was correct or not. This was done to ensure that subjects of the A-group participated actively and listened carefully. The assignment of the test subjects to either the SA or to the auditory (A) group was random. The results showed no improvement of the MMN responses after auditory training  and, to a lesser extent, in areas of the inferior frontal cortex (IFC)  is a method of source analysis of MEG sensor data in which a spatial filter is used to estimate the contribution of a given source location to the measured MEG sensor signal, while filtering out the contributions of other sources  and spuriously mapped by the beamformer to the brain.The beamformer approach SAM can be applied together with pseudo-This method has originally been developed to separate an active, task related condition, from the background brain activity , here with a spatial resolution of 3 mm, which is similar to the activation maps known from fMRI studies. Then, SPM can be used for the group analysis and the spatial normalization for the subjects (averaged across runs). Since one cannot assume a normal distribution for volumetric maps of pseudo-T images, non-parametric permutation tests should be used to investigate the significance of activated brain regions  was used to record the magnetic field responses. The recordings were carried out in a magnetically shielded and acoustically silent room. MEG signals were low-pass filtered at 150 Hz and sampled at a rate of 600 Hz. Epochs contaminated by muscle or eye blink artifacts containing field amplitudes that exceeded 3 pT in any channels, were automatically excluded from the data analysis. Subjects were seated in an upright position as comfortable as possible while ensuring that they did not move during the measurement. Subjects were instructed to stay in a relaxed waking state and not to pay attention to the stimuli. To distract attention from the auditory stimuli subjects watched a soundless movie of their choice, which was projected on a screen placed in front of them.The subject's head position was measured at the beginning and at the end of each run by means of three localization coils that were fixed to the nasion and to the entrances of both ear canals .A T1-weighted MR image was obtained from each participant using a three Tesla Scanner . Turbo Field acquisition was applied to collect 400 contiguous T1-weighted 0.5-mm thick slices in the sagittal plane. For co-registration with the MEG measurements the positions of the fiducial points (filled with gadolinium to be visible in the MRI) were used.Epochs of 3.1 s (comprising one standard and on deviant stimulus) were extracted from the dataset and filtered by 1–30 Hz bandpass filter. A multi-sphere head model fitted to the individual participants' structural MRI was used as volume conductor. To achieve as much similarity as possible between the background brain activity during the active and control condition, we contrasted each deviant with its directly preceding standard. This reduces the number of analyzed standards to the number of deviants . We conducted the beamforming analysis within the time window of 100–200 ms. The time window was chosen according to the corresponding source wave form that were obtained from the ERP analysis Figure . The actThe results of the beamformer analysis can be seen in Figure Comparing the beamformer results between the melody and the rhythm data sets shows that pitch and rhythm deviations are processed in different brain areas. While a melody deviation elicited activation in the right STG, in the right OFC, in the IFC, and SFG bilaterally, rhythmic deviation elicited activation in the left STG, the left and right IPL, and the right insula. One has to remember, however, that the results were obtained in a between-group and not in a within-subject design. However, the design of the two studies was identical except for the musical material that was trained. The results confirm previous music studies showing similar activation areas after a harmonic or rhythmic deviation . According to that model IPL, as part of the dorsal stream, is involved in sensorimotor mapping of sound to motor and articulatory networks. The ventral stream, on the other hand, is responsible for mapping sound to meaning. The dual stream model for language processing comprises thereby speech perception and production connecting the motor-speech-articulation systems in the parietal lobe with lexical-semantic representations (Hickok and Poeppel, . AccordiThe beamformer analysis revealed that IFC activation was similar in the left and right hemispheres. This finding is consistent with our previous ERP analysis of the data. In contrast to our melody study, where we found a stronger involvement of the right hemisphere after a pitch deviation, the results of the rhythm study suggest that both hemispheres were involved when processing a rhythmic deviation. Previous studies showed that musical rhythm is processed bilaterally (Limb et al., In addition, the beamformer analysis revealed activation in the right insula, an area that is associated with auditory temporal processing (Bamiou et al., To investigate the neural generators of musical deviance detection for rhythmic and melodic material we compared beamformer localization of musical MMN data from two previous ERP studies, in which the musical MMN was measured after short-term piano training. We analyzed only post-training data, since in the pre-training data the MMN were not robust enough to conduct a beamformer analysis.The training material of one study focused more strongly on the melodic harmonic progression whereas in the other study, training material focused on rhythmic progression. Accordingly, in the MEG measurements, subjects of the melody study were tested on a melody violation whereas subjects of the rhythm study were tested on a rhythmic violation.In the melody study, the beamforming analysis revealed significant right hemispheric neural activation in the STG, IFC, and the SFG and OFG within a time window of 100–200 ms after the occurrence of a deviant tone. IFC and SFG activation was also observed in the left hemisphere. In contrast, in the rhythm study, the beamformer analysis revealed neural activation bilaterally within the vicinity of posterior auditory cortices and in the IPL, in an area that has previously been associated with temporal processing. The results are in line with studies investigating neural sources involved in harmonic or rhythmic deviance detection in musical material (Maess et al., 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.


Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity –or chatter– that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios. Far from being silent and static, the habitat of a cell is usually composed by multiple and simultaneous signals. We can consider nutrients, hormones, temperature, light, and other stimuli as elements building a default environment in which cells grow, divide and die. This environment, which has an intrinsically fluctuating nature, is the setting in which cells process all incoming stimuli. Here we examine the role that this background activity –or signaling chatter– plays in the transmission of information in a typical human cell. We address this question using a cellular model of signal transduction that we simulate using both random and periodic stimuli. We find that the level of background chatter determines the response of the whole signaling network to external stimuli. Different areas of the network are activated by specific levels of background activity, routing the information through chatter-dependent paths. In this way, different levels of chatter allow the network to select between different responses, given the same stimulus. These features depend on the architecture and functional connectivity of a truly biological network, since we find that randomized versions of the model are incapable of showing this behavior. Signal transduction, the process through which information about the extracellular environment is conveyed to the cell's interior, is a property of all living organisms. Signaling molecules stimulate their receptors, which transmit the signal downstream through a series of protein-protein interactions that ultimately modify DNA expression and protein levels chatter, provided by other network inputs. To address this issue in a way that explicitly accounts for the complexity of the system under consideration, we use one of the most comprehensive dynamic models of cell signaling currently available in the literature, a recently published Boolean network for the human fibroblast that involves over 130 protein species Adding to these information-processing requirements, the signaling machinery has to cope with the fact that a cell's environment is not stationary, but subject to fluctuations context-dependent signaling Given the ubiquity of periodic oscillations in cellular processes The Boolean Network (BN) model used in this work was built by Helikar and coworkers original network hereafter) consists of 139 elements, or nodes, connected by 542 links. Nodes represent chemical species, which are assumed to be either active or inactive, and edges represent their interactions. The states of all species are updated synchronously at each iteration according to a set of node-specific deterministic rules. Hence, the state chatter in the network, which is lowest for Mathematically, the BN used here  version of the network show that its response is, in general, not sensitive to the chatter level  family are sensitive to chatter levels in a similar way to the original experimentally-based network  of the original network, while permuting randomly the update rules, thus changing the logic of each node  of each output node see . Figure We have studied in detail the periodic stimulation of every input node of the fibroblast network, and have found that they all show the same qualitative phenomenology see , with thWe now examine the extent to which the chatter-dependent ability of the network to select its response depends on its connectivity. To address this question, we now perform the same numerical experiment for the two randomizations of the network described previously  between the average signals of all pairs of nodes  surrounding cells in higher organisms, which affects processes as important as embryogenesis, tissue morphogenesis, remodeling and cancer progression Noteworthily, the information-carrying signals are frequently dynamical, since oscillations in cell physiology are ubiquitous context-dependent signaling. Our results fit well with these findings, as we see that the level of background activity of the input nodes determines the capability of the cell to respond to other inputs . In their work, Natajaran et al.interaction agent to refer to the network circuits that couple different signaling pathways. They claim that such circuits would be silent in single ligand experiments and become active upon multiple input signaling, causing the non-additive effects observed for certain pairs of inputs. In our theoretical study we effectively observe different areas of the network being used at specific chatter levels, thus supporting the existence of these circuits.At the experimental level, context-dependent signaling is beginning to be unraveled. For example, the Alliance for Cell Signaling (AfCS) recently compared the effect of 22 individually applied inputs  upon 42 cell outputs , to the effect of all possible pairwise combinations of those inputs et al. in that we focus on the dynamics of the system. Having recently explored the relaxation time and frequency response of the network Our results show specifically that a detailed signaling network, carefully compiled from published experimental data et al.et al.Recent studies have shown that signaling networks prioritize dynamic range over signal strength Our results also show that varying chatter levels allow the network to select which output nodes respond preferentially to a given input. Indeed, output switching is achieved via a mechanism that places few requirements on the temporal structure of contextual, non-specific signals. Thus, we conjecture that cells could use environmental noise (to which they are unavoidably subject) to choose among alternative information routes, and eventually among different cellular responses. Randomized versions of the original network in which the topology of the connections is maintained -and only the integration rules at the nodes are altered- fail to reproduce this property, indicating that the chatter-driven selectivity reported here is fine-tuned to the specific architecture and logic of the experimentally-supported network.classifier hyperspace proposed by Oda and Kitano in their study of the Toll-like receptor signaling pathway Concurrent with the ability of background chatter to select the dominant output for a given input, chatter also selects the network path through which information is transmitted. The nodes belonging to these preferred paths can be expected to form the Figure S1Annotated fibroblast network. Specific information about the update rule for each node can be found at http://mathbio.unomaha.edu/Database.(EPS)Click here for additional data file.Figure S2Network randomization. Two methods of randomizing signal integration at the network nodes. The update rule of one of the nodes of the original network is shown as an example (left column). The first randomization method  consists on shuffling the output of the update rule, in such a way that the logic of the logic gate changes but the number of active output states is maintained. The second randomization method  consists instead on shuffling the inputs of the network node while maintaining the logic of the update rule (the logic is given below the truth table in each case).(EPS)Click here for additional data file.Figure S3Topological comparison of the networks. Distribution of the number of canalizing functions for the AL networks, as defined in Figure 7. The arrow marks the number of canalizing functions of both the original and the AI networks.(EPS)Click here for additional data file.Figure S4Response of the biological and randomized networks to unstructured inputs. Temporal average of the output population activities for increasing chatter level for unstructured inputs. (a) Original network; (b–f) five out of 100 realizations of the altered-logic network models, in which the logic rules at the nodes are changed randomly ; (g–k) five realizations out of 100 of the altered-input network models, in which the logic rules are maintained but the inputs are shuffled randomly.(EPS)Click here for additional data file.Figure S5Quantifying the responsiveness of randomized networks to unstructured inputs. Distribution of the number of output nodes that respond to the unstructured inputs of Figure 9, for 100 realizations of each of the two types of network randomizations described in Figure 7. A node is considered responsive when the dynamic range of the average activity (EPS)Click here for additional data file.Figure S6Network response to periodic stimulation of the input node stress in the presence of chatter. Power spectral density of the average activity shown in (EPS)Click here for additional data file.Figure S7Response of the biological and randomized networks to structured inputs. Power spectral density at the stimulation frequency of the population activities of the output nodes  for five structured inputs Click here for additional data file.Figure S8Quantifying the responsiveness of randomized networks to structured inputs. Distribution of the number of output nodes that respond to a modulation of the stress input , for 100 realizations of each of the two types of network randomizations described in Figure 7. A node is considered responsive when the dynamic range of the power spectral density at the stimulation frequency Click here for additional data file.Figure S9Paths of information flow between different input-output pairs, and varying levels of chatter. Paths with highest correlation for all chatter levels (top panels in each figure set) and correlation scores of those paths as chatter increases (bottom panels in each figure set). Figure sets (a) to (e) correspond to the five different specific inputs, and within each set the columns correspond to the different outputs.(EPS)Click here for additional data file.Figure S10Network sensitivity to chatter. Maximum sensitivity of nodes and edges when no structured input is present (a) and for five structured inputs . Red (blue) color scales for negative (positive) values of the sensitivity. Only a few nodes and edges present a high maximum sensitivity in absolute value. Most of these few nodes and edges are part of at least one of the paths previously identified as dominant. The major part of nodes and edges not involved in transmission information paths are robust to chatter as they do not have large sensitivities to it.(EPS)Click here for additional data file.Figure S11Robustness to asynchronous updating. Network response to periodic stimulation of input node stress ((EPS)Click here for additional data file.Figure S12Role of temporal variation of the chatter. Fraction of responsive cells  versus chatter level (EPS)Click here for additional data file.Text S1Supporting Text.(PDF)Click here for additional data file.


This article introduces a discrete reaction-diffusion-mechanics (dRDM) model to study the effects of deformation on reaction-diffusion (RD) processes. The dRDM framework employs a FitzHugh-Nagumo type RD model coupled to a mass-lattice model, that undergoes finite deformations. The dRDM model describes a material whose elastic properties are described by a generalized Hooke's law for finite deformations . Numerically, the dRDM approach combines a finite difference approach for the RD equations with a Verlet integration scheme for the equations of the mass-lattice system. Using this framework results were reproduced on self-organized pacemaking activity that have been previously found with a continuous RD mechanics model. Mechanisms that determine the period of pacemakers and its dependency on the medium size are identified. Finally it is shown how the drift direction of pacemakers in RDM systems is related to the spatial distribution of deformation and curvature effects. Dictyostelium discoideum (Dd) Reaction-diffusion (RD) partial differential equations describe important spatio-temporal phenomena, including waves and patterns in a variety of chemical, physical, and biological systems. Important examples of these phenomena include waves in the Belousov-Zhabotinsky (BZ) reactions To model the effects of deformation on wave propagation in RD systems, it is necessary to describe the underlying mechanical phenomena in terms of the RD process. As such, a coupled reaction-diffusion-mechanics (RDM) framework was introduced in Continuum mechanics is among the most valuable and widely used approaches in engineering and modeling studies, however, it does not explicitly describe the particular micro-organization of a medium, which might be important for certain aspects of RDM systems. Cardiac tissue, for example, consists of individual cells that form layers of muscle fibers, which are tightly packed and organized by an extra-cellular matrix into branching sheet structures In this paper, discrete elastic modeling is coupled with FitzHugh-Nagumo type RD partial differential equations to study RDM systems. First, the process of the setting up of the discrete RDM (dRDM) model is described in detail, and computational and numerical aspects are addressed to discuss the macroscopic elastic properties of the medium. Secondly, as an illustration of the new modeling approach, the dRDM model is applied to study pacemaking activity in the RDM system shown in In this paper, as in Following 1 to the mass points 2 and 3. The positions of the mass points are given by 1 and 2 are connected by an active spring. The force generated by this spring on the mass points is given byTo model the mechanical properties in the dRDM approach a 2D lattice that consists of material points connected by springs  is used.1 and 2 given by1 and 3 are connected by a passive spring. The force generated by this spring is given byThe elastic mechanical properties of the dRDM model are determined by the geometry of the lattice unit cell and the stiffness of the springs. In classical continuum mechanics, elastic properties are represented by constitutive relations between the corresponding stress and strain tensors. Constitutive relations are successfully used to describe the elastic properties of many materials including biological tissues. It is possible to formulate the elastic properties of this paper's mass-lattice model in terms of an equivalent continuous material. The mass-lattice model  in this Numerical studies were performed to illustrate the material properties for conditions used in this paper's simulations. A deformation field was created by applying a force at the central mass point of an extended 2D lattice, whose boundaries were fixed in space (isometric boundary conditions), leading to finite displacements and local deformations. Next, Eqs.4–7 were solved to mechanical equilibrium and the displacement of the mass point from its initial position and the angle between the force and the displacement vector were calculated. Similar computations were performed for different orientations and amplitudes of the force vector. This comparison  demonstrmations  . The angThe deformation of cardiac tissue alters the process of wave propagation. It has been shown in studies of excised cardiac tissue and the whole heart that the direct physiological influence of contraction on cardiac tissue is given by a depolarising stretch-activated current ints see  normalizThe dRDM model was solved using a hybrid approach, combining an explicit Euler scheme for the RD Eqs.1–3,9 with a Verlet integration scheme The solution procedure of the dRDM model is as follows: after d points . For griFor the integration of the dRDM model Eqs.1–7, 9 several parameters of the numerical scheme were chosen. This section demonstrates how parameters were set to assure accurate and efficient computations.The RD Eqs.1–3 were solved using the finite difference approach with an explicit Euler integration scheme. Previous studies of the Aliev-Panfilov RD model used space steps of The equations describing the motion of the mass points Eqs.4–7 represent a system of coupled, driven, damped oscillators. As elastostatics was assumed in this work, the damping-stiffness ratio In RDM systems, the deformation is more smoothly distributed in space compared to the state variables of the RD equations e traced . The devNote that for testing patterns used here, the instantaneous error norm This section analyses and compares the computational performance of the dRDM and PKN approaches. In particular, the scaling of the computational times for the two modeling schemes versus the number of mechanical nodes We have introduced a discrete modeling framework to study the basic properties of RDM systems. We first show that the dRDM approach is able to reproduce some previously reported results on pacemaking activity, which were identified using the PKN model In To begin, the phenomenon of self-organised pacemaking activity is described. It has been found that a single electrical or mechanical point stimulus can cause the formation of a pacemaker in a RDM medium with non-oscillating RD kinetics. Pacemaking activity occurs because the contraction of the medium that follows a radially propagating wave of excitation subsequently stretches the medium in the neighborhood of the initiation site. This stretch induces a depolarizing stretch activated current Simulations with the dRDM and the PKN models were performed, which showed that all effects found with the PKN model We also studied how the location of the peripheral attractors depended on the medium size. The increased resolution of the dRDM model (compared to This section is devoted to the cases of pacemaking activity that result in a static pacemaker located at the center of the medium. The aim of this section is to understand the factors that determine the period of the pacemaker and its dependency on the medium size. This investigation commenced with the study of the spatial and temporal transient processes leading to the steady state configuration of a pacemaker with a constant period located at the center of the medium. The results on the study of how the medium size affects the equilibrium period of a stationary pacemaker located in the center of the medium are shown in Curvature effects are well known in the theory of excitable media For an expanding wave front This section focusses on pacemaker drift. on point . As a reon point . Howeveron point . This inTo show that the stretch activated current As demonstrated in In this paper, we introduce a discrete modeling framework for the study of reaction-diffusion-mechanics systems. The model is based on the coupling of a mass-lattice model with reaction-diffusion equations. Mass-lattice models are widely used in various areas of computational mechanics research and application Discrete mechanics modeling and thus our dRDM model is not limited to isotropic material relations. Bourguignon et al. showed that discrete mechanics modeling can be applied to describe elastic properties of anisotropic materials Although cardiac tissue is anisotropic, our isotropic approach can still be applied to several experimental systems. For example the dRDM model is suitable to describe electromechanical processes in cultures of cardiac cells. The tissues produced in these experiments do not show electrical or mechanical anisotropy.In this work, a discrete mechanical model was coupled with a low-dimensional RD model for cardiac excitation to study the effect of mechano-electrical feedback on cardiac excitation. The phenomenon of pacemaking activity due to stretch activated current was studied, which was previously done using a continuous RDM approach In the continuous modeling approach, the Laplacian in Eq.1 was formulated as a function of the metric tensor to model the influence of deformation on diffusibility