The growth of new blood vessels in adult life requires the initiation of endothelial cell migration and proliferation from pre-existing vessels in addition to the recruitment and differentiation of circulating endothelial progenitor cells. Signals emanating from growth factors and the extracellular matrix are important in regulating these processes.in vivo. In the absence of co-stimulation with an ECM glycoprotein, HGF could not promote endothelial cell migration but retained the capacity to induce a proliferative response utilising the Map kinase pathway. Through promoting Met-Integrin association, HGF-FN and HGF-VN complexes coordinated and enhanced endothelial cell migration through activation of the PI-3 kinase pathway involving a Ras-dependent mechanism whereas a Ras-independent and attenuated migratory response was promoted by co-stimulation of cells with HGF and a non-binding partner ECM glycoprotein such as collagen-1.Here we report that fibronectin (FN) and vitronectin (VN) modulate the responses of endothelial cells to HGF (Scatter Factor), an important pro-angiogenic mediator. Novel binding sites for HGF were identified on both FN and VN that generate molecular complexes with enhanced biological activity and these were identified in the supernatants of degranulated platelet suspensions implicating their release and formation These studies identify a novel mechanism and pathway of HGF signalling in endothelial cells involving cooperation between Met and integrins in a Ras dependent manner. These findings have implications for the regulation of neovascularization in both health and disease. The generation and repair of blood vessels in adult life requires the regulation of endothelial cell survival, migration, proliferation and their differentiation from lineage-committed progenitors by the coordinated action of several classes of vaso-active agents including growth factors, cytokines, and the extracellular matrix (ECM) -4. Eluci5β1 through a mechanism involving the promotion of an integrin α5β1-VEGFR-2 signalling moiety generated as a consequence of receptor ligation by a VEGF-FN complex. These events promoted the sustained activity of Erk kinase, which was coupled to the migratory response. More recently, we presented data demonstrating that FN significantly enhanced VEGF-mediated migration of CD34+ cells and their differentiation into endothelial cells [in vitro studies have highlighted the importance of hepatocyte growth factor (HGF) as a pro-angiogenic mediator. HGF, also termed scatter factor, has a well-established role in tumourogenesis but may be an important mediator of neovascularization since studies show that HGF induces the expression of VEGF in endothelial cells in vitro and that HGF synergises with VEGF to promote capillary-tube assembly in collagen matrices [There is growing evidence pointing to a close collaboration between growth factors and the ECM in several biological processes including vasculogenesis and post-natal revascularization. Studies have shown that the response of cells to growth factors such as vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) and epidermal growth factor (EGF) are potentiated by integrin ligation to specific ECM glycoproteins -8. In a al cells . In addimatrices ,12. In amatrices . The emematrices . Althougin vivo. Significantly, we show that HGF-FN and HGF-VN molecular complexes induce a unique and enhanced intracellular signal employing Ras, thereby highlighting an important mechanism of growth factor receptor tyrosine kinase and integrin cooperation in promoting pro-angiogenic responses.In the present work we show that HGF forms a specific physical complex with FN and VN and that these complexes are present in degranulated platelet suspensions implicating a putative role 125I-labelled HGF to a variety of ECM molecules immobilized on plastic wells. As shown in Fig. 125I-labelled HGF bound to both FN and VN specifically with residual binding observed to either collagen-1 or laminin. Further experiments were performed to locate the HGF binding site on the FN molecule using purified FN proteolytic fragments immobilised onto the polystyrene microtiter wells. In these experiments 125I-labelled HGF bound to the 70 kDa N-terminal fragment and the 40 kDa C-terminal fragment. No significant binding was observed to the 120 kDa fragment that harbours the internal cell binding domain .We recently identified binding domains on FN for VEGF, which played an important role in promoting the activity of VEGF . Since Hain Fig. . To furtin vivo we examined platelets, a rich source of growth factors, for the presence of these complexes. Washed human platelet suspensions were stimulated with thrombin (1 U/ml) to promote degranulation and the derived supernatants were immunoprecipitated with antibodies directed to FN or VN. The resulting immune complexes were analysed for co-precipitation of HGF  were incubated with an optimal concentration of HGF alone or in combination with fixed concentrations of FN, VN or collagen-1 Fig. . Signifi5β1 completely inhibited HGF-FN-induced endothelial migration  did inhibit endothelial cell migration to HGF-FN by 20% suggesting an ancillary role for this integrin in mediating HGF-FN responses. When endothelial cell migration was induced by HGF-VN complexes, the integrin dependence shifted as expected . This latter effect may be a consequence of integrin signal cross-talk (transdominant integrin regulation), as reported previously [To further characterize the degree and identity of integrin involvement in the observed migratory response, we investigated the consequences of blocking integrin receptors on HMVEC with specific integrin antibodies prior to HGF-ECM stimulation. Antibodies directed to the integrin αion Fig. . In contted Fig. . Under teviously ,15. Thes2β1, α5β1 and αvβ3 respectively, predominantly co-precipitated Met with the integrins α5β1 and αvβ3. In contrast, Met co-precipitation with the integrin α2β1 was minimal for lysates derived from cells stimulated with HGF and collagen-1. The level of Met expression in these samples was not altered by treatment of the cells with various combinations of HGF and ECM molecules  that were coupled to the migratory response, HMVECs were treated with specific inhibitors of MEK and PI-kinase. In cell migration assays, LY294002 but not U1026 inhibited endothelial cell migration induced by HGF-FN Fig.  and HGF-The effect of the co-administration of ECM molecules with HGF on endothelial cell proliferation was also investigated. In contrast to cell migration, HGF, in the absence of ECM molecules, induced a significant proliferative response Fig. . HoweverThe data shown above indicate that HGF-induced endothelial cell migration and proliferation were mediated by PI-3 kinase and Erk pathway respectively. We next investigated the role of Ras in regulating these two pathways induced by HGF-FN and HGF-VN complexes. Since Ras is a well-documented regulator of p85 PI-3 kinase and Erk and as well as a down stream effector of both the Met and integrin receptors, we assessed the activation status of Ras by measuring the comparative levels of GTP-loaded Ras after endothelial cells were stimulated with HGF in the presence and absence of ECM molecules Fig.  &6B. EndTo further characterise the role of Ras in regulating endothelial cell responses to HGF/ECM, the effect of the FPT-III inhibitor on the phosphorylation levels of Erk 1/2 and Akt was investigated. In cells stimulated with HGF alone, Erk 1/2 was activated and significantly inhibited by the FPT-III inhibitor and to a lesser extent by GGTI Fig. . A similThe major finding of the present report is that HGF-induced endothelial cell responses are significantly augmented through the formation of molecular complexes between this growth factor and the ECM glycoproteins FN and VN. The significance of this finding is highlighted by the observation that HGF-induced endothelial cell migration, a PI-3 kinase coupled response, did not occur in the absence of additional signals originating from the ECM. However, HGF-induced endothelial cell proliferation was evident in the absence of signals emanating from the ECM. These observations have led us to propose a model for the mechanisms of HGF-induced responses in endothelial cells Fig. . This mo5β1 derived from endothelial cell lysates stimulated with HGF-FN complexes but not with the integrin α2β1 derived from cells stimulated with HGF and collagen-1  in binding buffer (PBS containing 2% BSA) were added to the microtitre plates and incubated for 30 min at room temperature (RT) before washing and counting to determine bound radioactivity. SPR analysis was performed on the BIAcore X (Biacore Herts UK) as described previously [2, 2 mM CaCl2 and the sensograms recorded. The data was analysed by the ASSAY programme (Biosoft, UK) in order to determine the EC50 value and Kd.ECM proteins and FN peptides were purchased from Sigma and Gibco and were further purified by gel filtration and ion exchange chromatography. The assay was performed as described previously . 125I-HGeviously . HGF  and then blocked by incubation in 3.5 mg/ml BSA in basal culture medium) for 1 hour at room temperature followed by extensive washes with basal culture medium. The remaining cells were measured using a fluorescence plate reader (measuring fluorescence in the upper transwell compartment). For proliferation experiments, cell division was measure by fluorescence labeling of DNA (CyQuant, Molecular Probes). HMVEC was plated on poly-D-lysine coated 48-well plates and cultured overnight in MCDB-131 medium containing 5% FBS. After washing plates with PBS, endothelial cells were then cultured in MCDB-131 medium + 0.1% FBS containing HGF (10 ng/ml) in the presence or absence of VN, FN or Collagen-1 (10 μg/ml). Cells incubated for 48 h and HGF/ECM was added every 24 hours. Cell proliferation was quantified using a fluorescence plate reader.Human dermal microvessel endothelial cells (HMVEC) were maintained in EBM-2 growth medium (Clonetics Corp). Migration studies were carried out essentially as described previously  using se202/Tyr204) and Akt (Ser473) respectively by Western blotting. These studies were performed with both cells in suspension and with adherent populations. Cells were grown to 80% confluence and serum starved for 2 hours prior to harvesting. Cells were resuspended in serum-free MCDB-131 medium (BioWhittaker) supplemented with 0.1% BSA (resuspension buffer) at a concentration of 1–5 × 106 cells /ml. The cell suspensions were challenged 10 ng/ml HGF supplemented with 2 μg/ml collagen-1, or FN or VN for various durations ranging from 2 to 120 min at room temperature. Cells were harvested by centrifugation at 4°C and lysed in 10 mM Tris pH 7.4, 145 mM NaCl supplemented with 0.1% Triton X-100 and protease inhibitors. For inhibitor studies, serum-starved HMVEC suspensions were pre-treated with the inhibitor for 45 min prior to stimulation with HGF and ECM molecules for a further 60 min at room temperature. The cells were pelleted, washed in ice-cold resuspension buffer without BSA and lysed in a lysis buffer containing 1% (v/v) Triton X-100. Cell lysates were analysed by Western blotting using protocols specific for the phosphospecific antibodies according to the manufacturer's recommendations. Blots were cut along appropriate marker divides and probed with antibodies to phopho Erk 1/2 and Akt (Ser473)  simultaneously. For GTP-Ras pull down assays, serum-starved HMVEC were stimulated with HGF and ECM molecules for a desired time point and the cells were spun down and washed in ice-cold resuspension buffer without BSA. Cell pellets were lysed in MLB buffer  and 500 μg of cell lysate was mixed with a 10 μl suspension of RBD-Sepharose (Upstate Biotechnology) for each reaction at 4°C for 60 min. Sepharose beads were spun down and washed in MLB prior to solubilization and analysis by Western blotting probing for Ras using a monoclonal antibody (Upstate Biotechnology). For Ras inhibition studies cells were pre-incubated with FPT-III (100 μM) and GGTI (2 μM)  for 45 min at room temperature prior to cell stimulation for 60 min with HGF and ECM.HMVEC were assessed for the activation profiles of Erk1/2 and Akt using phosphospecific antibodies  to Erk (Thr2β1 (clone JBS2), α5β1 (clone JBS5) and αvβ3 clone (LM609). After analysis by SDS-PAGE and protein transfer, the blot was then probed with a monoclonal to Met (clone DL-21, Upstate Biotechnology) and developed by chemiluminescence. For Met tyrosine phosphorylation analysis, cells were stimulated with HGF alone or HGF-FN and HGF-VN complexes for various time points ranging from 15 mins to 2 hours at room temperature. Lysed samples were immunoprecipitated with a polyclonal anti-phosphoMet antibody  and the immune complexes analysed by SDS-PAGE and Western blotting using a monoclonal ant-Met antibody (Upstate). Met was visualised using chemiluminescence technology (Pierce).Human microvessel endothelial cells (HMVEC) in serum-free MCDB-131 medium (BioWhittaker) supplemented with 0.1% BSA were plated on collagen, FN and VN coated petri dishes in the absence or presence of HGF (50 ng/ml) for 15 min to 1 hour at room temperature. Cells were then harvested as described previously  and inteSupernatants from non-stimulated and thrombin-stimulated washed platelet suspensions were prepared as previously described . SupernaHGF, hepatocyte growth factor, FN fibronectin, VN vitronectin, Col-1, collagen-1, HMVEC, human microvessel endothelial cells.SR, study design, HMVEC migration studies, SPR ligand binding analysis, HMVEC signalling studies, co-immunoprecipitation studies (integrin-Ras), manuscript preparation. ESW, study design, HMVEC proliferation studies, solid-phase ligand binding studies, co-imunoprecipitation (integrin-Met), manuscript preparation. YMP, study design, co-imunoprecipitation studies (HGF-FN/VN). JM, protein purification and preparation, KVP, GTP-Ras pull-down assays, RS, technical support. MS, study design.


The large amount of data that are currently produced in the biological sciences can no longer be explored and visualized efficiently with traditional, specialized software. Instead, new capabilities are needed that offer flexibility, rapid application development and deployment as standalone applications or available through the Web.) – that enables fast development of applications for protein analysis and visualization. The toolkit is written in Java, thus offering platform-independence and Internet delivery capabilities. Several applications of the toolkit are introduced to illustrate the functionality that can be achieved.We describe a new software toolkit – the Molecular Biology Toolkit . Recent scientific and technical advances in the field of experimental biology, particularly in genomics, have produced large amounts of biological data, which has posed new conceptual challenges. The visualization and visualization-driven analysis of these experimentally derived data has become a key component of the scientific process.Until recently, these needs were typically approached by designing applications specialized in a set of well-defined specific tasks. So, for example, popular applications for molecular visualization include, but are not limited to, Molscript , PyMol , Rasmol This paper, aimed at bioinformatics software developers, provides a concise presentation of the design and capabilities of the toolkit, presents a number of models for its usage, and illustrates its performance with several applications. The paper is organized as follows. The following section provides a general overview of the application programming interface (API). The Results and Discussion section presents details about core components of the toolkit and illustrates its capabilities with two applications. The Conclusions section summarizes the main functionality of the toolkit, discusses availability and documentation, presents performance data and outlines future development plans.The application-programming interface (API) that serves as the foundation of the toolkit has emerged from the exploration of the typical needs encountered by a researcher in the analysis of a documented biological molecule. The list of requirements includes reading the data file and converting the information to data objects that are easily accessible by applications, extracting subsets of chemical components according to different chemical or biological properties of interest (e.g. chains, residues, CA atoms, ligands, etc.), deriving information that is not explicitly present in the original input data  and visualizing chemical or physical properties of different subsets of the molecule, or the entire molecule.StructureFactory class offers a uniform approach to loading structural data, independent of the format of the source. The class makes use of a set of loaders that can import data from a variety of sources, either on the same machine or located on a remote server. Moreover, this allows the developer to write applications that do not have to uniquely specify the source of data. Instead, a number of methods in the StructureFactory class enable loading of structures based on a series of source descriptors: file name, PDB id code, URL location, etc. The first loader capable of handling the source descriptor is used thus providing a data access and retrieval mechanism that is transparent to the user.Based on the requirements identified above, we have arrived at the multi-layered design illustrated in Fig. StructureFactory is a Structure object, which is effectively an interface to a summary of raw primary and secondary structure data. The Structure object is not restricted to holding structural information and can contain any other data relevant to the organization of the molecular entity. For instance, it can store the description of single or multiple protein sequences preserving the ordering and alignment of the amino acid residues and their properties.The output of the load methods in the StructureMap class builds the internal data model of the simple or complex molecule and provides hierarchical access to both raw data and derived information generated from the input. This includes access to chains, residues, atoms, bonds, nucleic acid components, ligand atoms, fragments associated with secondary structure components, or features defined by the user.The StructureStyles class provides information about rendering, coloring and selection attributes associated with any structure component produced by the StructureMap. A wide variety of methods in this class allow any module of an application, in particular any viewer, to set and retrieve the information describing the visually represented parameters of any structure component.The StructureDocument is designed as a container class that maintains a log of all loaded structures and viewers that are instantiated by an application in a given session. It also has the role of generating events associated with the addition or removal of structures and viewers.The The next level of the API contains graphical user interface (GUI) elements. This portion of the package contains high level constructs, such as windows and panels designed to display 3D graphics, protein or DNA sequences or tree representations of the chemical components of the molecule, as well as lower level components that can be used to build a molecular scene according to a developer's preferences.StructureMap class to retrieve all secondary structures within a given molecule, or how to obtain a list of atoms or residues. This is the level that most developers would modify in order to create custom applications tailored to their specific needs.Finally, at the application level, a number of applications are provided that can be used to illustrate the features of the toolkit, or as starting templates for new application development. For example, the applications illustrate how to use methods in the Note that care has been taken to further enable application developers to use different components of the toolkit to build purely analytical applications that have no visualization component. For example, a developer could write a command-line tool that simply loads a molecule and gathers statistics about the structure data without presenting any graphical interface. Such an application could be part of a back-end process that runs in a web-server environment.The MBT was developed as an object-oriented Java-based environment and hence is flexible, modular and lightweight, which facilitates maintenance, web deliverability and limits the required computer resources. Moreover, the toolkit can be easily extended and having been written completely in Java is effectively platform-independent.Structure object  is built, which is then attached as a BranchGroup node to a SceneGraphObject  representing the three-dimensional image of the molecule. The PsGeometry (primary structure) and SsGeometry (secondary structure) classes provide a number of methods that build complete 3D scenes from a given set of primary or secondary structure data object  provides a set of pluggable and extensible classes for use by application developers interested in the visualization and analysis of macromolecular data. The MBT provides a set of pre-written data loaders, viewers, a common data model and the means to add to and customize the toolkit for specific applications delivered as applets through the Web or as standalone applications. Base functionality and comparable tools (where applicable) are as follows:• Classes to load raw data from a number of common protein structure and sequence data sources (PDB, mmCIF, FASTA, etc.) and a means to easily add new "loader" modules independent of the applications they might serve.StructureComponent objects) are provided (e.g., Atom, Bond, Residue, Chain) and new data types are easily registered. Further the data model provides an extensible means to describe viewable or visible attributes (e.g., color, radius, drawing style) of these objects.• A common data model to which raw information is imported, mapped, and indexed. A number of data record types (• Written entirely in Java, programs may be embedded (using Java WebStart or the Java Plug-In) directly inside web pages. This enables the deployment of tightly coupled interactive web content much like the popular MDL Chime plug-in.• Applications are not restricted to the features provided by the MBT APIs. The Programmers Guide details how to extend the system.• With source code provided, core features of the toolkit may be directly modified or extended for independent use (though, this may cause your code to diverge from and become incompatible with subsequent releases of MBT). However, adding code within the existing framework and contributing it back for others to use is encouraged. The source code has been extensively commented to produce a rich and complete set of hyper-linked javadoc API documents.• A set of pre-written viewers (Sequence, Structure, Tree) that can be extended, replaced, omitted, or augmented with completely new viewers that implement entirely new visualization techniques.StructureViewer module provides visual representations similar to RasMol and Molscript such as balls-and-sticks, CPK spheres, split-bonds, extrusion/ribbon-style backbone traces, and secondary structure cartoons (Helix, Turn, Coil, Strand). It also provides 3D labeling.• The 3D • A scripting interface is being developed for MBT to enable toolkit functionality to be command-line or even script-driven (similar to MDL Chime or PyMol).5 atoms require physical memory in excess of 500 MBytes and on a notebook computer with a 1.2 GHz processor can take nearly one minute. Efforts at optimization are on-going.Version 1.0 of the toolkit and sample applications, including those described here, are available for download from the project web page . The sammbt@sdsc.edu. Bugs may be reported to a bug tracker available on the project web site [New applications are on-going including the generation of high quality images for all structures in the Protein Data Bank and new ways of visualizing protein-protein interactions. We invite contributions to the MBT by sending mail to web site .Project Name: Molecular Biology Toolkit (MBT)• Project Home Page: • Operating System: Platform independent• Programming Language: Java• Other requirements: Java 1.3.1 or higher, Java3D• License: Free for educational, research and non-profit purposes• Any restrictions to use by non-academics: Contact the University of California at San Diego's Technology Transfer Office (invent@ucsd.edu, 1-858-534-5815)• JLM is one of the designers of the API and co-developer of the toolkit. AG designed and implemented the geometry generation modules, implemented the algorithms for secondary structure generation and bond detection, and drafted the paper. OVB developed the PKR Explorer. QZ developed the Ligand Explorer. PEB coordinated the whole project, suggesting the general functionality and scientific objectives of the toolkit.


Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed.Here we describe a method for the normalization of a set of time-course electrophoretic data to be compared. The method uses Gaussian curves fitted to the complex peak mixtures in each electropherogram. It searches for target ranges for which patterns are dissimilar to the other patterns  and for references  in the set of resultant approximate patterns. It then constructs the optimal normalized pattern whose correlation coefficient against the reference in the range achieves the highest value among various combinations of candidates. We applied the procedure to time-course electrophoretic data produced by HiCEP, an AFLP-based expression profiling method which can detect a slight expression change in DNA fragments. We obtained dissimilar ranges whose electrophoretic patterns were obviously different from the reference and as expected, most of the fragments in the detected ranges were short (< 100 bp). The normalized electrophoretic patterns also agreed well with reference patterns.The normalization strategy presented here demonstrates the importance of pre-processing before electrophoretic signal comparison, and we anticipate its usefulness especially for temporal expression analysis by the electrophoretic method. Amplified fragment length polymorphism (AFLP) is a DNA fingerprinting technique using electropherograms . AFLP anMspI and MseI. Consequently, the HiCEP method can detect a slight expression change of transcript-derived fragments (TDFs) with high coverage. The estimated 30,000 transcripts expressed in a cell are divided into 256 subgroups (16 MspI-NN primers * 16 NN-MseI primers) containing approximately 120 PCR-amplified TDFs. This number is small enough to be separated by fluorescent capillary electrophoresis using an automated DNA sequencer such as the ABI Prism 310 (Applied Biosystems). We can achieve higher throughput by using several fluorescent dyes at once [Recently, we developed an AFLP-based gene expression profiling method called HiCEP (High Coverage Expression Profiling) . The exp at once ,15.MseI) tends to produce many small TDFs (< 100 bp) and in our experience this range is prone to errors of fragment sizing. Cumulative errors of fragment sizing interfere with normalization across different electropherograms and lead to the mis-assignment of valid TDFs. Hence, more detailed analysis such as observation of gradual expression changes in the time series of a TDF still counts in subjective visual examination [Normally, digitized electropherograms are imported into image analysis software such as GeneScan (Applied Biosystems), which outputs each fragment (band) together with its length (in bp), area and height , carrying out accurate fragment sizing and background subtraction for most of the operations. GeneScan is capable of separating the signal from each fluorophore to provide higher throughput analysis. However, it should be noted that intense signals from abundant TDFs can breed into each other, potentially confusing the fragment sizing ,15. Furtmination . FurtherThe purpose of the present study is to develop a normalization method for the automated analysis of temporal electrophoretic data. We assume the samples to be compared are identical, that TDFs have similar fragment lengths across electropherograms and that expression changes can be detected as variations in peak height using the HiCEP technique. The performance of the method is demonstrated by analyzing a large set of time-course data obtained from mouse embryonic stem (ES) cells, using HiCEP.We analyzed a total of 2560 HiCEP electropherograms (256 sets of ten), containing time-course data of embryonic stem (ES) cells 0, 12, 24, 48, and 96 h after adding stimulation for differentiation. Reproducibility was confirmed by the duplication. We applied the current method to each of the 256 sets.0 h-1, 12 h-1, and 48 h-2) in the range (35–50 bp) appear to be compressed on the short side. This is probably because another intense peak just under 35 bp is mistaken for the 35 bp marker peak. This reduces the overall similarity between lanes and makes it difficult to recognize identical TDFs such as red filled peaks in Fig. When a set of electrophoretic data is arranged and surveyed, one can often find ranges  in which peak fragment lengths are incorrectly measured. For example, in Fig. Ptarget and the other lanes within a certain range using equation 3. By using the average correlation coefficients, we can make a quality score function Qk (t) for all lanes (k = 1, 2, ..., 10) at arbitrary length t (see Methods). An example of the calculation for lane 0 h-1 is shown in Table Q(t) take the place of detailed visual evaluation of dissimilar ranges  and at 75 bp in two lanes (0 h-2 and 96 h-1).To this end, we first developed a method for displaying dissimilar ranges. The method is based on a moving-fragment approach that continuously determines the average correlation coefficient between particular lane ges Fig. . UndoubtNext, we applied a simple method for the automated detection of dissimilar ranges to 256 sets of electrophoretic data (see Method). The method identified a total of 362 dissimilar ranges. Most 289, 79.8%) of the ranges were of 100 bp or less. This is reasonable because the main source of fragment sizing errors is the presence of intense peaks near the marker 9.8% of t,15 and t0 h-1, 0 h-2, 12 h-1, 12 h-2, and 48 h-2) in range (35–50 bp) and two lanes (0 h-2 and 96 h-1) in range (50–100 bp). Of these, we at first suspected that two lanes (0 h-2 and 12 h-2) in range (35–50 bp) were false-positives (mistakenly identified as dissimilar). However, we observed that the range in the two lanes is worthy of being normalized: the fragment lengths on the short side of the range deviate gradually from the mean lengths of lanes 24 h-1, 24 h-2, 96 h-1, and 96 h-2 [see Visual examination revealed many of those ranges to be genuine, but not all. The set of ten electropherograms shown in Fig.  h-2 see .Visual examination of all the electropherograms did not reveal any false-negative errors (overlooked dissimilar ranges). Recall that the samples to be compared are identical and that the measure of the quality of fragment sizing is based on a calculation of the average correlation between electropherograms. These results suggest that the normalization strategy we present here is useful, especially for temporal expression analysis.T in equation 3 in the Methods section, which is the number of consecutive fragments making up the quality profile examined by the program. Quality profiles using the shortest span (T = 1) are noisier than those using a moderate span, and runs using spans of less than four fragments were found unsatisfactory in our investigation. On the other hand, long spans (T = 10) tended to miss small dissimilar ranges. These trends are essentially the same as those in the delineation of hydropathy plots of proteins using a moving-window approach and in the detection of transmembrane regions [T = 5 throughout the analysis, further improvement in the choice of parameters as well as the method for the detection of dissimilar ranges remains to be studied.The effectiveness of the method depends on the choice of the parameter  regions . Althoug96 h-2 as the best reference in ranges (35–50 bp) and (50–100 bp). We cannot, of course, reject the possibility that accurate fragment sizing is performed in the minority group (such as lanes 0 h-1, 12 h-1, and 48 h-2 in range (35–50 bp) in Fig. To normalize dissimilar ranges across a set of electropherograms, it is necessary to select one as a reference. In conventional algorithms the reference is selected manually ,18. For D × 100% expansion (or - D × 100% compression) to D × 100% compression of the short side of the original profile at intervals of d bp. The best approximating profile is one of the candidate profiles with {x × d - D × (Ce - Cs)} / (Ce - Cs) × 100% compression of the side in a given range (Cs - Ce bp), where x = {0, 1, ..., 2 × (Ce - Cs) × D / d}. There is of course a trade-off between the computation time and the normalization accuracy in the choices of parameters. In Model 1, we set D = 0.4 and d = 0.2. We expected that the normalization would be achieved by a linear expansion of the short side of the dissimilar range (35–50 bp) by anchoring the long-side in the target lane 12 h-1. Indeed, the best approximating profile that achieved the highest correlation coefficient against the reference 96 h-2 was the case of x = 9 (28% expansion).We prepared two models for accurate normalization of various types of fragment sizing errors. Model 1 is the case of an incorrect fragment sizing at the shortest (or longest) marker peak. Figure Mj in a dissimilar range (Mj-1-Mj+1 bp) (see Methods). Accordingly, the program can easily determine the length of 75 bp because there is only one marker length inside of the range (50–100 bp). We can directly apply the normalization procedure for Model 1 to Model 2 by considering two hypothetical dissimilar ranges, (50–75 bp) and (75–100 bp). The main difference from Model 1 is that the two ranges cannot be normalized independently in Model 2: {x × d - D × (100 - 50)}/(100 - 50) × 100% compression (resp. expansion) of the long-side of the original profile in range (50–75 bp) and {x × d - D × (100 - 50)}/(100 - 50) × 100% expansion (resp. compression) of the short side in range (75–100 bp) affect on each other. In Model 2, we set D = 0.1 and d = 0.2 as a maximal realistic displacement. The best approximating profile is the case of x = 13 and is consistent with the reference profile.Figure 0 h-2 has two normalized ranges) are normalized nearly perfectly  which is a possible false-positive error are normalized as 2.7% compression of a short side of the range. The correlation coefficients between the target 0 h-2 and the reference 96 h-2 in the range before and after normalization are 0.674 and 0.798, respectively.Figure tly Fig. . For exa48 h-2 indicates that an incorrect normalization is performed in range (35–50 bp) of the lane. The low correlation coefficient (0.4) between the normalized profile and the reference 96 h-2 in the range, compared to values (> 0.7) between four other normalized profiles (0 h-1, 0 h-2, 12 h-1, and 12 h-2) and the reference in the corresponding range, strengthens this suspicion [see 48 h-2 should be extended on the long side. We searched for the best range to be normalized and chose (35–53.6 bp). The correlation coefficient of the normalized profile, expanded by 26.3% on the short side in the range (35–53.6 bp), was 0.9. Undoubtedly an exhaustive search for edges in dissimilar range might yield better normalization for some cases. However, it also dramatically increases the possible combinations of normalization candidates. It is a balance between the computation time and the number of analyzable TDFs.A quality profile for lane cion see . After vQk (t) for lane k indicates greater consistency with the other lanes around arbitrary length t if the sample is identical (e.g., time-course data). The quality scores after normalization overall were higher than before  which indicates a relative similarity at t (bp) in lane k to the other lanes. We demonstrate two other sets of electrophoretic data and discuss the feasibility of the method.A critical step in the analysis of 1-D electrophoretic data is the assignment of the correct size to each TDF. In time-course data, one expects that the same TDFs should have quite close fragment lengths across electropherograms and that temporal expression changes are reflected as differences in peak height. We developed the current method aimed at temporal expression analysis by the electrophoretic method and used a scoring system for an objective evaluation of experimental reproducibility using Figure 24 h-2, 48 h-2, and 96 h-1) detected by the method. Of these, 24 h-2 and 96 h-1 were normalized using Model 1 and 48 h-2 was normalized using Model 2. Visual evaluation of the electrophoretic patterns and the quality scores after normalization verified the choices of the models as appropriate  where both Models 1 and 2 are applicable. A set of ten electrophoretic patterns and their quality scores shown in Figure 5 bp wherQ(t) necessary for the accurate alignment of valid TDFs across lanes is about 0.7  ≥ 0.7 in the range (35–700 bp) before and after normalization are 202,204  and 205,829 (92.7%), respectively. Furthermore, 3,334 (92%) of the 3,625 (= 205,829 - 202,204) new high-quality peaks were < 100 bp, which corresponds to the biased distribution of the detected dissimilar ranges (nearly 80% of which were 100 bp or less).We also estimated the feasibility of the method with regard to an increasing number of peaks with certain quality score or more. The minimum value of 0.7 Fig. . Accordii.e., corresponding TDFs across electropherograms should have nearly the same fragment lengths). The monitoring of temporal expression change by the HiCEP technique has great potential for screening of genes related to chemotherapeutic drug resistance, circadian rhythm, and so on [When we apply the method to HiCEP time-course data, we assume that the set of electrophoretic data to be compared is identical (nd so on ,23,24. A0 h-1, 0 h-2, 12 h-1, 12 h-2, 24 h-1, 24 h-2, 48 h-1, 48 h-2, 96 h-1, and 96 h-2.mRNAs were prepared from mouse embryonic stem (ES) cells at 0, 12, 24, 48, and 96 h after removal of Leukemia Inhibitory Factor (LIF) from the culture medium. The samples subjected to HiCEP reaction were duplicated. We designated each sample as MspI combined with MseI) and ligated with the corresponding adaptors. The resulting HiCEP templates, MspI-MseI-poly(A) mRNAs, were amplified by fluorescently labelled primers; for labelling, FAM, HEX, and NED were used. In total, 256 primer combinations  were used in the HiCEP analysis. For example, a primer combination of MspI-TA and GC-MseI is capable of amplifying particular transcript-derived fragments (TDFs) corresponding to that combination. The details of the protocol of the HiCEP reaction are described elsewhere [.mRNAs prepared from each sample were digested with two 4-bp-cutting endonucleases (lsewhere . An animL (in bp), peak height H, and area A (in arbitrary units). Accordingly, the subsequent normalization procedure accepts these three-tuples as input for detected TDFs between 35 bp and 700 bp. TDFs smaller than 35 bp or larger than 700 bp were omitted from the analysis because the range was outside the size calibration range.The PCR products were denatured and loaded on an ABI Prism 310 (Applied Biosystems) for capillary gel electrophoresis. The digitized images were analyzed by the GeneScan software (Applied Biotech). The size of the fragments was calculated by the software, according to internal molecular size markers of 35, 50, 75, 100, 139, 150, 160, 200, 300, 340, 350, 400, 490, 500, 600, and 700 bp, on each gel. The fragment sizing and baseline subtraction were performed by the software. The software quantifies each peak by the fragment length MspI-CT combined with tt-MseI (designated as CT-tt)' because the ten electropherograms have some ranges for which fragment sizing is obviously inappropriate (we therefore designated such ranges as "dissimilar ranges").The starting point of normalization is a set of lanes  in each of 256 primer combinations. We explain the procedure using data from the primer combination of 'Fi in lane P is originally characterized by the three-tuples (Li, Hi, Ai). If lane P consists of n fragments , the approximation of the lane at length t is given by:The first step starts from the Gaussian approximation of each lane. The use of the approximating lane is the same as described in Aittokallio et al. -27. Brieσi is obtained from the following equation:where CT-tt are shown in Fig. The approximation is performed independently for each lane. The ten approximate profiles of time-course data in the primer combination of , we next assign quality scores to each of the fragments , where the fragments are originally numbered with respect to their lengths. By using the ten approximate profiles, relative similarity scores  for intervals from fragment i to fragment (i+T-1) (i = 1, 2,..., n - T + 1) in lane Ptarget (target = {1, ..., 10}) are calculated from the following equation:For the automated identification of 'dissimilar ranges' from the expression profiles of ten lanes  is the Pearson correlation coefficient between the target lane Ptarget and one of the other lanes Pk in the interval (start-end bp) which always includes T fragments from fragment i to fragment (i+T-1) (i = 1, 2, ..., n-T+1). The interval is defined as: start = Li - 2.5σi and end = Li+T-1 + 2.5σi+T-1. In this analysis, the number of fragments T is held constant at T = 5; other numbers are of course possible. By applying a moving window of T fragments, most of the fragments (n-T+2 fragments in this case, with the exception of F1, F2, F3, F4, Fn-3, Fn-2, Fn-1, and Fn) have T relative similarity scores. Finally, the relative quality value Q(Li) for fragment Fi is defined as the average of the similarity scores which satisfy start ≤ Li ≤ end. An example of the calculation is given in Table t, Q(t), are interpolated by the use of cubic splines to . The procedure is applied to each of the ten lanes  and then the quality profiles  corresponding to the expression profiles are created  have a clear interpretation. The high (or low) score for Qk(t) in lane k indicates a high (or low) level of relative similarity between the lane and the others around the length t.The quality profiles delineated from Now we have information  for the automated detection of dissimilar ranges. Here we adopt a simple method for detecting the range. Briefly,Cseed_s - Cseed_e bp) which satisfy two conditions: a) Q(t) ≤ thresseed and b) they contain at least two peaks.1) Seek 'seed' ranges  Seek  (in this case, M1 = 35, M2 = 50, ...,  = 700) to Ctmp_s (resp. Ctmp_e) for Cs (resp. Ce); accordingly, both Cs and Ce =  and Cs <Ce3) Substitute the nearest marker length thresseed is set to 0.3 empirically. Forexample, P9 has the following parameters in Fig. Cseed_s = 57.04, Cseed_e = 89.98, Ctmp_s = 52.60, Ctmp_e = 104.60, Cs = M2, and Ce = M4. Although fine tuning might be necessary, the procedure enables us to display dissimilar ranges.Aparameter Cs - Ce bp), we have to select the "reference" . One method is to choose lane Preference satisfying max {}, where  is the average of Qk in the range (Cs - Ce bp) in lane . For example, the algorithm selects P10 (i.e., 96 h-2) as a reference in a particular range (M1-M2 bp) and also in range (M2-M4 bp).When we want to correct a dissimilar range (L) and the areas (A) of peaks in a dissimilar range so that the similarity between the normalized electrophoretic pattern and the reference pattern in the corresponding range can be maximized. To normalize a particular lane Ptarget against the reference Preference, we now consider the following two models. Model 1 is the case of an incorrect fragment sizing at the shortest (or longest) marker peak, i.e, Cs = M1 = 35 (or Ce =  = 700). The peak lengths deviate more and more from the reference length moving from Ce to Cs (or from Cs to Ce). Model 2 is the case of an incorrect fragment sizing near marker length Mj ). Roughly, the deviation of peak lengths from the reference length gradually increases starting from Cs; the maximum deviation is reached at Mj (Cs <Mj <Ce); the deviation decreases gradually; and finally disappears at Ce bp.The meaning of the word "normalization" here is to correct the fragment lengths (P3 (12 h-1) in range (M1-M2 bp) against the reference P10 (96 h-2). Undoubtedly, the profile displays a systematic deviation from the reference. The degree of the deviation gradually increases starting from M2 bp to M1 bp probably because an intense peak generated near the shortest marker peak for the correction of M1 bp is used mistakenly. We expect the normalization will be achieved by a linear expansion of the short side (M1) of the range (M1-M2 bp) by anchoring the long side. The best approximating profile is found by considering various combinations of normalization candidates starting from D × 100% expansion to D × 100% compression of the short side at intervals of d bp. We set D = 0.4, as a maximal realistic displacement and d = 0.2. Accordingly, in practice, the number of combinations is 2 × (Ce - Cs) × D/d + 1 ) in Model 1.Normalization is performed by either expanding or compressing. Consider, for example in Model 1, normalization for the expression profile of x (x = {0, 1, ..., 2 × (Ce - Cs) × D/d)}, we make a candidate profile Px by changing three parameters (Li, Ai, and σi) accompanied by fragments (Fi) in the dissimilar range (Cs - Ce bp), according to the level of correction (expansion or compression). Those parameters are calculated as follows:For each combination  in a given range (Cs - Ce bp) into eq. (1). The best approximate profile is the one that achieves the highest correlation coefficient between Preference and Px (x = {0, 1, ..., 2 × (Ce - Cs) × D / d}) in the range (Cs - Ce bp). In the normalization for the expression profile P3 in the range (M1-M2 bp) against the reference P10, the best normalized profile by our method matches well with the reference  in range (M2-M4 bp) with the reference P10 (96 h-2); there is no possibility of Model 1 (Cs ≠ M1) and the number of incorrect marker lengths is only one (M3). Model 2 is a mixture of Model 1. The normalization is also done by one of the {2 × (Ce - Cs) × D / d + 1} combinations starting from D × 100% compression of the long side in (M2-M3 bp) and D × 100% expansion of the short side in (M3-M4 bp) to D × 100% expansion of the long side in (M2-M3 bp) and D × 100% compression of the short side in (M3-M4 bp) at intervals of d (= 0.2) bp. Unlike Model 1, we set D = 0.1 as a maximal realistic displacement. In the normalization for the expression profile P2 in the range (M2-M4 bp) against the reference, the best normalized profile by our method is matches well with the reference  is very wide , there are two or more possibilities for incorrect marker lengths in Model 2. Of these cases, we only consider cases with j = 1 in Model 1 because such cases are the only realistic ones. For the remaining cases , the experiment should be redone rather than trying to normalize them by considering numerous possibilities. It should also be noted that there is a case of a dissimilar range (M1-M3 bp) to which both Models 1 and 2 are applicable. In this case, the best approximate profile is decided by comparing the two best possible profiles determined using Models 1 and 2.It should be noted that when a dissimilar range  in Fig. 0 h-2 and 12 h-2) confirmed the identification.Click here for fileMagnified electrophoretic patterns and the quality profiles in range (35–50 bp) in Fig. 0 h-2 and 12 h-2) which are suspected false-positive errors.Click here for file


We describe a simple approach we used to identify barriers and tailor an intervention to improve pharmacological management of hypertension and hypercholesterolaemia. We also report the results of a post hoc exercise and survey we carried out to evaluate our approach for identifying barriers and tailoring interventions.We used structured reflection, searched for other relevant trials, surveyed general practitioners and talked with physicians during pilot testing of the intervention. The post hoc exercise was carried out as focus groups of international researchers in the field of quality improvement in health care. The post hoc survey was done by telephone interviews with physicians allocated to the experimental group of a randomised trial of our multifaceted intervention.A wide range of barriers was identified and several interventions were suggested through structured reflection. The survey led to some adjustments. Studying other trials and pilot testing did not lead to changes in the design of the intervention. Neither the post hoc focus groups nor the post hoc survey revealed important barriers or interventions that we had not considered or included in our tailored intervention.A simple approach to identifying barriers to change appears to have been adequate and efficient. However, we do not know for certain what we would have gained by using more comprehensive methods and we do not know whether the resulting intervention would have been more effective if we had used other methods. The effectiveness of our multifaceted intervention is under evaluation in a randomised controlled trial. Much research has been carried out with the aim of influencing the performance of clinicians. The results have varied ,2. As wiThe reasons why clinical practice sometimes is not consistent with current best evidence varies across clinical problems and from one clinician to another. A logical consequence of this is to tailor quality improvement strategies to address specific barriers . SeveralIn this article we describe a simple approach we have used to identify barriers to changing professional practice. This was done as the first step in a process of developing an intervention to improve the pharmacological management of hypertension and hypercholesterolaemia . The int• Contrary to recommendations, physicians seem to rarely estimate the risk of cardiovascular disease before initiating treatment • Sales of thiazides are low, despite these drugs being recommended as first-line medication • Relatively few patients reach recommended treatment goals ,12We also report the results of a post hoc exercise and a survey we carried out to evaluate our approach to identifying barriers and interventions.We developed the intervention through a process of identifying barriers to implementation of recommendations and measures specifically addressing these barriers ("tailoring"). The methods we used were structured reflection, searching for other relevant trials targeted at improving the management of hypertension or hypercholesterolaemia, conducting a survey among general practitioners and discussion with physicians during pilot testing of the intervention.The three authors reflected over possible barriers based in part on our own experience as physicians working in primary care in Norway. We used a worksheet to structure our reflection . The worksheet included factors that might act as barriers in the practice environment, the professional environment, and related to physicians' knowledge, skills and attitudes. One worksheet was completed for each targeted behaviour: increasing the use of cardiovascular risk assessment before initiating treatment for hypertension or hypercholesterolaemia, increasing the prescribing of thiazides for the treatment of uncomplicated hypertension, and increasing the proportion of patients on medication for hypertension and hypercholesterolaemia that reach recommended treatment goals. The worksheet was used to facilitate our group discussion of possible interventions to address the identified barriers.Our research group had recently completed a trial of a strategy for guidelines implementation when we were planning this study . In that trial register for trials of interventions targeted specifically at the management of hypertension or elevated cholesterol in general practice.We considered systematic reviews of interventions to improve professional practice when we designed our strategy . We searWe surveyed general practitioners about some of the interventions about which we were uncertain after our structured reflection. The details of the survey have been described elsewhere . Briefly1. Do physicians assess cardiovascular risk before prescribing antihypertensive or cholesterol-lowering drugs?2. If not, would physicians be more likely to do so it they received a fee for this?3. Do physicians comply with current regulations limiting the reimbursement of cholesterol-lowering drugs?The last question was asked for two reasons. Firstly, we were considering making risk assessment a condition for reimbursement of the drugs. Secondly, the existing regulations were a possible barrier to adhering to our recommendations because they conflicted with these.During pilot testing of the intervention at two practices, which were selected for convenience, comments from physicians relevant to possible barriers were noted. We also informally evaluated each component of the intervention., December 2003. Each participant was asked to complete a worksheet to identify barriers and possible interventions related to the low use of thiazides among general practitioners. They were randomly allocated to four different groups where they collaborated on completing the worksheet. They were also asked to grade the importance of each barrier or intervention as minor, moderate or major. We disregarded those rated as minor. We compared the results from the four groups with the barriers and interventions we had identified.After we had finished designing the intervention we had the opportunity of testing our method of structured reflection at a gathering of international researchers in the Research Based Education and Quality Improvement group (ReBEQI) While conducting the randomised trial to test the effectiveness of our multifaceted intervention we carried out telephone interviews with physicians allocated to the experimental group. They were asked if they adhered to our recommendations and, if not, why. The responses where noted down during the interviews.Figure Many of the barriers were related to a lack of knowledge and could be addressed through educational interventions. The use of educational outreach visits was logical since we had planned to use an active intervention, based on our previous experience, and since this type of intervention has consistently lead to improved professional behaviour in randomised trials . SimilarThe search (July 2001) of the EPOC trial register for randomised trials with the word "hypertension" in any field yielded 58 references. Most were excluded after reading the abstracts, leaving eight, for which the full text was reviewed -22. ThisThe survey results did not indicate that a fee for estimating cardiovascular risk before initiating drug therapy would affect practice . The surWe did not identify additional barriers during pilot testing of the intervention with five physicians in two practices, but several of those already identified were confirmed, particularly barriers to prescribing thiazides.Based on our findings and an assessment of the feasibility and evidence of effectiveness for various interventions, we designed a multifaceted intervention. The elements of the intervention are described in table We also considered a number of interventions that we excluded. For example:• We considered placing computers in waiting rooms so that patients could assess their cardiovascular risk before seeing the physician, but concluded this would be costly and difficult to implement.• We considered providing pre-printed prescriptions, but found this would not to be relevant because most physicians use computerised systems for prescribing.• We considered exposing conflicts of interest among clinical specialists who advocated using other first line drugs than thiazides, but elected not to do so.• We considered exposing techniques used in pharmaceutical advertisements, such as using relative risk reductions rather than absolute risk reductions , but conNineteen researchers were divided into four groups. All groups considered advocacy by drug companies to be a major barrier to change. Routines or habits were also included as an important barrier by all the groups, as well as lack of knowledge concerning the effectiveness of thiazides, their favourable adverse effects profile, and their low cost. All the groups also mentioned competing guidelines or diverging opinions as part of the problem. Three of the groups considered local or national opinion leaders as potential barriers to change. Patients' expectations or perceived expectations were also mentioned by three of the groups.The interventions recommended by the groups to address the identified barriers are presented in table Among the 195 physicians exposed to the intervention, 149 (76%) were contacted during the trial period and agreed to answer our questions. No major additional barriers were identified. However, some physicians questioned whether adhering to the recommendations would represent a good use of resources, specifically the recommended treatment goals.Addressing barriers to change with tailored interventions makes sense and there is some empirical support for this . It is uSeveral qualitative methods can be used to identify barriers, such as interviews, focus groups and observation. These methods may be valuable, but they are relatively labour-intensive. We used a simpler approach to identifying barriers to change. Would the use of other methods have provided us with important additional information? Pilot testing and discussions with five physicians in two practices and interviews with 140 participating physicians did not indicate additional barriers. The post-hoc focus groups with international experts did not add much with regards to barriers and interventions. Several of these groups included "routines and habits" as a potential barrier, which was not explicitly mentioned among the barriers identified by the investigators. However, all interventions that were mentioned by more than one of the groups in the post-hoc focus group exercise were included in our multifaceted intervention. Our use of computerised reminders was based on the assumption that this would help to establish new routines, although we did not record routines and habits as a barrier when we developed the intervention.There are inherent weaknesses in our approach. One is that the investigators undertaking the structured reflection were few and we were prejudiced by our own experiences. The lack of patient involvement is another limitation, which possibly lead to an under-emphasis of patient-mediated interventions. A weakness with the group of international researchers who participated in the post-hoc focus groups is their lack of familiarity with the Norwegian context.A number of trials of tailored interventions have been conducted. The methods used to identify barriers to change have varied. Some investigators have simply hosted a meeting ,26, othe.Our simple approach to identifying barriers to improving practice appears to have been effective in identifying all of the important barriers, and it was efficient. However, we do not know for certain what barriers other methods would have identified or whether the intervention could have been more effective, if we had used other methods. Further work to address these questions is planned, including direct comparison of alternative methods and evaluations of theory-based approaches The effectiveness of our multifaceted intervention is under evaluation in a randomised controlled trial.None declared.All the authors participated in the process of structured reflection and in conducting the survey of physicians. AF was responsible for reviewing the results from previous research and pilot testing of the intervention. AF drafted the article while SF and ADO contributed to critical revisions of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


The key to a rational treatment of elementary Mendelian genetics, specifically to an understanding of the origin of dominant and recessive traits, lies in the facts that: (1) alleles of genes encode polypeptides; (2) most polypeptides are catalysts, i.e. enzymes or translocators; (3) the molecular components of all traits in all cells are the products of systems of enzymes, i.e. of fluxing metabolic pathways; (4) any flux to the molecular components of a trait responds non-linearly (non-additively) to graded mutations in the activity of any one of the enzymes at a catalytic locus in a metabolic system; (5) as the flux responds to graded changes in the activity of an enzyme, the concentrations of the molecular components of a trait also change.It is then possible to account rationally, and without misrepresenting Mendel, for: the origin of dominant and recessive traits; the occurrence of Mendel's 3(dominant):1(recessive) trait ratio; deviations from this ratio; the absence of dominant and recessive traits in some circumstances, the occurrence of a blending of traits in others; the frequent occurrence of pleiotropy and epistasis. AA + 2Aa + aa) and the dominant, hybrid and recessive traits written as (AA + 2Aa + aa). This assumption  to call alleles dominant or recessive, and to represent them by the same letters used by Mendel to represent traits [(i) A failure to distinguish between the parameters and the variables of any system of interacting components, specifically between the determinants  and what is determined (the form of the trait or characteristic expressed in a cell or organism). Thus, because Mendel defined the terms dominant and recessive for t traits .AA + 2Aa + aa) suggests, incorrectly, that dominant and recessive traits comprise two aliquots, (A + A) or (a + a), of dominance or recessivity.(ii) A trait series written as ((iii) A failure to take account of the long established fact that the first non-nucleotide product of the expression of an allele is a polypeptide and that most polypeptides are enzymes or membrane-located translocators.(iv) A failure to note that the components of all tangible traits comprised the molecular products of metabolic pathways, i.e., the products of sequences of enzyme-catalysed reactions.UU + 2Uu + uu) and the corresponding trait series as (A + 2H + a). In these statements (U) and (u) are normal and mutant (not dominant and recessive) alleles respectively. Mendel's notation (A) and (a) is used to represent dominant and recessive traits but (H) replaces Mendel's implausible notation (Aa) for a hybrid class of trait [WW + Ww + ww); the corresponding trait series will appear as (B + 2H + b). Mendel's notation (Aa) for a hybrid trait will be used in this article only when referring directly to Mendel's paper [Correction of the first two of these four faults has already been achieved (section 4 in reference ) by writof trait . MutatioUU + 2Uu + uu) is expressed as a series of qualitatively distinguishable F2 traits (A + 2H + a) when F1 hybrids (H) are allowed to self-fertilise [Our stated task was to explain logically how an allele series .E. Any segment of a sequence of enzyme-catalysed reactions can then be written as shown in Figure Metabolic processes are facilitated by a succession of catalysed steps; i.e. by enzyme-catalysed transformations of substrates to products or by carrier-catalysed translocation of metabolites across membranes. Because enzymes and membrane-located carriers (or porters) are saturable catalysts that exhibit similar kinetics it is convenient in this article to refer only to enzymes and to represent both kinds of catalysts by the letter There are ten important features of any such system.E1 to E6, is embedded within a metabolic pathway, i.e. within a system of enzymes.(1) Each enzyme, external metabolites X0 and X6 are enclosed by a membrane.(2) All components of this system except the E1 and E6 may then represent membrane-located enzymes or translocators.(3) X0 and X6 interact with only one enzyme, whereas each internal metabolite (S1, S2, S3, S4, S5) interacts with two flanking enzymes.(4)E) at each catalytic locus. In a diploid cell there will be two specimens of enzyme molecules  at each catalytic locus: one specified by the maternal allele, the other by the paternal allele, at the corresponding gene locus or loci. The effective catalytic activity at each metabolic locus in a diploid will be, in the simplest case, the sum of the two individual activities. It is the single effective enzyme activity (v) at each catalytic locus that concerns us here, irrespective of whether the cell is haploid, diploid or polyploid.(5) In a haploid cell there will be one specimen of an enzyme molecule (v) at any one metabolic locus can be left at its current value or changed to and maintained at a new value by the experimentalist, e.g. by suitable genetic manipulation of an allele. Each allele in these circumstances is therefore an internal parameter of the system; it is accessible to modification by the direct and sole intervention of the experimentalist [(6) The catalytic activity  Because X0 and X6, the concentrations of metabolites S1 to S5 within the system cannot be fixed and maintained at any desired value solely by the direct intervention of the experimentalist. The concentrations of S1 to S5 are internal variables of the system. (If a fixed amount of any one of these metabolites were to be injected through the membrane into the system, continued metabolism would ensure that the new intracellular metabolite concentration could not be maintained).(8) In contrast to v) and the flux (J) through the system are also variables of the system.(9) By the same arguments, each reaction rate (concentrations (s1, s2, s3, s4,s5) of the intracellular metabolites shown in Figure v1, v2, v3, v4, v5, v6; and the flux J through this system of enzyme-catalysed steps.10) The magnitude of each variable of the system is determined at all times by the magnitudes of all the parameters of the system and of its immediate environment. The variables comprise the 0 The magX0 and X6 constant, the system depicted .S1 to S5 will settle to an individual steady value.At the same time the concentration of each intracellular metabolite activity of any one enzyme depends, at all times, on the activity of the adjacent enzyme; and this is true for every pair of adjacent enzymes throughout the system .In a metabolic system, the product of any one enzyme-catalysed reaction is the substrate for the immediately adjacent downstream enzyme Figure . If, forX6 is a terminal product. X6 is not an intermediate metabolite, flanked by two adjacent enzymes; it is not a substrate that is further metabolised by the system depicted. There are instances where an intracellular terminal product is formed. We must therefore add the words in parentheses if the statement is to apply generally].[This last statement is obviously still true for the system in Figure v) of one enzyme at the corresponding metabolic locus; but, for reasons just stated in the first paragraph of this section 2.3, the activity (v) of each of the other enzymes will alter, the flux (J) will change, and the concentrations of all the metabolites (S1-S5) will also change, some more than others, until the system settles to a new steady state.A finite change (by mutation) in any one allele at a locus will change the activity  would be the sole cause of any changes in the system variables. In short, we are obliged to adopt a whole-system (a systemic) approach if we want to understand how the flux to a trait component responds to a change in any one internal or external parameter of the system, no matter how that change in a parameter value is brought about. We are here concerned with changes in any one internal parameter such as a mutation in one or both alleles of a diploid cell.Thus, finite changes in the magnitude of E1 to E6 in Figure Suppose the activity of any one of the enzymes Neurospora crassa ([Saccharomyces cerevisiae [It was shown, by experiment, that graded changes in the activity of any one of four different enzymes in the arginine pathway resulted in a non-linear (quasi-hyperbolic) response of the flux to arginine in constructed heterokaryons of  crassa  had little effect on respiration until the enzyme activity was decreased to about 25% of normal; further decreases in this one enzyme activity caused a precipitous, continuously non-linear, decrease in mitochondrial respiration . Other eHow did the observations of non-linear responses of individual fluxes to graded changes in any one enzyme activity lead to a rational explanation for the origin of Mendel's dominant and recessive trait classes ? For reauu,uU,UU) in determining the outcome of mutations or changes in gene dose is set aside; this role will be considered in Section 2.8. For the moment, attention is focussed on what can be learned from the non-linear response of a flux – to the molecular component(s) of a trait – when the activity of one enzyme in a metabolic system is changed in graded steps by mutation or by changes in gene dose. Figures Neurospora crassa before any significant diminution in the flux to arginine (and in the concentration of arginine) was detectable [In this Section 2.4, and in Sections 2.5–2.7, consideration of the role of allele pairs . FigureA) corresponded to the normal (100%) activity of the enzyme that was subsequently mutated to give lower activities [versus trait A) defined the terminus of the asymptote of the flux response plot depicted in Figure H) must then correspond to any point on the asymptote of Figure H) from its parent that displayed a dominant trait (A). A recessive (a) must then correspond to any point on the steeply falling part of the flux-response plot  and the hybrid (H) from the recessive trait (a), e.g. dominant trait red flowers and hybrid red flowers from the recessive trait white flowers [necessarily correspond to zero flux  in Figure It was shown that a dominant trait  there will be a non-linear response of the flux to the molecular component(s) of a defined trait; and concurrently a non-linear response of the concentrations of the normal molecular components of a trait (section 2.3).The paper by Kacser and Burns  thus expa) were displayed in 1/4 of his F2 population of plants (A + 2Aa + a) when these same recessive traits were not displayed in Mendel's hybrids (Aa). We have replaced Mendel's implausible idea that his F1 hybrids (Aa) displayed only trait (A). We have substituted the plausible idea – based on experimental evidence [H) is indistinguishable from trait (A). In the treatment advocated here, there is no problem in understanding how 1/4 of the individual plants in the F2 population of genetically related plants (A + 2H + a) displayed the recessive trait (a). We can now also see why Mendel emphasised the need to study crosses between parental plants that displayed readily distinguishable trait forms, e.g. red flowers (A) in one parent and white flowers (a) in the other [Section 2.9 in reference  showed tevidence  – that, trait dominance and trait recessivity are not independent phenomena (nor are they opposite, one to the other). We cannot define a dominant trait except as an alternative to a recessive trait; both traits must be observable before we can identify either of them. The statements in these last two sentences were obvious in Mendel's original paper [Note too that al paper  but theyorigin of dominant and recessive traits also account for the occurrence of Mendel's 3(dominant):1(recessive) trait ratio? The answer is yes. Does it also explain why this ratio is not always observed? The answer is again, yes  is indistinguishable from the trait (A), as in Figure A + 2H + a) becomes 3(A) + (a); i.e. the trait ratio in this population will be 3(dominant):1(recessive). This explanation for the occurrence of the 3:1 trait ratio in Mendel's, or any other F2 population of cells or organisms, depends entirely on an experimentally observed, sufficiently asymmetric, response of the flux (to the molecular components of defined trait) when changes occur in enzyme activity at any one metabolic locus in a fluxing biochemical pathway  can be clearly distinguished from the dominant trait (B) because the concentrations of the molecular components of this trait were sufficiently different when one enzyme activity in the metabolic system is decreased to a sufficient extent. The trait displayed by the hybrid (H) is now distinguishable (rather than indistinguishable) from the dominant trait (B) expressed in a genetically related normal cell or organism when, as in Figure B) and (b) is possible in the hybrid (H), for example when traits (B) and (b) are distinguished by colour differences.Figure It is well known that dominance and recessivity are not universally observed. Are they therefore of no significance? Some authors have been tempted to think so. Their view is understandable because, before the work of Kacser and Burns , we lackand recessive traits in genetically related organisms only when the enzyme activity at a metabolic locus is decreased from 100% to an activity approaching, but not necessarily reaching, 0% activity.Can we now see why dominance and recessivity are not always observed? The answer is again, yes. Examination of Figure traits).When the response plot is of the kind shown in Figure Only if the effective enzyme activity is decreased by at least 95% in this instance Figure , would cconcentrations a normal metabolites has been treated in the present article as the source of a change in trait. This accords with the treatment in Figure The change in the UU, Uu, uu in a diploid cell (Section 1) only if the mutant allele (u) was expressed as a catalytically inactive polypeptide. The same considerations apply to the relative enzyme activities expressed from the allele pairs WW, Ww, ww in Figure In Figure UU, Uu, uu or WW, Ww, ww) in which the mutant alleles (u or w) express a catalytically inactive polypeptide.It is obvious that the continuously non-linear response plots , incorrectly, as (AA + 2Aa + aa) and the number of distinguishable chromosome pairs as (AA + 2Aa + aa), so establishing a false one-for-one relationship between pairs of chromosomes (AA or aa) and dominant or recessive traits (AA or aa). Sutton's notation for chromosome pairs was later transferred to allele pairs. In this article, dominant and recessive traits are represented, as Mendel did, by (A) and (a) respectively; alleles have been represented by different letters (e.g. UU, Uu, uu) in order to distinguish alleles (parameters) from traits (variables). We should allow for the situation where (U†) is a mutant of (U) that would express an allozyme activity lower than that expressed from (U) but not so low as that expressed from (u); and where (u*) would be a mutant of (U) that expresses an allozyme activity greater than that expressed by (u) = 0 in the traditional treatment but not so great as to merit the notation (U). The outcome of different hypothetical crosses that involve different mutations of one both alleles at a given locus in genetically related diploid parents would then be as follows:The restriction to just three allele pairs in a diploid may be traced to Sutton . He wrotUu × Uu) would give, on average, the allele series (UU + 2Uu + uu) thus permitting expression of no more than three distinctive enzyme activities at the corresponding metabolic locus.(1) Repeated crosses (Uu* × Uu) would give the allele series (UU + Uu + Uu* + uu*) in which two of the allele pairs differ from those in the progeny of the first cross; and in which three different heterozygotes are formed.(2) The cross (U†u × Uu) would give the allele series (UU† + Uu + U†u + uu) in which only one allele pair in the progeny populations is identical with one of the allele pairs in the progeny from the second cross.(3) The cross (UU† × Uu) would give, on average, the allele series (UU†+ UU + Uu + U†u) which has only two allele pairs in common with the progeny of the third of these crosses of genetically related parents.(4) The cross (U†u × Uu*) would give, on average, the allele series (UU†+ U†u* + Uu + uu*).(5) The cross (U) at this particular locus so, among their progeny, the allele pair (UU) occurred. Analogously, among the progeny from the third cross, the allele pair (uu) occurred. But, importantly, in each of crosses (2), (3) and (4) three different heterozygotes occurred in each progeny population . The allele pairs in the heterozygotes in any one progeny population of these crosses (2), (3) and (4) are not all identical with those in the progeny of another of these crosses. The parents in the fifth cross did not share an identical allele; no two alleles of a pair are then identical in the progeny. The allele pair (Uu) occurs in all of the progeny of these five crosses but only because one of two parents carried this allele pair or because one parent carried allele (U) and the other carried allele (u).In the second and fourth crosses it was assumed that the two heterozygous parents did possess exactly the same normal allele -(5) between two heterozygous parents will produce, under the circumstances noted above, truly homozygous allele pairs (such as UU and uu) but they will also produce, on average, three different heterozygotes among their progeny (four heterozygotes in the fifth cross).Cross 1) typifies events in self-fertilising organisms but is not typical of sexual reproduction in other organisms  will be expressed; one from the gamete donated by the male parent the other from the gamete donated by the female parent. For simplicity, it will be assumed here that the combined allozyme activity at each catalytic locus in the metabolic pathways of the cell is the sum of the activities the two allozymes at each such locus.UU + 2Uu + uu) in a diploid will then generate the enzyme series (EE + 2Ee + ee) at one metabolic locus in different, genetically related, individuals. This enzyme series provides two extreme combined allozyme activities, namely 100% (EE) and 0% (ee). There are no allele pairs at this locus that could provide <0% or >100% enzyme activity. All other allele pairs, e.g. (UU†), (U†u), (U†u*), (Uu*), (uu*), would provide combined allozyme activities that lie between the 100% and 0% values just described. Only if (u) happens to be a null mutant, will the heterozyote (Uu) express a single enzyme activity (v) equal to 50% of the maximum available from (UU). Only in this circumstance will the allele pair (uu) express two inactive polypeptides; the enzyme activity will then be zero at a metabolic locus and a "metabolic block" will occur at that locus.The traditional allele series (UU, UU†, U†u, Uu, Uu*, uu*, uu). They would contribute seven different allozyme pairs (EE, EE†, E†e, Ee, Ee*, ee*, ee) at one metabolic locus and seven different, single, enzyme activities (v), one from each pair of allozymes. Given a range of enzyme activities in excess of the traditional three, a sufficient number of co-ordinates will be available to establish a continuously non-additive plot of the response of one defined flux (J) against changes in enzyme activity (v) at one metabolic locus in genetically related cells or organisms  and (u*) each represent only one of several possible mutations of allele (U), we may be reasonably confident of observing traits expressed from allele pairs in addition to, or instead of, those expressed from the two traditional mutant pairs (Uu) and (uu). Assembling sets of enzyme activity and flux (or metabolite concentration) data from the progeny of different but genetically related parents then creates the non-linear flux response plots illustrated in Figures Assembling the data from, for example, the second and third of the three hypothetical crosses between the genetically related parents described above gives an allele series  in a diploid allele series at a locus, and on only one corresponding hybrid trait. Furthermore, statements that all heterozygotes express 50% (and only 50%) of the phenotype expressed from the homozygous wild-type are based on the false idea that the mutant allele (u) always produces a totally inactive enzyme. Figures Uu), there must be a number of heterozygotes (e.g. UU†, U†u, Uu*, uu*), and a corresponding a range of enzyme activities (v), that account for the response of a flux (J) to a change in enzyme activity at one metabolic locus ; they could have accounted for the occurrence of Mendel's hybrids (Aa) which appeared to display only the dominant trait (A). This is further evidence that the traditional treatment of elementary Mendelian genetics is inadequate and misleading [This simple explanation for the occurrence of more than three co-ordinates for a plot of flux response against changes in enzyme activity (or gene dose) means that it is no longer acceptable to base arguments and conclusions on the assumed presence of only one heterozgote  where (A) indicates the dominant trait form, (a) the recessive trait form and (H) a hybrid trait that may be indistinguishable  or distinguishable .These comments apply to the apparently qualitative differences examined by Mendel  were not changed. Their concentrations were changed as graded mutations at a gene locus caused graded changes in one enzyme activity in those pathways that created arginine, biomass, melanin, or the products (of ethanol metabolism). Similarly, a change in the "flux to DNA repair" was achieved by graded increases in the dose of the gene specifying the synthesis of the "repair enzyme" that excises covalently-linked adjacent thymines in DNA and allows incorporation of thymidine in place of the excised pyrimidines. This "repair enzyme" activity is absent in Xeroderma pigmentosum patients.The flux responses were labelled "Flux to arginine", "Flux to biomass", "Flux to melanin", "Flux to products", "Flux to DNA repair"  or (aa). The single letters (A) and (a) stood for qualitative differences in trait form in Mendel's work; they stand equally well for quantitative changes in a trait in modern work. The non-linear response plots of Kacser and Burns [Additional examples of quantitative changes in the concentration of molecular components of a trait will be found in other publications -11. Nonend Burns  apply toincreasing the dose of this particular gene in a wild-type haploid cell  is unlikely to produce a detectable change in the phenotype . It was demonstrated that it was necessary, under these circumstances, to increase concurrently the gene dose at each of no fewer than five loci if significant increases in the flux (and in the concentration of metabolic product) was to be achieved [Figure achieved . The sysachieved  has obviFigure decreases in the dose of normal alleles at any one locus (or in the enzyme activity at the corresponding metabolic locus) may not elicit detectable changes in the trait(s) of the cell. In other words, given a response plot approximating to that shown in Figure It was pointed out section 2.6) that substantial .6 that sThis proposed explanation for metabolic buffering is quite general; it does not depend on the particular kinetic mechanisms that have been suggested to account for this buffering ; it alsoAttempts to improve the concentration of metabolic products by increasing the gene dose at one locus above that available in the wild-type or normal cell could be successful, at least to some self-limiting extent, if a response plot like Figure If the explanation for the origin of dominant and recessive traits depends on realising that fluxing metabolic pathways generate the molecular components of all traits, and that mutating any one enzyme in these pathways alters the flux and the concentrations of those normal metabolic products that are molecular components of a trait, other genetic phenomena could perhaps also be explained. Only two of the thirteen texts surveyed  gave a dp, q). The sum of the concentrations of (p) and (q) is constant (is conserved) but the ratio of the two concentrations (p/q) is a free variable. All the characteristics of the metabolic system in Figure p/q) controlled metabolic fluxes were and remain untenable; one variable of a system cannot be said to control another variable of the system.The following account, like those preceding it, does not depend on the fiction that all mutations generate inactive enzymes. Figure X1 to S4 divides into two output fluxes [Ja) and a proportion (1-α) enters the other output flux (Jb). The magnitude of (α) is determined by the magnitudes of the activities of all the enzymes of the metabolic system; (α) is a systemic characteristic [Ja and Jb shown in Figure Figure t fluxes . Of the teristic . Again, Ja and Jb. Pleiotropy (a change in more than one trait as a consequence of a single mutation), when it is detected, is thus seen to depend on mutating an enzyme within a metabolic pathway, on the consequential changes in metabolite concentrations, and on the structure and interdependence of biochemical pathways. Only if one of the enzymes in the input pathway shows zero activity will both output fluxes (Ja and Jb) cease  would decrease flux Ja and increase flux Jb. The concentrations of metabolites in pathway Ja would decrease and those in pathway Jb would increase, a further example of a pleiotropic response to a single mutation. But suppose that, following the mutation of E5a, a mutation occurred in E6b or any other enzyme in this alternative output limb. Clearly, the effect of the first mutation on the cell characteristics would be at least partly nullified by the second mutation – a phenomenon known as epistasis and sometimes attributed in genetic texts to an interaction between genes but shown here to depend on mutations of one or more enzymes, and on the structure and interdependence of metabolic pathways. Only if the activity of one of the enzymes in one of the two output pathways is diminished to zero by mutation, will the products of that output limb downstream from the mutation be lost.Given a steady input flux from 4 Figure , a mutatIf the fluxes proceeded in the opposite direction to that shown in Figure Particular but common metabolic structures Figures , 5 proviNeurospora crassa and tested their ability to grow on basal medium or on basal medium supplemented with different metabolites or cofactors. Wild-type Neurospora crassa grew on basal medium. Different isolated mutants would grow only if the basal medium was supplemented with the specific product of an enzyme rendered partially or fully inactive in one of the mutants. These brilliant observations led to the paradigm "one gene, one function" [Beadle and Tatum  isolatedunction" ,20, lateunction"  made expunction" -24] on iunction" ).Beadle  expresseThis lack of attention to the implications of Garrod's work is all the more surprising when it is recalled that Bateson [, p.133] indirectly by a system of enzymes  most of which, but not all, are enzymes (E1, E2, E3, E4). All polypeptides, catalytic and non-catalytic, are synthesised in this way.Figure X1, X2 and X3 in Figure X3 stands for those substrates that lead, through a series of enzyme-catalysed reactions, to the synthesis of nucleoside triphosphates (NTPs) and their subsequent incorporation into mRNA. Note that mRNA is a terminal product of this pathway. It is a coding entity, a proxy for DNA. Each mRNA specifies the order of incorporation of individual amino acids into a polypeptide, but no individual mRNA molecule participates as a substrate in the subsequent steps of the catalysed formation of a polypeptide. The control of the overall expression of a gene as a polypeptide is therefore necessarily treated in Metabolic Control Analysis as a cascade of two fluxing metabolic pathways, one that starts at X3, the other that starts at X2 [ts at X2 .X2 stands for those extracellular substrates that lead, through a series of enzyme-catalysed reactions, to the synthesis de novo of amino acids (AAs) and their subsequent incorporation, along with any existing amino acids, into a polypeptide (P). In a haploid cell, one polypeptide is synthesised from each gene locus. In a diploid, one polypeptide is synthesised from each of two alleles at a gene locus. If these pairs of polypeptides are catalytically active, each enzyme in a diploid cell (E1, E2, E3, etc) consists of a pair of allozymes, one of each pair specified by the allele derived from the male parent, the other specified by allele derived from the female parent. Each pair of allozymes, whether normal or mutated, exhibits only one measurable activity (v) at a catalytic locus in a metabolic pathway. If the pairs of polypeptides (P) synthesised by a diploid cell are not catalytically active they will not, of course, play a direct role in catalysing a metabolic pathway. They may have other important functions (e.g. as hormones) and may be components of traits.X1 stands for all those initial extracellular substrates feeding the matrix of inter-dependent biochemical pathways that typify all functioning cells. It is these pathways that generate the non-protein, non-polyribonucleotide, molecular products of all cell traits.Each of these three major fluxing pathways Figure  is catalX1 for the simple reason that this pathway stands for all the matrix of interdependent biochemical fluxes that generate such a wide range of the non-protein (and non-polyribonucleotide) molecular components of cell traits .Most attention is concentrated on the pathway initiated by X1.If any one of the three major pathways shown in Figure If the implications of the work of Beadle and Tatum  were notX1, X2, X3in Figure R) in permitting extracellular effectors to modulate intracellular metabolism (and thus the effects of a mutation) will be found elsewhere [Note that changes in the concentrations of external metabolites (whether they are substrates like lsewhere ,34-36.If pleiotropic and epistatic responses to a mutation are as common as is suggested (sections 5.1–5.4), the question then arises: how do we account for Mendelian segregation of traits during sexual reproduction? The answer lies in the fact that a mutation at a biochemical locus, within the matrix of interdependent pathways, has its most obvious effect on the most closely associated pathways. Distant pathways  will be less obviously affected. Kacser and Burns (Reference , p.649) The explanation advocated in this article for the origins of dominant and recessive traits from normal and mutant alleles in a diploid is based on:(i) An obligatory distinction, by notation and nomenclature, between the variables (traits) and the parameters  of genetic/biochemical systems.(ii) The contention that the molecular components of all traits are the products of fluxing metabolic systems Figures , 4, 5, 6(iii) Experimental evidence for an inevitable non-linear response of a flux (through a metabolic system of enzymes) to graded changes in the activity of any one of those enzymes , evidenc(iv) A demonstration that dominant and recessive traits arise from changes in the concentration of the normal molecular components of a defined trait.(v) The argument that changes in concentration of a trait component may nevertheless be revealed as a qualitative change in that trait.(vi) A demonstration that both alleles  at a locus in a diploid are generally expressed. If the normal allele expresses a catalytically active polypeptide, many mutants of this allele will express an enzyme with lower activity; a mutated enzyme with zero activity is an extreme case.uu, 2uU, UU) one of which is a unique heterozygote (uU).(vii) The demonstration that an explanation of Mendel's observations cannot be based on an allele series containing only three terms (e.g. (viii) A demonstration that dominant and recessive traits cannot be generated by those polypeptides that are not enzymes embedded in a system of enzymes.H) expresses a dominant trait (A) because the  recessive allele (u) in a heterozygote (Uu) is always completely ineffective or because the allegedly dominant allele (U) suppresses the allegedly recessive allele (u) in the heterozygote [(ix) Rejection of the unjustified traditional claim that a hybrid (rozygote .(x) Rejection of the traditional, unsubstantiated and implausible claim that one so-called dominant allele in a heterozygote is as effective as two such alleles in the wild-type cell .It was also shown that pleiotropy and epistasis can be explained by taking a similar system approach to that used in explaining the origin of dominant and recessive traits.It is then apparent that, to account rationally for Mendel's observations of dominant and recessive traits, a minimum of four conditions must be met.(i) Alleles must be distinguished by notation, nomenclature and concept from traits; functions of components of the genotype must be distinguished from properties of components of the phenotype. Traits alone may be dominant or recessive.(ii) Alleles cannot be called "dominant" or "recessive". .(iii) It must be shown how dominant traits become distinguishable from recessive traits in the same cell or organism Figure , 3.(iv) It must be shown how a hybrid trait sometimes becomes indistinguishable from the dominant trait and sometimes does not Figures , 3. The If all four conditions are be met; the first two conditions must first be met. The treatment given in sections 2–5 meets each of these requirements.origin of dominant and recessive traits that arose from mutations of alleles at any one gene locus in a diploid or polyploid cell (sections 2.3, 2.4). Inherent in this explanation, as set out above (sections 2.5, 2.6), are further explanations for the occurrence of the 3(dominant):1(recessive) trait ratio in some situations in a diploid  may produce a trait that is indistinguishable from the trait expressed from the "homozygous" allele pairs (UU).Kacser and Burns  providedsections .3, 2.4. In other words, provided:(a) all current misrepresentations of Mendel's paper  are firs(b) alleles are distinguished by notation and nomenclature from the traits they specify,(c) alleles are regarded as normal or mutant (but not dominant or recessive), it is possible to provide a rational and internally consistent explanation for the origin of Mendel's dominant and recessive traits, for the occurrence of his 3:1 trait ratio, and for exceptions to these observations noted by later investigators. The same systemic approach is applicable to current problems in biotechnology and medical genetics (section 4). It also explains the origins of pleiotropy and epistasis (section 5); and challenges the assumption that a mutation in a non-catalytic protein provides an example of Mendel's dominant and recessive traits [A + 2Aa + a). In the present paper these proportions have been written as (A + 2H + a). If the symbol (H) for a hybrid in Figure Aa), it will be clear why Mendel postulated that his hybrids (Aa) displayed trait (A) and not trait (a). If the same exercise is repeated in Figure H) temporarily by (Bb), it will be clear why Mendel observed an anomalous blending of flower colours in the hybrids when he crossed parental bean plants bearing different flower colours.Mendel found, by experiment, that the proportions of plant forms in each of his F2 populations was represented by  Persistent misrepresentations of Mendel's paper, and incorporation of these distortions into currently favoured explanations of Mendel's observations [ Persistespecify the kind (and order of incorporation) of amino acids into polypeptides . Dominance and recessivity are a reflection of changes in the concentration(s) of the molecular component(s) of a trait when an enzyme is mutated within a fluxing metabolic pathway.(2) A failure to recognise the consequences of not distinguishing between the function of the alleles and the properties of traits in attempting to explain Mendel's results. Normal and mutant alleles variables of a biological system to perturbations of the magnitude any one system parameter.(3) Tardy recognition of the need to adopt the systemic approach of Metabolic Control Analysis in explaining the response of the (4) A reluctance to accept a change in concepts even when currently accepted representations of Mendel's results are demonstrably untenable.(5) Elucidation of the double helical structure of DNA Figure  and all (6) A determination in some quarters to regard genetics as an autonomous subject. It has been obvious at least since the work of Beadle and Tatum  that sucIt is significant that Kacser & Burns were also one of two sets of authors who initiated the systemic approach to the control of metabolite concentrations and fluxes ,40. ThisA) and (a). These statements are here formally withdrawn. They were consistent with textbook treatments of Mendelian genetics [In an earlier paper  it was sgenetics  but a sugenetics  will be