The process of morphogenesis has long inspired the wonder and imagination of those who study it. And until the advent of adequate microscopy and lab techniques in the early 19th century, theories based more on imagination—like preformation, which held that sperm harbored fully formed, tiny beings—than observation persisted. But observationally based embryology, it turned out, revealed a notion even more fantastic: the complex higher-order architecture of tissues and organs emerges from a single cell.Drosophila, Fz can activate two distinct developmental pathways: the Wnt/β-catenin pathway and the Fz/planar cell polarity (Fz/PCP) pathway. In the Wnt pathway, a Wnt ligand activates the transmembrane Frizzled receptor, which in turn activates the subcellular Disheveled (Dsh) protein, setting off a signaling cascade that ultimately activates genes involved in cell division. The Fz/PCP pathway affects the orientation of wing bristles and the symmetry of the repeating units (ommatidia) in the fly's compound eye.Patterns and structures arise largely through cell-to-cell signaling, directed by signaling molecules (ligands) and their receptor targets. These signaling pathways control key developmental processes like cell proliferation and orientation . A relatively small cadre of molecules is enlisted over and over again to initiate an equally limited number of pathways to shape a developing embryo. Though the mechanics and effects of many of these pathways are understood, far less is known about the mechanisms that regulate which pathway is activated. One well-studied family of proteins, called Frizzled (Fz), regulates body symmetry and cell polarity, which, among other things, makes sure the bristles on a fly's wing all point in the same direction. In the fruitfly Previous studies suggest no clear association between a particular ligand–receptor combination and the downstream pathway, begging the question of how similarly structured receptors can signal through a common protein (Dsh) to activate different signaling pathways. As Jun Wu, Thomas Klein, and Marek Mlodzik report in this issue, it's all a matter of being in the right place at the right time.Since the same Wnt ligand–Fz receptor combinations can produce different results, the researchers reasoned that signaling specificity might depend on the context and cell type. This notion is supported by evidence that Wnt ligands bind at Fz mainly along the basolateral membrane of developing epithelial cells and that Fz hews to the apical membrane of developing wing epithelia during PCP signaling.  The researchers investigated whether this location bias affects which pathway is activated by focusing on two members of the Fz family: Fz1 and Fz2. Either can activate the Wnt pathway, but only Fz1 is involved in the Fz/PCP pathway. Wu et al. first confirmed that the proteins congregated in distinct subcellular regions of developing wing epithelial cells. Then they looked for sequences or domains in the proteins that might account for their location preferences by creating Fz1/Fz2 hybrids made of various combinations of three different Fz domains. (One was the ligand-binding domain, the second the transmembrane domain, and the third the cytoplasmic “tail.”) All the hybrids with a Fz1 tail localized along the apical membrane while those with a Fz2 tail preferred the basolateral membrane, indicating that the tail domain of a receptor controls its location.The team went on to correlate apical Fz with higher levels of Fz/PCP signaling, based in part on observations that wing hairs point away from areas of Fz expression, a result associated with PCP signaling. They also showed that increased Fz activity in apical regions results in wing notches and missing bristles—traits associated with reduced Wnt signaling—indicating that apical Fz expression interferes with Wnt/β-catenin signaling. That Fz receptors can elicit distinct responses depending on their subcellular location helps explain how so few molecules can juggle so many tasks, including the miraculous feat of building an organism.


To determine if the search technique that is used to sample randomized controlled trial (RCT) manuscripts from a field of medical science can influence the measurement of the change in quality over time in that field.RCT manuscripts in the field of brain injury were identified using two readily-available search techniques: (1) a PubMed MEDLINE search, and (2) the Cochrane Injuries Group (CIG) trials registry. Seven criteria of quality were assessed in each manuscript and related to the year-of-publication of the RCT manuscripts by regression analysis.No change in the frequency of reporting of any individual quality criterion was found in the sample of RCT manuscripts identified by the PubMed MEDLINE search. In the RCT manuscripts of the CIG trials registry, three of the seven criteria showed significant or near-significant increases over time.We demonstrated that measuring the change in quality over time of a sample of RCT manuscripts from the field of brain injury can be greatly affected by the search technique. This poorly recognized factor may make measurements of the change in RCT quality over time within a given field of medical science unreliable. Considerable effort has been directed toward improving randomized controlled trial (RCT) design, execution, and reporting -6,14. SuAs much as the RCT manuscripts published in a single journal or group of journals would provide a well-defined parent population, the RCT manuscripts from a given field of medical science would be difficult to completely identify. Ultimately no search strategy can claim to identify all manuscripts on a given topic that have been published in every book and journal worldwide. Thus, two search techniques might provide considerably different samples of RCT manuscripts from the same field of medical science depending upon how much and / or what parts of the parent population of RCT manuscripts they can access. The current communication empirically demonstrates this point as a potential pitfall in measuring the change in quality over time of RCT manuscripts sampled from a representative field of medical science.We chose internal validity as a measure of quality according to the definition given by Gehlbach , namely We intended to measure the presence or absence of various criteria of RCT quality as described in the published manuscript. No attempt was made to contact the authors of a manuscript either to clarify the information provided in the manuscript or to gain additional information about a RCT. We acknowledge that relying on the published manuscript in order to assess the quality of a RCT may be biased (1) against well-designed RCTs that were reported in poorly written manuscripts and (2) in favor of poorly-designed RCTs that were reported in well-written manuscripts . Thus, osine qua non of the RCT, additional criteria have been so frequently included in their design and execution that they are now commonly considered as part of quality RCTs. Several sources (themselves located by PubMed MEDLINE and bibliography searches) were used to identify such criteria [Although random allocation and the use of a concurrent control group are the criteria ,9,13-18.We limited our quality scale to measure criteria that have been demonstrated empirically to be associated with the quality of RCTs. This necessarily excluded many items associated with RCT design and execution that are widely thought to affect quality or that are included in commonly-used quality scales, but it provided us with a defensible "bare minimum" definition of quality. It should be noted that we did not intend our list of criteria to be encompassing of all aspects of quality; our criteria were intended to serve only as a tool for the comparative analysis of the two sets of RCT manuscripts for the purpose of this study.Each of the seven criteria was scored as being present 1 point) or as absent (0 points) in the RCT manuscript. Definitions of each criterion are shown in Table  point orThe intra-rater reliability for the scoring of the quality criteria was determined by comparing the individual criteria scores given to n = 16 RCT manuscripts by one of the authors of this communication (MKB) on two occasions separated by 3 weeks. The correlation coefficient (Kappa) measured in this manner was 0.94.Inter-rater reliability was determined by comparing the quality criteria scores given to n = 10 RCT manuscripts by two different examiners. One copy of each manuscript was scored by one of the authors of this communication (MKB) while the other copy was scored by an independent examiner (Dr. Babak Jahromi, Department of Neurosurgery, the University of Toronto) who was provided with a thorough description of the criteria. The correlation coefficient (Kappa) for inter-rater reliability was determined to be 0.74.We chose to evaluate the field of brain injury because two search techniques for sampling the population of these RCT manuscripts were readily available. The first search technique was our own PubMed MEDLINE search. The second search technique was performed by the Cochrane Collaboration Injuries (CIG) Group, and forms the CIG trials registry. Copies of the RCT manuscripts identified by these two search techniques were retrieved through the library holdings and interlibrary loan services of five universities.Next, the manuscripts were read by one of us (CY) to screen-out inappropriately identified manuscripts. Table The design and yield of the two search techniques was as follows:1) the PubMed MEDLINE search: The first search technique we used to identify RCT manuscripts pertaining to brain injury involved the PubMed search engine of the MEDLINE database. It was designed to represent a typical literature search performed by a North American researcher who is fluent only in English. The search term "brain injuries" (C10.228.140.199) was used with the limitations of (1) randomized controlled trial, (2) human subjects, and (3) publication in English. The PubMed MEDLINE search included manuscripts indexed from January, 1966, up to February, 2001 (the time at which the search was performed).         The PubMed MEDLINE search identified n = 139 manuscripts. During the screening process, n = 41          manuscripts from the original 139 (30%) were discarded leaving n = 98 manuscripts (see Table 2.) the CIG trials registry: The Injuries Group of the Cochrane Collaboration was kind enough to share their list of RCT manuscripts with us for the purpose of conducting this study. The list of manuscripts they provided was compiled by means of the following three steps:Fiona.Renton@lshtm.ac.uk) as                   well as various hand searches of journals performed during the writing of systemic reviews; it is                   updated quarterly.                  step 1) The CIG trials master list was searched using the keywords "head" or "brain" in conjunction                   with "injur*" or "trauma*". The CIG trials master list is a local database maintained at the London                   School of Hygiene and Tropical Medicine that uses a detailed search strategy to identify RCTs                   from multiple computerized databases  MEDLINE, EMBASE, and CENTRAL databases were searched using the exploded                   keyword "head injuries:ME" or "head injuries:TI". EMBASE includes references from 1974                   onward and, while it uses its own database, it is based on an indexing hierarchy which incorporates                   that used by MEDLINE. Here, MEDLINE was searched with the SilverPlatter search engine, not                   with the PubMed Search engine. Manuscripts of the MEDLINE database indexed as early as 1966                   were accessible to the SilverPlatter search engine. The CENTRAL database is a general list of                   clinical trials that is maintained by the collaborative efforts of multiple Cochrane specialty groups.                  step 3) Manuscripts identified by hand searches of relevant journals and from references provided                   by direct contact with experts in the field of brain injury were also included.         The original CIG trials registry was completed in 1998 and was last fully updated in May, 2001; it is that          version which was used in our study.         The CIG trials registry included n = 312 manuscripts. During the screening process, n = 139 manuscripts          from the original 312 (45%) were discarded leaving n = 173 RCT manuscripts (see Table 3.) overlap between the PubMed MEDLINE search and the CIG trials registry: Of the total unscreened samples of manuscripts identified through each search technique, n = 80 manuscripts were present in both samples; this corresponded to 58% of the sample of manuscripts identified by PubMed MEDLINE search and 26% of the sample of manuscripts from the CIG trials registry. After the removal of inappropriate manuscripts during the screening process, and scoring process only n = 56 manuscripts were identified by both the PubMed MEDLINE search and the CIG trials registry. This corresponded to 57% and 32% of the PubMed MEDLINE search and the CIG trials registry samples, respectively.Each of the RCT manuscripts was read by both authors of the current communication (CY and MKB) who, for clarity's sake, will be referred to as "examiners". One examiner ("non-judging examiner": CY) performed the screening process described previously, then recorded the year-of-publication of each manuscript that survived the screening process in a computerized spreadsheet (Microsoft Excel) and marked them with identification numbers. Then, the non-judging examiner hid the names of the authors of the manuscript, the authors' degrees and departmental affiliations, the journal in which the RCT manuscript was published, and the year-of-publication of the manuscript with black marker. This information was covered wherever it was found in the manuscript so that when the manuscript was scored by the second examiner ("judging examiner": MKB) there would be no potential for bias ,19. The As mentioned above, allocation concealment was included in the list of quality criteria after the first evaluation of the manuscripts. Accordingly, the judging examiner re-read all the manuscripts specifically to determine the inclusion of allocation concealment. The manuscripts were still blinded as described above, and the data was entered into a third spreadsheet that was subsequently analyzed independently of the preexisting data.Manuscripts in French and Spanish were read without written translation by the judging examiner, whereas written translations were provided to the judging examiner for manuscripts in Japanese (by CY), German and Italian (by Mrs. Margaret K. Borsody), and Chinese (by Language Line, Inc., document translation service).After completion of the scoring process, statistical analyses were conducted by the judging examiner. The data was considered interval in nature and thus data analysis for discrete variables was used . FurtherSince the samples of manuscripts from the PubMed MEDLINE search and the CIG trials registry are known to be derived from the same parent population of RCTs (i.e., RCTs in the field of brain injury), it is inappropriate to directly compare them against each other with statistical tests. Rather, it was our goal to analyze the two samples of RCT manuscripts separately, and to make likely conclusions about the parent population from each sample of manuscripts as if there was no other sample of manuscripts available for comparison. Then, knowing that the two samples of RCT manuscripts represent the same parent population, it was our intention to compare the conclusions derived from the separate analyses to determine the impact of the search technique thereupon.Regression analysis of the individual quality criteria against the year-of-publication of the RCT manuscripts was performed to determine if the frequency of reporting of each quality criteria changed over time. For the sample of RCT manuscripts identified by the PubMed MEDLINE search, no significant relationship was found for any individual quality criterion  that themselves can affect the identification of manuscripts from the common database. Whatever may be the cause for the discrepancy between our two samples, it may undermine any claim that a search technique necessarily produces a more representative sample from a field of medical science simply because it identifies a greater number of RCT manuscripts.The two search techniques otherwise differ in several ways. For example, the PubMed MEDLINE search was designed so as to exclude any manuscripts published in a non-English language. This would approximate the typical literature search performed by many researchers in North America, and accordingly all the manuscripts identified by the PubMed MEDLINE search were readily available in local university libraries. Conversely, the CIG trials registry tended to include more references from the non-English language literature (n = 27 manuscripts after the screening process). This inclusiveness of the CIG trials registry seemed to account for the 15 irretrievable manuscripts listed by the CIG trials registry. It is reasonable to state that the non-English language literature is part of medical science and that it should not be discounted solely because of its country-of-origin or the language in which it was written. As another difference, the CIG trials registry involved hand-searches of journals and lists of references provided by authorities in the field of brain injury, which are not features of the PubMed MEDLINE search and which may predispose the CIG trials registry search technique toward recovering more recently-published manuscripts. Recently published manuscripts may be of higher quality, thereby biasing the longitudinal measurement of quality in the RCT manuscript sample provided by the CIG trials registry. Alternatively, such extra efforts would be considered by most to improve on the yield of a search technique by including journals and books that are not indexed by computerized databases.a priori that neither of the search techniques we used necessarily sampled RCTs from the field of brain injury in a representative manner. Furthermore, we do not claim to have accurately measured how the quality of RCT manuscripts is changing over time in the field of brain injury with either one of them. This is because we are not confident that either search technique provided a representative sampling of the field of brain injury . With regards to the field of brain injury, it is clear from our observations that neither the PubMed MEDLINE search nor the CIG Trials Registry can claim to be complete, as each search technique failed to identify a large number of RCT manuscripts that were found by the other search technique. In other fields of medical science there may be specialized databases or registries that claim to identify all relevant manuscripts . The authors of the current communication cannot understand how such a claim can be made or proven, since demonstrating the completeness of a search strategy would require proving that there are no relevant manuscripts that it does not identify. Ultimately, proving that something does not exist is scientifically impossible. Alternatively, it might be claimed that a search strategy identifies the majority of relevant manuscripts. This, of course, depends upon (1) the definition of a majority and (2) the assumption that the finding of even a few relevant manuscripts not identified by the search strategy means there are no other such manuscripts outside of the reach of that search strategy. Again, such a claim would depend upon the assumption that the inability to find further relevant manuscripts indicates that no further relevant manuscripts exist; as described above, this is a scientific impossibility. Rather than claiming perfection or near-perfection, it would seem to us to be more appropriate and accurate to claim that a given search strategy has exhausted all options for identifying relevant manuscripts.Arguments can be made that either of the search techniques provided a more representative sample of RCT manuscripts from the field of brain injury, but which search technique is superior – if either can be said to be so – is not a concern of the current study. It was solely our intention to compare the findings provided by two commonly-used search techniques to demonstrate that the search technique can in fact influence the measurement of the change in RCT quality over time. We acknowledge What, then, should be done to avoid a biasing influence related to the search technique during reviews of RCT quality over time? The simplest means of avoiding a such an influence would have apparently been to use multiple search techniques in order to better sample the parent population of RCT manuscripts in a field of medical science. In general, including multiple techniques into a single 'comprehensive' search would be preferable to a simple search involving only a single technique, but even so this does not ensure that the combination is truly comprehensive . Essentially this was the goal of the CIG trials registry, but even it did not completely encompass the sample of manuscripts identified by the PubMed MEDLINE search despite involving a MEDLINE search of its own. Similarly, previous reviews of RCT quality have often involved secondary searches following an initial computerized search, but such efforts certainly cannot match the breadth and thoroughness of that from the Cochrane Collaboration. If such reviews of RCT quality are to judge entire fields of medical science it would seem that the search techniques they employ must be shown to produce a representative sampling of the parent population of RCT manuscripts as well as a high yield from that parent population. We hope that the findings presented here bring more attention to this concern in future reviews of the change in RCT quality over time.We demonstrated that measuring the change in quality over time of a sample of RCT manuscripts from the field of brain injury can be greatly affected by the search technique. This poorly recognized factor may make measurements of the change in RCT quality over time within a given field of medical science unreliable. The search strategy should be accurately reported in any study that attempts to follow trends in the quality of RCT manuscripts over time, and its limitation in sampling the RCT manuscripts from a field of medical science should be acknowledged and evaluated.The author(s) declare that they have no competing interests.Both MKB and CY contributed equally to this study, and the exact nature of their contributions are described in the Methods section.The pre-publication history for this paper can be accessed here:


Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis.We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis.d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data. Moreover, the correlation between molecular and phenotypical data requires acquiring both with comparable profoundness leading to the development of large and small scale databases holding both information -3. In thIt is not the purpose of this paper to survey the various solutions available to query, visualize and mine data, but rather to illustrate how such concepts could be combined usefully within one software tool. Here, the layout should not only preserve the structure of the information, it should also convey the quality of the distribution of the values contained in the database. The features of the display should then be designed to highlight those regularities, patterns or dependencies that are not easily detectable with an ordinary front-end.One visual representation, which motivated the graphical display of the tool we describe here, is the data matrices handled in microarray studies, in which rows in the matrices typically represent genes and columns individual samples . Rather With d-matrix we propose a generic front-end solution capable of extracting, exploring, visualizing and analyzing complex data. The software can be interfaced with the most common relational database management systems without any intervention on the schema or pre-processing phase. As the name suggests, the visual model proposed has the form of a matrix. Its elements are boxes whose colors show the quality of the underlying information. The granularity of the data display allows consequent drill-down, i.e. the user is able to focus the observation on a single data point. In addition, value frequency bars are available to present compact overviews. It also offers the possibility to define categories using context-sensitive rules and to assign colors to classes. The direct implementation of a broad range of descriptive and advanced statistics together with a hierarchical sorting feature permits user-defined exploration of the data.The process of developing a uniform web interface for disparate data sources is a complex task because of the variability in the data models that underlie each source. To enable an effective two-dimensional display, the d-matrix model consists of a three-level tree. For the representation of a large database schema requiring a higher number of levels, several d-matrix instances can be built on the same database.Within the proposed model, the main table addressing the objects of a study is considered as the root, the first level of the tree. The second level consists of tables that are joined with the root by means of its primary key and the third level consists of tables that are further joined with the ones at level two. In particular, the dependency of the root table with the second level tables can be either one-to-many or one-to-one, while the dependency of the second level table with the third level ones can be either many-to-one or one-to-one. To apply different query and visualization rules each branch of this tree is defined as a data group characterized by the same storing strategy. In cases where Entity-Attribute-Value (EAV) tables are interfaced, the Entity must correspond to the main ID.As an example, we can refer to the CardioVascular Genetics database (CVGdb) schema set up by our group Figure . Here, wsummary node can be included in the query form. For each value of the x-axis, the values of the summary nodes are computed by counting the nonempty nodes in the respective folders.The schema is presented to the user in a structure recalling a file system selector Figure . Nodes rThe query process consists of two steps. First, the users select all nodes they want to be included in the query Figure ; second,The graphical output of d-matrix consists of two-dimensional matrices, whose colored boxes code the meaning of the underlying information, the description of the chosen nodes and a prospect of statistical evaluations Figure . The discases (and aggregations of them) are represented column-wise.In cases of single dependency each data point is represented by one box of the matrix. If there is a multiple dependency (two-table data groups), subsequently more rows for each value of a single node are displayed. EAV data groups can lead to both single or multiple dependency; in the second case the entries are aggregated in one matrix box. In Figure When the matrix oversize the available space, the use of two distinct scrollbars lets the user move the data matrix horizontally and vertically. The general overview is given together with the advantage of single-case addressability, i.e. each case (tuple) representation is entirely visible and its components clearly distinguishable.The display is obtained as a group of images (generated using the Perl GD module and stored as temporary files), each in a separate HTML DIV container, which can be moved independently.The matrix display represents a summarized view of the query. Each box holds three levels of detail: first, the coordinates that uniquely identify the box position and represent two units of information; second, the color that corresponds to either a single value or a category; third, the hidden content of the box obtained by drill-down, which gives all remaining information for that box.In the d-matrix display the drill-down can be obtained for each box in form of a pop-up window Figure . The conIt is possible to add further detail by the mean of hyperlinks to grant access to remote databases, external analysis results and multimedia documents Figure , or evenThe software requires four configuration files: the data definitions file that is needed to connect d-matrix with the relational schema, a database settings file storing the information to access the database, a color file for the definition of the colors used in the matrix and a general server settings file. Every configuration file is maintained as plain text to permit easy access and modification.definitional abstraction) [qualitative abstraction [The structure of the data definitions file must reflect the hierarchy in which the metadata  have to be organized on the screen, while its textual content depicts a level of abstraction (raction)  between traction , allows For each attribute value, value range or defined category, rules can be given to assign its respective color. This leads to a common method to visualize both discrete and continuous variables. In addition, categorized numeric values can be treated as categorical in specific contexts like sorting and statistics. Furthermore, colored boxes can be composed by combining the values of two nodes, which enables, for example, the visualization of both Alleles within horizontally split boxes for sequence variations Figure .Several data definitions files (each defining a separate d-matrix instance) can independently coexist on the same server for the same or different database systems and schemata.d-matrix permits consecutive data-filtering operations that – as a whole – can be seen as a single user-driven data mining session. A compact and information-dense graphical outcome, context-sensitive categorization, hierarchical sorting and drill-down enable this mining process. Frequency bars give an overview of the overall queried dataset whereas box plots improve the visual perception of the data distribution. A key feature within the mining process is the opportunity to obtain different views of a single data set rapidly in parallel using different browser windows. Here, the interactivity becomes a primary factor and is supported by the human-oriented representation.A wide range of descriptive statistics and statistical tests is directly accessible. This permits statistical evaluation of the correlation between attributes and determination whether it is reasonable or not to assume that a sample fits to a specific distribution. For numerical values it is possible to perform up to ten different statistical tests, while for non-numerical entities  the Chi-square and Fisher exact tests are available. The user interface automatically performs a selection of attributes and tests according to their respective compatibility.In addition to directly implemented tests, external data analysis environments like R  or user For interfacing d-matrix with the CVGdb, we assigned categories if appropriate and colors to more than 700 nodes. Figure Finally, the session explained is just one out of several examples in which d-matrix proved to be highly effective for the visualization of regularities and dependencies within the CVGdb data. Moreover, based on the general visualization concept, d-matrix provides an integration between clinical and genetic information that is crucial for the correlation of phenotypical and molecular data Figure .With respect to an ongoing project on gene regulation, we found it very convenient to visualize potential transcription factor binding sites (TFBS) in promotor sequences by interfacing d-matrix . Here, tTo demonstrate the versatility of the software, we further interfaced d-matrix with a database that represents the periodic table of the elements . AlthougThe interfacing with both dataset required only one working day for each.We have presented d-matrix, a non-task-specific database front-end with a new visualization strategy with embedded analysis features.The graphical outcome of d-matrix consists of colored boxes arranged in matrices; it permits single-case addressability with further drill-down capability. Together with the hierarchical sorting and statistical feedbacks, d-matrix enables consecutive data-filtering operations that – as a whole – can be considered as a single data mining session. Also, the result of such a session can be exported for further study. For a qualitative evaluation of d-matrix, one should not only focus the attention on the final display, which only represents the end product of a sequence of user-driven data exploration sessions. The high level of interactivity that our approach offers is indeed a primary factor; with d-matrix, the communication between human and computer is a rapid interaction.The future development of d-matrix will focus on the implementation of clustering algorithms to be executed before display. Furthermore, we envisage the design of instruments to inquire metadata to maximize the quantity of information that will be eventually displayed and analyzed . In addiAn inquiry of the solutions reported to date for data exploration, visualization and analysis resulted in an approximate distinction between reports about efforts for database development with their task specific front-end solutions and stand-alone data analysis, visualization and mining tools. In our view, d-matrix stands in between those two groups and aims to combine features of both efforts, which we believe can be very effective and useful in general and especially for the association of distinct data types such as phenotypical and molecular data. As a front-end, it does not require complex installation processes or maintenance, and it is suitable for multi-user remote access. As a visual data mining tool, it gives an effective display that allows the detection of exceptions, trends, regularities, clusters and dependencies, as well as incomplete or erroneous data.Project name: d-matrixProject home page: Operating system(s): Platform independentProgramming language: PerlOther requirements: d-matrix was successfully interfaced to Oracle 8i, MySQL, Microsoft Access and text-based databases and is compatible with recent JavaScript-enabled browsers.License: d-matrix is available on request from the author. To academic institutions d-matrix is available for a fee of 250 Euro that is intended to cover our costs of distribution and maintenance.DS developed the first generation of d-matrix and carried out the main programming work. RG is the current maintainer and carried out the main implementation. SM and HPS participated in the design, testing and quality control. HH participated in the conceptual design. SS conceived the development of d-matrix, managed and participated in its design and implementation.


Capacitation is an important physiological pre-requisite before the sperm cell can acrosome react and fertilize the oocyte. Recent reports from several laboratories have amply documented that the protein phosphorylation especially at tyrosine residues is one of the most important events that occur during capacitation. In this article, we have reviewed the data from our and other laboratories, and have constructed a heuristic model for the mechanisms and molecules involved in capacitation/acrosome reaction. The process of fertilization is characterized by a series of complex set of events. It involves a species-specific interaction between egg and sperm activating a chain of events that leads to formation of zygote, fetus and finally a baby. However, before a spermatozoon can fertilize an oocyte, it must undergo a cascade of biochemical and physiological changes that facilitates its binding and penetration into the oocyte ,2. This in vitro. In fact, most of the information about various aspects of sperm capacitation has emanated from in vitro studies. Sperm cells can be capacitated in vitro by using chemically defined media containing appropriate concentrations of electrolytes, metabolic energy sources, and serum albumin (cholesterol acceptor). Although minor variations exist between these media depending on the mammalian species, most of these media contain bicarbonate, calcium and a macromolecule predominantly serum albumin.Capacitation confers upon the spermatozoon an ability to gain hyperactive motility, interact with oocyte zona pellucida (ZP), undergo acrosome reaction and initiate oocyte plasma membrane fusion . CapacitAlthough capacitation of a sperm cell is required before fertilization virtually in every mammalian species studied, the molecular mechanisms and signal transduction pathways involved in this process are not clearly understood. Capacitation involves an increase in membrane fluidity, cholesterol efflux, ion fluxes resulting in alteration of sperm membrane potential, increased tyrosine phosphorylation of proteins, induction of hyperactivation and the acrosome reaction. Protein phosphorylation represents a very important aspect of capacitation. Recently, many advances have been made in the study of phosphorylation of proteins during capacitation, which we will review in this article.Phosphorylation of proteins is a post-translational modification event that acts as one of the cell's regulatory mechanisms to control various processes such as cellular growth, cell cycle control, cytoskeleton assembly, ionic current modulation, and receptor regulation ,6. In faMature spermatozoa are terminally differentiated and specialized cells. They are highly compartmentalized but are devoid of any major transcriptional and translational activity. Therefore one can justify the importance of post-translational modifications such as protein phosphorylation/dephosphorylation in regulating important phenomena such as sperm capacitation, hyperactive motility and acrosome reaction, which are required for the spermatozoon to reach, bind, penetrate and fuse with the oocyte. The phosphorylation/dephosphorylation state of phosphoproteins is controlled by the activity of protein kinases and phosphatases, and the counteracting activities of these kinases and phosphatases provide cells with a "switch" that can turn on or turn off the function of various proteins.Earlier studies reported the presence of various phosphoproteins, protein kinases and protein phosphatases in mammalian spermatozoa and implicated their role in sperm motility acquisition, capacitation and acrosome reaction ,8. Phosp32P metabolic labeling and in vitro kinase assays, human sperm was found to have at least seven proteins (200, 112, 104, 48, 42, 31 and 25 kDa) that are phosphorylated and fourteen proteins (122, 105, 95, 89, 73, 62, 48, 46, 40, 33, 30, 28, 25 and 22 kDa) that are autophosphorylated [Historically, in 1989, Leyton and Saling provided the first evidence for the presence of tyrosine phosphorylation in mammalian spermatozoa namely the mouse sperm  and alsoorylated . FA-1 anorylated ,15. Treaet al [It has been observed that different compartments of human spermatozoa undergo a specific sequence of phosphorylation during capacitation and upon binding to zona pellucida . In ordeet al  localizeet al . The incet al . A signiet al ,19.1 from a predominantly cytosolic localization to the membrane-bound activity, followed by phosphorylation at tyrosine residues. Since the acrosomal region of the sperm cell is involved in sperm-zona interaction, the shift in phosphotyrosine-specific fluorescence seems to have a physiological significance [A study from our laboratory using human sperm revealed that the capacitating conditions and zona exposure increases the degree of tyrosine phosphorylation per sperm cell as well as the number of sperm cells that were phosphorylated, especially in the acrosomal regions of the sperm head . Interesificance .2+ in the signal transduction pathway. A 55 kDa tyrosine phosphorylated protein has been linked to motility in bovine spermatozoa [Tyrosine phosphorylation of the sperm flagellar proteins has shown to be related to the acquisition of the hyperactive motility ,24, whicrmatozoa . During rmatozoa . HSP-90 rmatozoa .et al [In boar spermatozoa, capacitation induces tyrosine phosphorylation of plasma membrane proteins, which are believed to initiate binding to the zona pellucida and induce acrosome reaction . It has et al  have exaet al . On a siet al .in vitro modifies both the sperm tyrosine phosphorylation and capacitation. The selective sperm binding to oviductal epithelial cells [It is interesting to know that sperm-oviductal epithelial cell interaction al cells  suppressal cells  and canial cells  spermatoet al [Akap4 gene causes defects in sperm flagellum and motility. In the mice lacking AKAP4 protein, that undergoes phosphorylation during capacitation, sperm numbers were not reduced but the spermatozoa failed to show progressive motility, rendering the male mice infertile [In order to understand the molecular basis underlying capacitation, it is very important to characterize phosphoproteins involved in the signal transduction pathways. Although a large number of proteins have been reported to be tyrosine phosphorylated, very few have been characterized so far. Most of the studies at the beginning used specific inhibitors and/or phospho-specific antibodies to delineate phosphoproteins in sperm cell. Mass-spectrometric analysis provides another powerful approach to identify and characterize these phosphorylated proteins. Ficarro et al  used thiet al . Also, tnfertile . The micnfertile . It woulet al [Several studies have correlated the degree of tyrosine phosphorylation with the capacitative state of spermatozoa. Visconti et al ,39 examiet al . Later set al ,41, catset al , pigs [4et al , boar [4et al , bovine et al ,46, equiet al , cynomolet al , tammar et al  and humaet al ,50. All The increase in protein tyrosine phosphorylation during capacitation has been shown to be regulated by a cAMP-dependent pathway involving protein kinase A (PKA) in sperm of various species including mouse , hamsterPKA is a tetrameric enzyme composed of two regulatory and two catalytic subunits. The activity of PKA is dependent on the activities of adenylate cyclase and phosphodiesterase. In sperm cell, PKA is compartmentalized thus ensuring specificity of function through binding of its regulatory subunit to the AKAP family of proteins. Different types of AKAPs have been characterized from sperm of different species ,52,53. Psrc-related protein tyrosine kinase, c-yes  has been detected in the human sperm head [yes belongs to PTK family. The activity of the c-yes kinase depends on cAMP, indicating again that tyrosine phosphorylation of proteins in sperm cell is a result of the cross-talk between the cAMP pathway and tyrosine kinase(s). The protein kinase C (PKC) is also present in mammalian spermatozoa and its role has been implicated in sperm motility and acrosome reaction [Presence of various tyrosine kinases (RTKs and PTKs) has been demonstrated in spermatozoa of several mammalian species. These include c-ras in human sperm cells , EGF recerm head . The c-yreaction , but itsThe sperm proteins also undergo phosphorylation at ser/thr residues. Studies have shown capacitation-associated increase in phosphorylated ser/thr residues in human  and hamsThere is evidence that the mitogen-activated protein kinases (MAPKs), also known as extracellular signal-regulated kinases (ERKs), are present in spermatozoa and are involved in sperm motility and capacitation ,67 and aThe MAPK isoform ERK2 , the adaThe lipid composition of sperm plasma membrane is different from that of somatic cell plasma membrane. The plasma membrane of sperm head has high amounts of cholesterol, which regulates the membrane fluidity and plays an important role of capacitation . The cho2+ efflux can increase the permeability of sperm cell plasma membrane to various ions such as Ca2+ and HCO3-, which can then affect the downstream signaling molecules.Cholesterol efflux from sperm plasma membrane is associated with the activation of membrane signal transduction pathways related to capacitation . β-Cyclo2+ influx is one of the crucial biochemical events that occur during capacitation. There is abundant evidence to support requirement of Ca2+ for capacitation [2+ in sperm cell during capacitation in several mammalian species [2+ during capacitation. There is also evidence that different aspects of human sperm function such as capacitation, acrosome reaction and ZP binding in vitro has distinctive Ca2+ requirements. Experiments have shown that in humans 0.22 mM of Ca2+ ions are needed for capacitation while ≥ 0.58 mM is required for AR and ZP binding [2+ is needed to achieve sperm capacitation in the mouse [Cacitation ,78 and icitation . It has  species -85. It h binding . Althoughe mouse , millimohe mouse .2+ on tyrosine phosphorylation in spermatozoa. The reports on mouse [2+ increase tyrosine phosphorylation. In contrast, other studies have demonstrated the opposite effect [2+ negatively regulates tyrosine phosphorylation during sperm capacitation. A recent study was conducted by Baker et al [2+ on tyrosine phosphorylation of human and mouse spermatozoa and delineate the mechanisms by which this cation exerts its regulatory effect. They reported that Ca2+ suppresses tyrosine phosphorylation by decreasing the availability of intracellular ATP [2+ during capacitation of sperm of several mammalian species [Contrasting reports exist on the impact of extracellular Caon mouse  and humaon mouse  have doce effect ,89, indier et al  to invesular ATP . Since t species , it coul3- in capacitation is well elucidated [3- regulates capacitation is not clear. However, the HCO3- influx has been associated with an increase in intracellular pH observed during capacitation, regulation of cAMP levels, reversible change in the lipid architecture of plasma membrane, and hyperpolarization of sperm plasma membrane.The requirement of for HCOucidated ,92. The +/HCO3- cotransporter has been demonstrated in the mouse sperm and it plays a significant role in capacitation [3- influx [3- may be involved in stimulation of adenylate cyclase activity rather than modulation of pH [3- induces rapid changes in plasma membrane lipid architecture and in motility by a cAMP/PKA-dependent pathway in boar spermatozoa [et al [3- in protein tyrosine phosphorylation in sperm during capacitation and fertilization. It is interesting that HCO3- levels are low in epididymis and high in seminal plasma and oviduct [3- in the male and female reproductive tracts could play an important role in the suppression of capacitation in the epididymis and the promotion of this process in vivo in the female reproductive tract. It is observed that a protein, designated as CRISP-1, that is added to the sperm surface in epididymis is lost during capacitation [The presence of a Nacitation . An incr- influx . However- influx . HCO3- mon of pH ,96. HCO3rmatozoa . Harrisormatozoa  and Da Ra [et al  have sho oviduct . These ccitation . Additioin vitro addition of HCO3- on intracellular cAMP production and protein tyrosine phosphorylation in human spermatozoa [3- in the medium resulted in a significant increase in sperm motility as well as several hyperactivation parameters, mediated by an increase in cAMP production and tyrosine phosphorylation of AKAP3. The effects disappeared with the addition of 4,4'-diisothiocyanostilbene-2, 2'-disulfonic acid, which is a known inhibitor of bicarbonate transport. It was observed that both tyrosine phosphorylation of AKAP3 and sperm motility were blunted by the soluble adenylate cyclase (sAC) inhibitor 2OH-estradiol. These findings indicate that HCO3- stimulates human sperm motility and hyperactivation through activation of sAC and tyrosine phosphorylation of AKAP3, causing an increased recruitment of PKA to AKAP3 [A recent study investigated the effect of rmatozoa . The addto AKAP3 .2O2 and superoxide anion are involved in the regulation of human sperm capacitation and protein tyrosine phosphorylation [et al [2O2 activates adenylate cyclase to produce cAMP, leading to PKA-dependent protein tyrosine phosphorylation. Since adenylate cyclase present in sperm is activated by HCO3-, they proposed that H2O2 and HCO3- can act in concert or partially substitute each other [2O2 are beneficial, high concentrations can lead to sperm immobilization and death. It has been proposed that the accurate balance of amount of ROS produced and scavenged at any moment determines whether a sperm function will be promoted or jeopardized. Nicotinamide adenine dinucleotide (NADH) and NADPH are also known to promote sperm capacitation [2O2 by superoxide dismutase. The inhibition of lactate dehydrogenase (LDH)-C4 blocks capacitation of mouse spermatozoa in vitro [In vitro studies have shown that low concentration of NO enhances the acrosome reaction of mouse [Studies have suggested that ROS such as Hrylation ,102. Somrylation , while orylation . In a ren [et al  suggestech other . Howevercitation . It has citation  and promin vitro . LDH-C4 in vitro . NADH-diin vitro . The frein vitro . Non-capin vitro . It has in vitro . In vitrof mouse  and bullof mouse  spermatoof mouse .2+ channel (PR1), a membrane-associated protein tyrosine kinase , and a plasma membrane chloride channel (PR3) [2+ influx in the human spermatozoa through PR1 [Progesterone has been reported to affect several sperm functions especially capacitation, motility and acrosome reaction . The effel (PR3) . The proough PR1  and voltough PR1 . In humaough PR1 ,118 causough PR1  with an ough PR1 . The tyrough PR1 . Progestough PR1 .in vitro effect of GABA was studied on bovine spermatozoa capacitation [2+ and cAMP levels was induced by GABA, and the GABA A-R antagonists, bicuculline or picrotoxin abolished the effect [2+, cAMP and tyrosine phosphorylation.Recently GABA has emerged as a putative modulator of sperm function. GABA is the most widely distributed inhibitory neurotransmitter in vertebrate central nervous system. Three types of membrane receptors (A, B and C) mediate the inhibitory action of GABA. GABA A-receptor has been identified in human spermatozoa. In a recent study, the citation . It was e effect . Thus, GAII is known to be present in seminal plasma and it modulates the adenylate cyclase (AC)/cAMP signal transduction pathway . The exiCytokines are a family of polypeptide hormones produced primarily by the cells of the immune system in response to various stimuli including foreign antigens. Although originally identified from the immune cells, the non-immune cells also secrete them. The cytokines can have both positive and negative effects on a variety of cell types and tissues. Cytokines are present in circulation and local genital tract secretions in both men and women ,125. TheBefore fertilization can occur, spermatozoon undergoes a series of changes to acquire the ability to bind and penetrate the oocyte. These events are regulated by the activation of intracellular signaling pathways involving various molecules such as cAMP, protein kinase A, receptor tyrosine kinases, and non-receptor tyrosine kinases. A number of molecules have been identified, that regulate these pathways such as calcium, bicarbonate, ROS, GABA, progesterone, angiotensin, and cytokines. Studies have shown that phosphorylation of sperm proteins is an important aspect of capacitation and has been shown to be associated with hyperactivated motility, zona pellucida binding and acrosome reaction. Extensive research has started to elucidate various pathways involved in protein phosphorylation during sperm capacitation. Presence of three major pathways involving cAMP/PKA, receptor tyrosine kinases, and non-receptor protein tyrosine kinases have been shown. These pathways are not mutually exclusive and may involve cross-talk among several molecules. The exact pathways have not been clearly delineated. Several questions such as how does the stimulation of cAMP/PKA pathway upregulate tyrosine phosphorylation are still to be answered. Many components of these pathways and several phosphoproteins remain to be identified. The novel technologies such as gene knockout technique will help us to elucidate the key molecules in these pathways. Based upon the present data, we have constructed a heuristic model that is shown in figure


Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, high-throughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression.The results of a small-scale analysis indicate that the regression coefficients reflect the connectivity of the network quite well. Using the mathematical modeling framework of Biochemical Systems Theory (BST), we also show that the regression coefficients may be translated into constraints on the parameter values of the nonlinear BST model, thereby reducing the parameter search space considerably.The proposed method provides a good approach for obtaining a preliminary network structure from dense time series. This will be more valuable as the systems become larger, because preprocessing and effective priming can significantly limit the search space of parameters defining the network connectivity, thereby facilitating the nonlinear estimation task. The intriguing aspect of profiles is that they implicitly contain information about the dynamics and regulation of the pathway or network from which the data were obtained. The challenge for the mathematical modeler is thus to develop methods that extract this information and lead to insights about the underlying pathway or network.The rapid development of experimental tools like nuclear magnetic resonance (NMR), mass spectrometry (MS), tissue array analysis, phosphorylation of protein kinases, and fluorescence labeling combined with autoradiography on two-dimensional gels promises unprecedented, powerful strategies for the identification of the structure of metabolic and proteomic networks. What is common to these techniques is that they allow simultaneous measurements of multiple metabolites or proteins. At present, these types of measurements are in their infancy and typically limited to snapshots of many metabolites at one time point , to swith NMR ,4, 2-d gwith NMR  or protewith NMR ), or to with NMR  that peret al. [i.e., the maximum deviation from steady state) as well as the slopes of the traces at the initial phase of the response. Torralba et al. [in vitro glycolytic system. Similarly, by studying a large number of perturbations, Samoilov et al. [In simple cases, the extraction of information can be accomplished to some degree by direct observation and interpretation of the shape of profiles. For instance, assuming a pulse perturbation from a stable steady state, Vance et al.  present a et al.  recentlyv et al.  showed tFor larger and more complex systems, simple inspection of peaks and initial slopes is not feasible. Instead, the extraction of information from profiles requires two components. One is of a mathematical nature and consists of the need for a model structure that is believed to have the capability of capturing the dynamics of the underlying network structure with sufficient accuracy. The second is computational and consists of fitting this model to the observed data. Given these two components along with profile data, the inference of a network is in principle a regression problem, where the aim is minimization of the distance between the model and the data. If a linear model is deemed appropriate for the given data, this process is indeed trivial, because it simply requires multivariate linear regression, which is straightforward even in high-dimensional cases. However, linear models are seldom valid as representations of biological data, and the alternative of a nonlinear model poses several taxing challenges.canonical forms, which are nonlinear structures that conceptually resemble the unalterable linear systems models, but are nonlinear. Canonical models have in common that they always have the same mathematical structure, no matter what the application area is. They also have a number of desirable features, which include the ability to capture a wide variety of behaviors, minimal requirements for a priori information, clearly defined relationships between network characteristics and parameters, and greatly enhanced facility for customized analysis.First, in contrast to linear models, there are infinite possibilities for nonlinear model structures. In specific cases, the subject area from which the data were obtained may suggest particular models, such as a logistic function for bacterial growth, but in a generic sense there are hardly any guidelines that would help with model selection. One strategy for tackling this problem is the use of The best-known examples of nonlinear canonical forms are Lotka-Volterra models , their The strict focus on two-component interactions in LV models has substantial mathematical advantages, but it has proven less convenient for the representation of metabolic pathways, where individual reaction steps depend on the substrate, but not necessarily on the product of the reaction, or are affected by more than two variables. A simple example of the latter is a bi-substrate reaction that also depends on enzyme activity, a co-factor and possibly on inhibition or modulation by some other metabolite in the system. These types of processes have been modeled very successfully with GMA and S-systems. Between these two forms, the S-system representation has unique advantages for system identification from profiles, as was shown elsewhere -24 and wcf. [The inference of a nonlinear model structure from experimental data is in principle a straightforward "inverse problem" that should be solvable with a regression method that minimizes the residual error between model and data. In practice, however, this process is everything but trivial (cf. ) as it acf. , similarcf. , mutual cf. , and gencf. . An indicf. . The alget al. [Nonlinear estimation methods have been studied for a long time, and while computational and algorithmic efficiency will continue to increase, the combinatorial explosion of the number of parameters in systems with increasingly more variables mandates that identification tasks be made easier if larger systems are to be identified. One important possibility, which we pursue here, is to prime the iterative search with high-quality starting conditions that are better than naïve defaults. Clearly, if it is possible to identify parameter guesses that are relatively close to the true, yet unknown solution, the algorithm is less likely to get trapped in suboptimal local minima. We are proposing here to obtain such initial guesses by preprocessing the temporal profile data and fitting them preliminarily by straightforward multivariate linear regression. The underlying assumption is that the structural and regulatory connectivity of the network will be reflected, at least qualitatively, in the regression coefficients. D'haeseleer et al.  exploredet al. [et al. [e.g., [et al. [Several other groups have recently begun to target network identification tasks with rather diverse strategies. Chevalier et al.  and Diazet al. ,34 propo [et al.  recently. [e.g., ,36) that. [e.g.,  used a n [et al.  used neun species can often be represented by a system of nonlinear differential equations of the generic formThe behavior of a biochemical network with X is a vector of variables Xi, i = 1, ..., n, f is a vector of nonlinear functions fi, and μ is a set of parameters. If the mathematical structure of the functions fi is known, the identification of the network consists of the numerical estimation of μ. In addition to the challenges associated with nonlinear searches mentioned above, this estimation requires numerical integration of the differential equations in (1) at every step of the search. This is a costly process, requiring in excess of 95% of the total search time; if the differential equations are stiff, this percentage approaches 100% [n differential equations, and if measurements are available at N time points, the decoupling leads to n × N algebraic equations of the formwhere hes 100% . A simplhes 100% ,17. Thusμij are the "unknowns" that need to be identified.It may be surprising at first that it is valid to decouple the tightly coupled system of nonlinear differential equations. Indeed, this is only justified for the purpose of parameter estimation, where the decoupled algebraic equations simply provide numerical values of variables (metabolites or proteins) and slopes at a finite set of discrete time points. The experimental measurements thus serve as the "data points," while the parameters a priori not trivial. However, we have recently shown [The quality of this decoupling approach is largely dependent on an efficient and accurate estimation of slopes from the data. Since the data must be expected to contain noise, this estimation is ly shown ,39 that f in Eq. (1) about one or several reference states. As long as the system stays close to the given reference state(s), this linearization is a suitable and valid approximation. We consider four options: (I) linearization of absolute deviations from steady state; (II) linearization of relative deviations from steady state; (III) piecewise linearization; and (IV) Lotka-Volterra linearization.The smoothing and decoupling approach reduces the cost of finding a numerical solution of the estimation task considerably. Nonetheless, algorithmic issues associated with local minima and the lack of convergence persist and can only be ameliorated with good initial guesses. To this end, we linearize the model zi = Xi - Xir, where Xir denotes the value at a reference state of choice. If the reference state is chosen at a stable steady state, the first-order Taylor-approximation is given byOption (I) is based on deviations of the type A is the n × n Jacobian with elements aij = (dfi / dXj) calculated at Xr .where  Xr cf. -34). If . If A isui = zi/Xir. At a steady state, this yields the linear systemFor option II, we define a new variable A' is an n × n matrix in which a'ij = (Xjr / Xir)·aij.where f, a perturbation of this magnitude may already lead to appreciable approximation errors. While this is a valid argument, it must be kept in mind that the purpose of this priming step is simply to detect the topological structure of connectivity and not necessarily to estimate precise values of interaction parameters. Simulations (see below) seem to indicate that this detection is indeed feasible in many cases, even if the deviations are relatively large.A general concern regarding linearization procedures is the range of validity of sufficiently accurate representation, which is impossible to define generically. From an experimental point of view, the perturbations from steady state must be large enough to yield measurable responses. This may require that they be at the order of 10% or more. Depending on the nonlinearities in ai0, which is equal to fi(Xr). The choice of subsets and operating points offers further options. In the analysis below, we use the locations of extreme values (maximum deviation from steady state) of the variables as the breakpoints between different subsets. Thus, a variable with a maximum and a later minimum has its time course divided into three subsets.In order to overcome the limitation of small perturbations, a piecewise linear regression (option III) may be a superior alternative. In this case, we subdivide the dataset into appropriate time intervals and linearize the system around a chosen state within each subset. Most  reference states are now different from the steady state, with the consequence that Eq. (3) has a constant term Xi and Xj is assumed to be proportional to the product XiXj [Xi isThe fourth alternative (option IV) is a Lotka-Volterra linearization. In a Lotka-Volterra model, the interaction between two species uct XiXj . FurtherXi, which is usually valid in biochemical and proteomic systems, because all quantities of interest are non-zero. Thus, the differentials are again replaced by estimated slopes, the slopes are divided by the corresponding variable at each time point, and fitting the nonlinear LV model to the time profiles becomes a matter of linear regression that does not even require the choice of a reference state. The quality of this procedure is thus solely dependent on the quality of the data and ability of the LV model to capture the dynamics of the observed network. It is known  that thatXi, i.e., vij's and wi's, or αij's). The response variable is the rate of change of a metabolite, while the predictors are the concentrations of each metabolite in the network. The different linearization models (I-IV) differ in the transformations of the original datasets, which are summarized in Table yi =  /Xir, and the predictor variables are transformed as xi = (Xi - Xir)/Xir.No matter which option is chosen, the next step of the analysis consists of subjecting all measured time traces to multivariate linear regression and solving for the regression coefficients (Xj affects the dynamics (slope) of another metabolite Xi. In particular, a coefficient that is zero or close to zero signals that there is no significant effect of Xj on the slope of Xi. By the same token, a coefficient that is significantly different from zero suggests the presence of an effect, and its value tends to reflect the strength and direction of the interaction. In either case, the coefficients computed from the linear regression provide valuable insight into the connectivity of the network. Furthermore, the estimated coefficients provide constraints on the parameter values of the desired nonlinear model f. Indeed, if f consists of an S-system model, the coefficients estimated from the regression can be converted into combinations of S-system parameters, as is demonstrated in the following theoretical section and illustrated later with a specific example.The result of the regression is a matrix of coefficients that indicate to what degree a metabolite f in Eq. (1) if this model has the form of an S-system. To determine the relationships between the regression coefficients and the parameters of the S-system, it is convenient to work backwards by computing the different types of linearizations discussed before for the particular case of S-system models. This derivation is simply a matter of applying Taylor's theorem.The regression analysis yields coefficients that offer information on the connectivity of the network of interest. It also provides clues about the parameter values of the underlying nonlinear network model In the S-system formalism, the rate of change in each pool (variable) is represented as the difference between influx into the pool and efflux out of the pool. Each term is approximated by a product of power-law functions, so that the generic form of any S-system model isn is the number of state variables [gij and hij are called kinetic orders and describe the quantitative effect of Xj on the production or degradation of Xi, respectively. A kinetic order of zero implies that the corresponding variable Xj does not have an effect on Xi. If the kinetic order is positive, the effect is activating or augmenting, and if it is negative, the effect is inhibiting. The multipliers α i and β i are rate constants that quantify the turnover rate of the production or degradation, respectively.where ariables ,14. The zi = Xi - Xis, where the subscript s denotes the value of the variable at steady state, then leads directly toIf the Taylor linearization is performed at a steady state, the production term of the S-system model equals the degradation term. The absolute deviation of the first option, wherecij = gij - hij,cf. [Fij are always non-negative, while cij may be either positive or negative depending on the relationship between Xi and Xj. A common scenario is that a variable Xj influences either the production or degradation of variable Xi, but not both. In this case, a positive (negative) cij implies activation (inhibition) of production or inhibition (activation) of degradation. The special case of cij = 0 permits two possible interpretations: 1) gij = hij = 0, which implies that Xj has no effect on either production or degradation of Xi; or 2) gij = hij ≠ 0, which means that Xj has the same effect on both production and degradation of Xi. The former case is the more likely, but there are examples where the latter may be true as well, and this is indeed the case in the small gene network in Figure (cf. ). The soaij in Eq. (3) corresponds to the product of Fij and cij:Comparing the expression in Eq. (6) with the linear regression results, one sees immediately that each coefficient aij = Fijcij.     (7)aij have been estimated, the parameters of the corresponding S-system are constrained – though not fully determined – by Eq. (7). In particular, Eq. (7) does not allow a distinction between various combinations of gij and hij, as long as the two have the same difference. For instance, re-interpreting the regression coefficients as S-system parameters does not differentiate between the overall absence of effect of Xj on Xi (gij = hij = 0) and the same effect of Xj on both the production and degradation of Xi (gij = hij ≠ 0). This observation is related to the observation of Sorribas and Cascante [Thus, once the regression has been performed and the coefficients Cascante  that steui = (Xi - Xis) / Xis, in option II, are assessed in an analogous fashion. In this case one obtainsRelative deviations from steady state, wherecij = gij - hij,Fi are positive, while cij may be either positive or negative.. Again, The piecewise linear model for an S-system is easily derived as well. It is given asXjr denotes the value of the variable at the reference state. This case also includes the situation of a single approximation, which however is not necessarily based on a steady-state operating point.where Xi and then linearizing around an operating point. The resulting expressions become especially simple if this point is chosen as the steady state. In this case, the relationship between the parameters of the LV system and the S-system areIn the case of the Lotka-Volterra linearization, the correspondence between computed regression coefficients and S-system parameters is determined most easily by dividing the S-system equations by the corresponding cij = gij - hij.where et al. [We applied the methods described in the previous sections to simulated time profiles obtained from the small gene network in Figure et al.  used it et al. ,39.et al. [To generate time profiles, the system was implemented with the parameter values published by Hlavacek and Savageau , and as et al. , the modet al. [i.e., the maximum deviations from steady state) of the various variables both in time and size is in accordance with their "topological distance" from the perturbed variable, and variables not directly affected by the perturbed variable have zero initial slopes. As an example, the effect of a perturbation in X3 is shown in Figure X1 and X4 reaching their maximal deviation from steady state before X2 and X5, suggesting that X1 and X4 precede X2 and X5 in the pathway. The value of the initial slope is different from zero for X1 and X4, implying that these variables are directly affected by X3, whereas X2 and X5 have zero initial slopes suggesting that their responses are mediated through other variables.Quasi as a pre-analysis, we examined the guidelines proposed by Vance et al. . Indeed,X2 on X3. This result is not surprising, because the effect of X2 is the same on both the production and degradation of X3, which leads to cancellation. It is noted that this analysis does not necessarily distinguish between transfer of mass and a positive modulation, because both result in a positive effect on a variable. In a realistic situation, biological knowledge may exclude one of the two options, as in this case, where modulation is the only possibility for the effect of X3 on both X1 and X4, because the former is a protein and the latter are RNA transcripts. For the mathematical model in the S-system form, this is not an issue, as both types of influence are included in the equations in the same way (as a positive kinetic order).Maximal information about the network is obtained when every variable is perturbed sequentially. Experimentally, such perturbations could be implemented with modern methods of RNA interference  or, for While Vance's method works well in this simple noise-free system, it is not scalable to larger and more complex systems. The next step of our analysis is therefore regression according to the four options presented above and with a number of simulated datasets of the gene network that differ in the variable to be perturbed and the size of the perturbation. Because the illustration here uses a known model and artificial data, it is easy to compute the true regression coefficients through differentiation of the S-system model. These coefficients can be used as a reference for comparisons with coefficients computed from the entire time traces, which mimics the estimation process for (smoothed) actual data.The results for three of the options (I, II and IV) can be summarized in the following three points, while the piecewise linear model will be discussed afterwards.The network connectivity is reflected in the values of the regression coefficients. The values of the estimated coefficients provide strong indication as to which variables have a significant influence on the dynamics of other variables. A comparison between computed and estimated coefficients is shown in Table a12 and a24) are not estimated as exactly zero, but their values are at least one order of magnitude smaller than the coefficients that are in actuality not zero. Table X3 and X4. A possible explanation for X3 is that the effect of X2 is present in the non-linear system, but not in the linear system, and thus the behavior of X3 must be explained by the other variables. Overall, of the 25 theoretically possible connections, 76% are correctly identified, while 24 % are false positives.(1) The different linear models give  the same results. A comparison of the results of the three models reveals that the values of the regression coefficients are very similar  The greater the perturbation, the less accurate is the estimation of the regression coefficients. The deviation between the estimated and computed coefficients increases as the size of the perturbation increases (see Table (3) t = 0 to the time of the first extreme value for a given variable . For the perturbed variable  the first limit point was given by the smallest of the limit points of the other variables. The second interval contained the data points from the first to the second extreme value (a minimum), while the third interval included the remaining data points. The midpoint of each interval was taken to be the reference state. The result of the piecewise linear regression for a 5% deviation in X3 is given in Table X3 in the two last subsets reflect the variable's connectivity to a much greater extent than the other linearization approaches. As the reference state is different from the steady state, the effect of X2 is present in the linear system as well, and thus there is no compensation through the other variables. Another benefit is that the piecewise model tolerates larger perturbations. Even for a two-fold perturbation, the fraction of correctly identified coefficients in the last subset is 84%.The piecewise linear model was obtained by dividing the whole dataset into three smaller subsets for each variable. The first interval contained the data points from X3 is identified correctly from comparing the six models. The classification of the remaining four connections varies greatly among the different models, and it is therefore impossible to deduce a type of interaction with sufficient reliability.If we compare the results of all four linearized models, the degree of similarity may provide a measure of how reliable the estimated coefficients are, assuming that an interaction identified in all models is more reliable than an interaction identified in only one or few of the models. Considering the piecewise linear model as three models, yielding a total of 6 models from one dataset, one may thus determine the most likely connectivity for the small gene network. The result is presented in Table X1. Table X1, X3 and X5. If so, the linear model in Eq. (8) suggests the following:In addition to reflecting the connectivity, the coefficients provide likely parameter ranges or likely constraints on parameter values of the true model. As an example, consider variable  and the regression coefficients (aij) are taken from the model in Eq. (4). The values of the variables at steady state are known. Because the kinetic orders may be positive or negative and the cij may result from different combinations of gij's and hij's, it is not possible to deduce directly which exponent is greater than the other. However, in many cases one may have additional information on the system, which further limits the degrees of freedom (e.g., [ must be satisfied and provides yet another constraint.where m (e.g., ). In addIdentifying the structure of metabolic or proteomic networks from time series is a task that most likely will require large, parallelized computational effort. The search space for the algorithms is typically of high dimension and unknown structure and very often contains numerous local minima. This generic and frequent problem may be ameliorated if the search algorithm is provided with good initial guesses and/or constraints on admissible parameter values. Here, we have shown that linear regression may provide such information directly from the types of data to be expected from future experiments. For illustrative purposes, we used artificial data from a known network, but all methods are directly applicable to actual profile data and scaleable to large systems.The coefficients estimated from the different regressions reflect the effect of one variable on another surprisingly well and thus provide a simple fashion of prescreening the connectivity of the network. In addition, the estimated coefficients provide constraints on the parameter values, if the alleged nonlinear model has the form of an S-system. To explore the pre-assessment of data as fully as feasible, we studied four linearization strategies: using an absolute deviation from steady state; a relative deviation from steady state; piecewise linearization; and Lotka-Volterra linearization. Interestingly, all models gave qualitatively similar results for the analyzed example, and this degree of similarity may provide a measure of how reliable the identified connections are. Specifically, of the 25 possible connections in the small gene network studied, 19 were identified correctly in at least 83 % of the regression analyses.A concern of any linearization approach is the validity of the linear approximation. However, as long as the perturbation from steady state remains relatively small, the estimated linear model is likely to be a good fit of the actual nonlinear model, at least qualitatively. This limitation may furthermore be alleviated by fitting the profile data in a piecewise linear fashion. As most reference states in this case are different from the steady state, this strategy has the added benefit that more of the true relationships within the nonlinear model are likely to be preserved. As an alternative, one could explore the performance of the so-called "log-linear" model, which is linear in log-transformed variables .The Lotka-Volterra linearization did not perform as well as expected with regard to large perturbations. This may be a consequence of the particular example, which was originally in S-system form rather than in a form more conducive to the LV structure, which emphasizes interactions between pairs of variables. Since it is easy to perform the LV analysis along with the other regressions discussed here, it may be advisable to execute all four analyses.The illustrative model used for testing the procedure consisted of a relatively small system with only five variables and relatively few interactions. Nonetheless, one should recall that this very system required substantial identification time in a direct estimation approach . In ordeNone declared.SRV performed the analysis and prepared the results. JS developed and implemented the neural network for computation of slopes. EOV developed the basic ideas and directed the project.universal function which smoothes the data with predetermined precision and also allows the straightforward computation of slopes that can be used for network identification purposes. This appendix briefly outlines the procedure; details can be found in Almeida [It was recently shown that good parameter estimates of S-system models from metabolic profiles might be obtained by training an artificial neural network (ANN) directly with the experimental data. The result of this training is a so-called  Almeida  and Voit Almeida . The ANN Almeida .Noise and sample size do not have a devastating effect on the results of the ANN-method, as long as the true trend is well represented . In factThe use of the entire time course is in stark contrast to earlier methods of parameter estimation and structure identification in metabolic networks. Mendes and Kell  applied et al. [Chevalier and co-workers  first fiet al.  suggestez(tk + 1) = z(tk)exp(h·A),     (A1)h is the step size. The problem is thereby reduced to a mulitilinear regression in which the matrix Φ = exp(h·A) is the output. Instead of estimating the slopes, they obtain the Jacobian directly by  expanded in its Taylor-series. This approach yields a faster convergence to the elements of the Jacobian than the one suggested by Chevalier et al. [where r et al. , but theOur approach takes advantage of the entire time course and is therefore less sensitive to the particularities of assessing a system at a single point. The ANN itself does not provide much insight, because it is strictly a black-box model, but it is a valuable tool for controlling problems that are germane to any data analysis, namely noise, measurement inaccuracies, and missing data.


To investigate potential mechanisms for telomere capture the spatial arrangement of telomeres and chromosomes was examined in G1 (non-cycling) mitotic cells with diploid or triploid genomes. This was examined firstly by directly labelling the respective short arm (p) and long arm subtelomeres (q) with different fluorophores and probing cell preparations using a number of subtelomere probe pairs, those for chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. In addition some interstitial probes (CEN15, PML and SNRPN on chromosome 15) and whole chromosome paint probes (e.g. WCP12) were jointly hybridised to investigate the co-localization of interphase chromosome domains and tethered subtelomeres. Cells were prepared by omitting exposure to colcemid and hypotonic treatments.In these cells a specific interphase chromosome topology was detected. It was shown that the p and q telomeres of the each chromosome associate frequently (80% pairing) in an intrachromosomal manner, i.e. looped chromosomes with homologues usually widely spaced within the nucleus. This p-q tethering of the telomeres from the one chromosome was observed with large (chromosomes 3, 4, 5), medium sized (6, 7, 9, 10, 12), or small chromosomes (17, 18, 20). When triploid nuclei were probed there were three tetherings of p-q subtelomere signals representing the three widely separated looped chromosome homologues. The separate subtelomere pairings were shown to coincide with separate chromosome domains as defined by the WCP and interstitial probes. The 20% of apparently unpaired subtelomeric signals in diploid nuclei were partially documented to be pairings with the telomeres of other chromosomes.A topology for telomeres was detected where looped chromosome homologues were present at G1 interphase. These homologues were spatially arranged with respect to one-another independently of other chromosomes, i.e. there was no chromosome order on different sides of the cell nuclei and no segregation into haploid sets was detected. The normal function of this high frequency of intrachromosomal loops is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. This intrachromosomal tethering of telomeres cannot be related to telomeric or subtelomeric sequences since these are shared in varying degree with other chromosomes. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing. The reason why the telomeres attach to the nuclear membrane in meiosis is not dependent on the presence of normal numbers of TTAGGG repeats and, in fact, still occurs in late generation Terc-/- mice without detectable pantelomere repeats [et al. [et al. [et al. [et al. [et al. [et al. [In both plants and animals, during early meiosis in normal cells there is a clustering of all or most of the telomeres of the entire chromosome set to a single region on the nuclear membrane -3. This  repeats . In plan repeats  but a po repeats  but has  repeats ,8 but th [et al. ,10 using [et al. ,12 but N [et al. ,10 propo [et al.  where th [et al. ,15. From [et al.  studied  [et al.  were abl [et al.  utilisin [et al. ,19. We rall p-q signals tethered was 46–72% as compared to 33–58% in the triploid cells. This would be expected with more opportunity for interhomologous tethering in the triploid nuclei with an extra chromosome.Fluorescence in situ hybridization (FISH) performed with the various subtelomere probes Table  gave disThe triploid cells were used to test the likelihood that intrachromosomal pairing of subtelomeric signals was occurring rather than the pairing of p and q signals with the q and p signals of the other homologue(s). As can be seen in figure Further confirmation that the p-q tetherings in figure This study shows that the pairings of red/green signals from the subtelomeres of the short-arm and long-arm respectively occur at high frequency in these non-cycling diploid nuclei. In many cases the association is so close that the subtelomeric signals are superimposed  no such p-q telomeric tethering pattern is present in our observations of lymphocytes (not shown) and the only associations are of sister chromatids. That most lymphocytes are at G2 can be observed by the doubled signals representing sister chromatids (not shown) which is in contrast to the single (chromatid) signals in the unreplicated G1 nuclei , reviewed in Kolodner et al. [et al. [et al. [et al. [et al. [telomere transposition by Daniel et al. [reciprocal translocation was unproven. This translocation relocated the telomeres to the opposite chromosome arm and then by recombination the result was a duplication of one telomere on both chromosome ends and a deletion of the other. For this model to be correct a high frequency of balanced telomeric translocations would have to occur. These would be observed as large pericentric inversions and are rarely reported – see review in Daniel, 1988 [et al. [As reviewed by Ballif et al.  there aret al.  or by  in an intrachromosomal manner which was confirmed by studying chromosome domains with interstitial probes (chromosome arms) and WCP probes. It was found that homologues were regularly arranged without any defined relationship to non-homologous signal groups; i.e. there was no apparent chromosome order on different sides of the nuclei and no segregation into haploid chromosome sets was detected. The normal function of this high frequency of intrachromosomal telomeric pairings is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing.Cell lines were retrieved from liquid nitrogen, washed in Dulbecco's phosphate buffer (DPB) and reconstituted in Hams F10 medium. The following lines were used: a diploid skin fibroblast line CG04-0743BBRS with karyotype 46,XX derived from fetal cartilage and a triploid 69,XXX cell line CG01-2042YA of diandric origin derived from mesodermal cells of a chorionic villus biopsy. These were cultured until they reached confluence via contact inhibition. At this stage the cells exhibit a number of swirls of closely packed cells in parallel. They were then severally prepared for trypsin harvest usually 48 hours after the last media change without colcemid/colchicine treatment and without the usual 0.075 M KCl hypotonic treatment. The cells were trypsinised off and fixed three times in 3:1 methanol to glacial acetic acid and were stored at room temperature (R.T.) in fixative for 1–3 days. This period allowed some mild acidic digestion of the chromatin and spreading of the nuclei when slides were prepared. At the end of the storage period, cells were rewashed once with fresh fixative and dropped onto glass slides as per routine techniques, and stored on trays in a low humidity cabinet until used for FISH.The identity of the probes used in the study is shown in Table Slides were pretreated with a combined Pepsin/Rnase step. This was performed by prewarming RNAse and pepsin to 37°C, 200 μl of RNAse  was dispensed onto each slide, coverslipped and incubated at 37°C for 40 minutes in a humidified chamber. Coverslips were removed and slides washed twice for 5 minutes in 2xSSC at room temperature (RT). Slides were briefly drained and 200 μl of pepsin (0.2% in 0.01 M HCl) was placed on the slides, coverslipped and incubated at 37°C for 30 seconds. Coverslips were removed and slides were washed twice for five minutes in phosphate buffered saline (PBS) at RT. Fixation was with 6% paraformaldehyde in PBS, by dispensing 200 μl/slide, and adding a coverslip for 2 minutes at RT. Slides were then washed twice for five minutes in PBS at RT, dehydrated through 70, 90 and 100% ethanol for 3 minutes/wash at RT, and air dried. Probes in hybridisation mix were stored at -20°C, removed and thawed for 30 minutes; dispensed onto slides, covered with 15 mm diameter coverslips, and sealed with liquid rubber – art cement. Joint denaturation was achieved at 75°C for 5 minutes on a Omnigene hot plate, transferred to a humidified hybridization chamber at 37°C and hybridised overnight. After this the coverslips were removed. Post-hybridization washes were 0.4 SSC/0.3% NP40 at 73°C for 2 minutes then quickly transferred to 2xSSC/0.1% NP40 at RT for 1 minute. Slides were counterstained in DAPI and then rinsed and air dried. When ready, slides were mounted in antifade (2.3% DABCO in 40% glycerol/0.02 M TRIS-HCl) and covered until fluorescence examination. Slides were examined on a Zeiss Axioscop 20 fitted with a Zeiss fluoarc light source and images captured on an Applied Imaging Cytovision station using the false colours that are attributed by the software.Initially, the subtelomere probes were labelled in Spectrum Orange for all short arms and Spectrum Green for all chromosome long arms. Cells were separately probed with the two subtelomeric probes for a single chromosome at the one time. Probe pairs were used for the subtelomeres of chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. Images were captured for a large number of cell groups for each chromosome and pairings were scored on the captured images. Signals were interpreted as paired if the distance between signals was 10% or less of the greatest diameter of the nucleus . In addition to the above subtelomeric probe pairs, other probes were used to investigate the frequency of non-homologous tetherings (subtelomeric probes 1p and 9q, see Table Two such experiments were performed in the current study see fig . These cAD designed the study, captured and analysed all FISH signals, and drafted the manuscript.LH performed all growing of probes, labelling of probes, and most probe hybridizations.