Noroviruses are understudied because these important enteric pathogens have not been cultured to date. We found that the norovirus murine norovirus 1 (MNV-1) infects macrophage-like cells in vivo and replicates in cultured primary dendritic cells and macrophages. MNV-1 growth was inhibited by the interferon-αβ receptor and STAT-1, and was associated with extensive rearrangements of intracellular membranes. An amino acid substitution in the capsid protein of serially passaged MNV-1 was associated with virulence attenuation in vivo. This is the first report of replication of a norovirus in cell culture. The capacity of MNV-1 to replicate in a STAT-1-regulated fashion and the unexpected tropism of a norovirus for cells of the hematopoietic lineage provide important insights into norovirus biology. Noroviruses -- which cause epidemic gastroenteritis -- can now be grown in cells of the innate immune system, providing a tool to examine this pathogen while offering insights into norovirus biology Norovirus  of the family Caliciviridae are major agents of acute gastroenteritis (−/−) mice (Viruses within the genus nteritis . Norovirnteritis . Most nonteritis . Our recnteritis . This pr/−) mice  that areHere we show for the first time that MNV-1 grows in macrophages (MΦ) and dendritic cells (DCs) and provide the first tissue culture model for a norovirus. Using this model we demonstrate that MNV-1 growth in vitro was inhibited by the IFN-αβ receptor and STAT-1. In addition, we isolated the first three-times plaque-purified strain of MNV-1 (MNV-1.CW1) and characterized it in vitro and in vivo. Sequencing of serial passages of MNV-1.CW1 indicated remarkable sequence stability over time and indicated that an amino acid substitution in the capsid protein of serially passaged MNV-1 was associated with a loss of virulence in vivo.−/− mice were infected with MNV-1 by the oral route and tissue sections analyzed by immunohistochemistry for the presence of MNV-1 protein. MNV-1-specific staining was observed in spleen and liver 2 d postinfection  mice   and bone-marrow-derived DCs (BMDCs) were inoculated with a MNV-1 stock derived from the brain of infected IFNαβγ receptor/−) mice . Cytopats (MEFs) A. While We used this information to screen available MΦ cell lines for growth of MNV-1, including the murine lines RAW 264.7  and J774−/− BMMΦ, STAT1−/− and wt BMDCs, and RAW 264.7 cells consistently supported the growth of MNV-1, while wt BMMΦ varied in their ability to support virus growth  with three rounds of plaque purification in RAW 264.7 cells to generate the MNV-1.CW1 strain. This strain was amplified in RAW 264.7 cells, after which virus particles were concentrated and subjected to purification by isopycnic centrifugation in CsCl. A distinct band was observed in CsCl gradients at a density of 1.35 ± 0.01 g/cmoviruses . Examinaoviruses A. Furtheoviruses B,C. Westoviruses B and masoviruses , did notoviruses D. MAb A6oviruses E. TogethTo compare MNV-1 replication in cells with that of other caliciviruses, we analyzed viral RNA and protein synthesis in MNV-1.CW1-infected RAW 264.7 cells. Northern blot analysis using a probe specific for the positive strand of the MNV-1 genome showed an increase in the accumulation of full-length (7.4 kb) and subgenomic-length (2.3 kb) MNV-1 genome over time F. RadiolPositive-strand RNA viruses , includi−/− MΦ and MEFs  growth was consistently observed in wt BMMΦ. These data demonstrated that our plaque purification and serial passage in RAW 264.7 cells had not changed the tropism of the virus for primary DCs and MΦ and confirmed the importance of STAT-1 in controlling MNV-1 growth at the cellular level.To determine whether the plaque purification and sequential amplification of MNV-1 in RAW 264.7 cells had altered its growth characteristics, different cell types were infected with passage (P) 3 of MNV-1.CW1. In general, the growth of MNV-1.CW1 (P3) in wt or STAT1and MEFs A as welland MEFs B and 5A.−/−, or protein kinase R (PKR)−/− are not susceptible . Therefore, we determined whether molecules other than STAT-1 exhibited antiviral effects at the level of the infected cell. Primary BMMΦ from wt mice or mouse strains deficient in STAT-1, IFNαβR, IFNγR, IFNαβγR, iNOS, or PKR were directly compared for their ability to support virus replication at two different multiplicities of infection (MOIs)  or Type I IFNR alone (IFNαβR−/−) supported replication of virus as efficiently as STAT1−/− cells. In addition, wt BMMΦ and wt BMDCs secrete IFNα after MNV-1-infection, as determined by IFNα enzyme-linked immunosorbent assay (ELISA) (data not shown). This is consistent with a direct role for IFN signaling in MNV-1 growth but does not rule out the possibility that effects of STAT-1 and IFNαβR occur in vivo prior to explantation of the bone marrow. Absence of IFNγR, iNOS, or PKR did not have a statistically significant effect on MNV-1 growth in BMMΦ. Together, these data demonstrate that the antiviral molecules STAT-1 and IFNαβ are part of a cellular response that limits norovirus growth.Previous studies demonstrated that a lack of STAT-1 or both IFNαβR and IFNγR increase susceptability to MNV-1 infection. Mice lacking individual IFNR, inducible nitric oxide (iNOS)n (MOIs) B. Again,−/− mice were infected orally with MNV-1.CW1 from three successive passages  , 5,000 times greater than the lethal dose for P1. In addition, immunohistochemical analysis of sectioned spleen and liver from STAT1−/− mice infected orally with 1.5 × 106 pfu of MNV-1.CW1 (P3) did not reveal any MNV-1-specific staining, unlike the parental virus  A. Oral a and P3) . These d and P3) . In contTo examine the molecular basis for this attenuation, consensus sequence analysis was performed on the RNA genome of MNV-1 present in the original brain tissue stock , and in viruses from each subsequent cell culture passage of MNV-1.CW1 (P1 through P3) . Three n−/− mice led to our finding that MNV-1 grows in these cell types in vitro. This provides the first tissue culture model for a norovirus. In addition, the antiviral Type I IFN response with signaling through STAT-1 is crucial for resistance to murine norovirus infection in vivo and in vitro. Taken together the previous in vivo data . IFNαβR−/−, IFNγR−/−, and IFNαβγR−/−  or brain homogenate from uninfected mice. Organs were collected into 10% buffered formalin and embedded in paraffin for sectioning by standard methods. Immunohistochemistry was performed as described previously  per well. Plates were incubated at 37 °C and 5% CO2 for 2 d. To visualize plaques, cells were stained with 2 ml of 56 °C 1.5% SeaKem agarose in complete MEM containing 1% neutral red per well for 6–8 h.RAW 264.7 cells were seeded into six-well plates at a density of 2 × 10For plaque neutralization assays, differing concentrations of purified mAb  were incubated with equal plaque-forming units of either MNV-1.CW1 or MNV-1 brain homogenate for 30 min at 37 °C prior to performing the MNV-1 plaque assay.g) in a SW32 rotor. Cell pellets were then resuspended in PBS and mixed with CsCl to a final density of 1.335 g/cm3 and centrifuged for at least 18 h at 35,000 rpm  in a SW55 rotor. A wide lower band (1.35 ± 0.01g/cm3) and narrow upper band (1.31 ± 0.01g/cm3) were typically seen in the gradient. Each band was collected by puncturing the side of the tube with a needle before overnight dialysis against PBS at 4 °C.RAW 264.7 cells were infected with MNV-1.CW1 for 2 d at an MOI of 0.05. Cellular debris was removed from freeze/thaw lysates by low-speed centrifugation for 20 min at 3,000 rpm. Supernatants were layered on top of a 5-ml 30% sucrose cushion and centrifuged at 4 °C for 2.5 h at 27,000 rpm  according to the manufacturer's recommendations. One microgram of total RNA from MNV-1- or mock-infected cells was subjected to electrophoresis on a 1% formaldehyde gel. RNA Millennium Size Markers  were used as size markers. Northern blotting was performed using standard protocols  vector between the T7 and SP6 promoters. The resulting plasmid was linearized with Bsu361 and in vitro transcribed with SP6 RNA polymerase  to generate RNA transcript probes for detection of positive-sense viral RNA, or with T7 polymerase (Roche) to generate transcripts for detection of negative-sense viral RNA. To label probes, the transcription reaction was carried out in the presence of [Protocols . Probes The ELISA was performed as described previously  with theNegative staining electron microscopy of CsCl-purified virions was performed as described previously . For thiRNA was extracted from brain tissue or cell culture material with Trizol (Invitrogen) and reverse transcribed with Superscript II enzyme (Invitrogen). Genome-specific sequences were PCR-amplified with Elongase enzyme (Invitrogen) to produce seven overlapping fragments. The DNA fragments were gel-purifed and sequenced directly with reagents in the BigDye Terminator version 3.1 Cycle Sequencing Kit  on a 3100 DNA sequencer (Applied Biosystems). Data were analyzed with the Sequencher software package . Oligonucleotide primer sequences are available upon request.


There have been few reports of pharmacokinetic models that have been linked to models of the cardiovascular system. Such models could predict the cardiovascular effects of a drug under a variety of circumstances. Limiting factors may be the lack of a suitably simple cardiovascular model, the difficulty in managing extensive cardiovascular data sets, and the lack of physiologically based pharmacokinetic models that can account for blood flow changes that may be caused by a drug. An approach for addressing these limitations is proposed, and illustrated using data on the cardiovascular effects of magnesium given intravenously to sheep.The cardiovascular model was based on compartments for venous and arterial blood. Blood flowed from arterial to venous compartments via a passive flow through a systemic vascular resistance. Blood flowed from venous to arterial via a pump (the heart-lung system), the pumping rate was governed by the venous pressure (Frank-Starling mechanism). Heart rate was controlled via the difference between arterial blood pressure and a set point (Baroreceptor control). Constraints were made to pressure-volume relationships, pressure-stroke volume relationships, and physical limits were imposed to produce plausible cardiac function curves and baseline cardiovascular variables. "Cardiovascular radar plots" were developed for concisely displaying the cardiovascular status. A recirculatory kinetic model of magnesium was developed that could account for the large changes in cardiac output caused by this drug. Arterial concentrations predicted by the kinetic model were linked to the systemic vascular resistance and venous compliance terms of the cardiovascular model. The kinetic-dynamic model based on a training data set (30 mmol over 2 min) was used to predict the results for a separate validation data set (30 mmol over 5 min).-1) described the distribution of magnesium into a deeper (probably intracellular) compartment. The final kinetic-dynamic model was able to predict the validation data set. The mean prediction error for the arterial magnesium concentrations, cardiac output and mean arterial blood pressure for the validation data set were 0.02, 3.0 and 6.1%, respectively.The kinetic-dynamic model was able to describe the training data set. A recirculatory kinetic model was a good description of the acute kinetics of magnesium in sheep. The volume of distribution of magnesium in the lungs was 0.89 L, and in the body was 4.02 L. A permeability term (0.59 L minThe combination of a recirculatory model and a simple two-compartment cardiovascular model was able to describe and predict the kinetics and cardiovascular effects of magnesium in sheep. The effective use of some drugs can be limited by their adverse effects on the cardiovascular system, particularly when they are used intravenously in relatively high doses. There have been many studies documenting the cardio-vascular effects of drugs. Similarly, many mathematical models of the cardiovascular system, of varying complexity, have been presented in the literature ,2. In pia priori the cardiovascular consequences of, for example, a change in dose regimen of a drug.In contrast, for traditional ("non-volatile") drugs, there have been very few instances in which kinetic models of a drug have been linked to cardiovascular pharmacodynamic models. The work of Francheteau et al. is an important exception , but evemax model). Rather, a number of cardiovascular variables may be altered simultaneously in a manner that is complex and interrelated. Thus, any dynamic model used must account for these intrinsic relationships between cardiovascular variables. Another problem is that changes in the cardiovascular system (in particular blood flow distribution) invariably alter the kinetics of the drug under study. Therefore, the kinetic model of the drug must be able to account for the effects of blood flow changes on the disposition of the drug. This requires the kinetic model to have a physiological basis, and importantly excludes the widely used mamillary compartmental pharmacokinetic model.There are a number of difficulties in implementing this approach for traditional drugs. One problem is that most drugs do not cause changes in one single cardiovascular variable (such as blood pressure) that can be described in the usual manner using a simple semi-empirical dynamic model  are important. This depends on the drug and the intended use of the model, but it is proposed that there is a minimum set of variables that is needed for a basic description of cardiovascular status.2. Devising a way of conveniently presenting the output of the dynamic model for a range of cardiovascular variables for comparison with data.3. Identifying a cardiovascular model of the appropriate complexity. Ideally the model must be of the minimum complexity that includes the cardiovascular variables identified above, and the major sites of action of the drugs.4. Identifying which parameters of the cardiovascular model can be estimated by curve-fitting, and which require prior estimates or measurements of physiological values. Most cardiovascular models are stiff numerical systems with many parameters, and only a small number can be estimated by curve-fitting the data in the traditional way.With respect to the pharmacokinetic model, there remains one crucial step:5. Constructing a kinetic model with a physiological basis that is sufficiently realistic to describe and predict the concentration of the drug in the key target organs controlling the cardiovascular system. On first principles, these could be expected to include:a. the myocardial concentrations when the drug has a direct myocardial effect ;b. the CNS concentrations when the drug affects the cardio-respiratory control centre of the brain;c. the arterial blood concentration when the drug affects baro-receptors or smooth muscle in the walls of the arterial vascular system;d. the venous blood concentration when the drug affects smooth muscle in the wall of the capacitance vessels of the venous vascular system.It is known that these concentrations can follow different time-courses, particularly after bolus administration or a change of infusion rate ,11. HoweThe data used to construct the model were collected in the same laboratory using a conscious chronically instrumented sheep preparation and have been published previously ,9. This Data set 1  , predominantly for curve-fitting. The R language, Version 1.9.0,  was usedi is a weighting term, p is the number of parameters and n is the number of data points:For the least squares curve-fitting, the best fit was determined as that with the highest Model Selection Criteria (MSC) and without non-identifiable parameters. The MSC is essentially an inverse Akaike Information Criterion scaled to compensate for data sets of different magnitudes , and is calculated as follows where wAll data points were weighted equally. A parameter was arbitrarily defined as non-identifiable if the standard deviation of the parameter returned by the fitting program was greater than the parameter estimate (i.e. the coefficient of variation was greater than 100%). A model with non-identifiable parameters means that the data do not contain sufficient information to estimate the parameter with precision.The symbols used throughout have been based on standards for the pharmacokinetic literature. Unfortunately, the use of C for both concentration (in pharmacokinetics) and compliance (in cardiovascular physiology) creates of conflict for this paper. To avoid confusion, CPL will be used for compliance here.-1), Stroke volume , Heart rate , Cardiac output , Systemic vascular resistance  and mean arterial blood pressure .The choice of the cardiovascular variables used in the model is clearly dependent on the drug under study and the intended purpose of the model. However, we propose that a minimum of 7 fundamental cardiovascular variables is sufficient for most pharmacological purposes. These variables are shown with their default unit of measurement in the model: Central venous pressure , Myocardial contractility .That the function of the left and right side of the heart is the same, and there are no abnormalities in the pulmonary vasculature so that the heart-lung system can be treated as one pump.Long time-scale events such as fluid shifts and renal mechanisms controlling blood pressure are ignored.Furthermore, this choice of variables is dictated by several fundamental relationships between the variables. Firstly, that myocardial contractility is a proportionality constant between CVP pressure and stroke volume (the volume of blood pumped with each beat of the heart).CVP * CNT ≈ SV    ...(2)Mathematically, CNT must therefore have the units of volume / pressure. However, contractility is difficult to measure in vivo, and that there are a number of surrogate measures including dp/dt. These can also be used with appropriate scaling factors.That there are no factors affecting the relationship between myocardial fibre length (the true determinant of stroke volume) and central venous pressure . CVP is therefore used as an easily measured index for myocardial fibre length – the assumption is that the two are related using a scaling factor. Left ventricular end diastolic pressure is also as an alternative index when data are presented as percent change from baseline.The second relationship is that between stroke volume and heart rate to give the cardiac output (the volume of blood pumped by the heart per unit time):SV * HR = CO    ...(3)The last relationship is that between cardiac output and systemic vascular resistance to approximate the mean arterial blood pressure.CO/SVR ≈ MAP    ...(4)This is because MAP usually greatly exceeds the CVP:CO/SVR = MAP-CVP    ...(5)-1 min.SVR therefore has the units of pressure over flow. In this paper, the resistance units (RU) are therefore mmHg LThe effect of a drug on one or two variables can usually be summarised on a plot of the variable (drug effect) against time. However, it is more difficult to summarise the dynamic effect of a drug on the cardiovascular system for the following reasons: First, the large number of variables required in the summary, where the seven described above could be considered a minimum. Second, the fixed inter-relationships (e.g. Eqns. 2–4) between these variables that should be revealed by the summary ). Third, usually an analysis requires comparing one cardiovascular state (e.g. pre-drug) with another (e.g. post-drug), or examining the time-course of drug effects.It is proposed that a "cardiovascular radar plot" with a modified logarithmic, normalised scale is an efficient means of limiting these problems. An example radar plot is shown and described in Fig. There are many published models of the cardiovascular system of various levels of complexity and intended for various tasks . HoweverIn vivo, the cardiovascular system has two major control systems; control of cardiac output via the Frank-Starling mechanism, and control of blood pressure via baroreceptor control of heart rate. These were added progressively to the model.A simple model of the Frank-Starling mechanism was developed Fig.  assumingPressure = Volume/Compliance    ...(6)The solution to the simple Frank-Starling model can be found algebraically, but for consistency is shown in a, CPLv) were chosen to duplicate the following behaviour  as the ratio of arterial compliance to venous compliance (Cratio).Central to the Frank-Starling model is the concept of cardiac function curves – usually given as the pressure in each pool as cardiac function (contractility) is increased from zero to a normal value. These curves are useful for finding appropriate initial estimates for blood volume, arterial and venous compliance, and systemic vascular resistance. To achieve the physiologically plausible cardiac function curve shown in Fig. our Fig.  which isThe control of arterial blood pressure via baroreceptor control of heart rate was added to the simple Frank-Starling model, as shown in Fig. v0 and Va0, see Fig. The final version of the model introduced a number of constraints to increase its physiological plausibility. These were: 1) Assuming that under baseline conditions that approximately 1/3 of the total blood pool is in the arterial system. 2) That the intercepts of the pressure-volume "curves" for the venous and arterial compartments were linear such that both curves gave the mean circulatory pressure (MCP) at the unstressed volumes (VHR = MAPset + MAPdelta*(HRgain*S1)    ...(7)The resultant cardiac function curves for this model are shown in Fig. For convenience, the target cardiovascular variables of the final constrained model discussed above are summarised in Table The sensitivity of the baseline cardio-vascular model to changes in parameter values was determined via Monte-Carlo simulation . Multi-vv, CPLratio, CNT, S1 and S2. Vblood could also be considered a primary variable, but it was considered unlikely that magnesium could change the blood volume. The remaining cardiovascular variables were considered secondary in that they would change in response to changes in the primary variables as given by Eqns 2 to 4.Changes in cardiovascular variables with the administration of magnesium were analysed as percentage change from baseline. This removed the contribution of inter-animal variability in baseline cardiovascular variables  to vaInitially, the only primary parameter fitted to the data for each time point was SVR while the other parameters were held constant. This was based on the prior knowledge that this was the primary mechanism of action of magnesium. If the MSC was low and the cardiovascular radar plot showed a poor fit between model predictions and the data, an additional parameter was fitted one at a time from the remaining parameters listed above. A parameter was removed from the fit if it produced an undefined estimate. The parameter was kept in the fit if it improved the MSC and the fidelity of observed vs. predicted plots on the cardiovascular radar. By this process, the values of the primary cardiovascular parameter at each key time point required to describe the observed data were determined.Conventional mamillary pharmacokinetic models are essentially empirical and do not include parameters (other than clearance) that represent defined physiological processes. This is problematic when drugs affect the cardiovascular system, or it is necessary to predict the kinetics of the drug when the underlying physiology has changed. This was the case for magnesium, which affected cardiac output significantly Figs.  &10. FulThe magnesium concentration data from Data set 1 were used to develop a recirculatory model of magnesium kinetics that could account for the observed cardiac output changes. The processed used was similar to that described by the authors for other drugs . The finKey points during the model development process were: 1) The representation of the lungs as a single compartment. 2) The representation of the cardiac output change as an empirical forcing function  and the concentrations of magnesium in arterial and coronary sinus blood were examined using hysteresis plots (effect vs. concentration). A concentration-effect relationship was considered plausible if produced a predictable relationship with minimal hysteresis that was consistent with the known mechanisms of action of the drug.By these criteria, it was found that the arterial concentrations were the better predictor of the fitted cardiovascular parameters shown in Table art < 2.66 thenif CSVR = 17elseart + 21.68    ...(9)SVR = -1.759*Cv). This was related to the arterial concentration using a simple threshold CPL-1 . This suggests that specifying the value for one of these parameters significantly constrains the value that can be taken for the other, as would be expected on physiological grounds. It can be concluded that each parameter had an important role to play in the model, and that each could only take a limited range of values to be consistent with the required baseline physiology. By extension, the assumptions regarding the values of these parameters are likely to be appropriate. Furthermore, the changes in these parameters observed following magnesium administration therefore reflect the effects of this drug rather than uncertainty in the parameter space of the model.The baseline cardiovascular variables and the parameters that produced them are summarised in Tables The method of estimating cardiovascular model parameters from cardiovascular data for individual time points was effective. Thus, it was possible to find a parameter set at each time point Table  that proThe recirculatory pharmacokinetic model was able to fit the observed concentrations with adequate fidelity Fig. , middle A feature of recirculatory pharmacokinetic models is that their initial kinetics are governed by first-pass passage of drug through the lungs, and the dilution of the injected drug with the cardiac output . The carRelating the estimated cardiovascular parameters in Table The recirculatory model of magnesium disposition was able to accurately predict the time-course of the arterial magnesium concentrations observed for the validation Data set 2, despite the large change in cardiac output produced by magnesium Fig. . The meaThe final pharmacodynamic model was able to accurately predict the time-course of the cardiac output changes observed for the validation Data set 2 Fig. . The meaThe dynamic model captured the general trend of the mean arterial blood pressure for the validation data Fig. , but somIn this paper, all cardiovascular effects were related to the arterial concentration of magnesium. As covered in the introduction, there may be other sites in the body that have a theoretical claim to being the most appropriate link concentration for certain cardiovascular dynamic effects. For example, the reductions in myocardial contractility caused by thiopental have been shown to have a better temporal relationship to the thiopental concentrations in the myocardium itself rather than in arterial blood . This coIn recirculatory models, it is possible to add a "target organ" to represent organs such as the heart . The facThere are a number of limitations of this modelling approach, many of which are inherent in the assumptions made in the construction of the model. Other limitations may become apparent if the model is used outside of the range of the data used to develop the model. For example, the CL term in the kinetic model was very low Table . This maThe cardiovascular model also assumes an instantaneous baroreceptor response. While it is relatively easy (in modelling terms) to add a delay to this response, this was not supported by the data. However, if the model is extended to situations with very rapid blood pressure changes (e.g. orthostatic hypotension) this deficiency may become significant.Constructing physiologically based models, even of the simplicity presented here, requires crossing many decisions points where a choice must be made from multiple options – sometimes the choices are data driven, sometimes theory driven, sometimes the subjective experience of the model maker must be called upon. While a "wrong" model is evident because it does no match the data, there is clearly no "right" model of the cardiovascular system. It is anticipated that more limitations of the cardiovascular dynamic model will become apparent when model is rigorously compared to data for other drugs, and for other cardiovascular scenarios. It is should be expected that the model will continue to evolve as these data are collected and analysed.The combination of the recirculatory kinetic model and the simple cardiovascular dynamic model was able to describe and predict the concentrations and cardiovascular effects of magnesium in sheep. It is proposed that the general methods used here could be applied to other drugs with cardiovascular effects. The authors are currently applying the method to intravenous anaesthetics.Cardiovascular term DescriptionFrank-Starling modelblood Blood volumeVv Central venous pressureCVP = Pa Mean arterial pressureMAP = PMCP Mean circulatory pressurea Volume of blood in arterial compartmentVv Volume of blood in venous compartmentVa0 Volume of blood in arterial compartment at MCPVv0 Volume of blood in venous compartment at MCPVa Arterial complianceCPLv Venous complianceCPLratio Ratio of venous over arterial complianceCPLSVR Systemic vascular resistanceCO Cardiac outputL Cardiac output (left side)COR Cardiac output (right side)COHR Heart rateSV Stroke volumekc unit conversion factor – contractilityCNT Contractilityadditional for Frank-Starling-Baroreceptor modelMAPset Mean arterial pressure set pointHRgain Gain for heart rate controladditional for Constrained-Frank-Starling-Baroreceptor modelaS Pressure in arterial compartment when stressedPvS Pressure in venous compartment when stressedPaS Volume in arterial compartment when stressedVvS Volume in venous compartment when stressedVslopeMAP slope for arterial pressure-volume relationshipintMAP intercept for arterial pressure-volume relationshipslopeCVP slope for venous pressure-volume relationshipintCVP intercept for venous pressure-volume relationshipS1 Sympathetic tone coefficient – ChronotropyS2 Sympathetic tone coefficient – Contractilitymax maximum for stroke volume-CVP relationshipSV50 half-volume for stroke volume-CVP relationshipSVnSV "Hill factor" for stroke volume-CVP relationshipPharmacokinetic term Description DescriptionRecirculatory model0 doserate of zero order infusionRtau duration of zero order infusionart Arterial magnesium concentration Cven Venous magnesium concentration Cart,e Arterial magnesium concentration (endogenous)Cven,e Venous magnesium concentration (endogenous)Cart,x Arterial magnesium concentration (exogenous)Cven,x Venous magnesium concentration (exogenous)Clung Apparent distribution volume of the lungVCL Clearancebody Apparent distribution volume of the body compartmentVPS Permeability-surface area product of deep compartmentdeep Apparent distribution volume of the deep compartmentVRNU participated in the original magnesium studies and performed the modelling. GLL participated in the original magnesium studies and acted as a resource for cardiovascular theory. Both authors contributed to the manuscript.The simple Frank-Starling model. The simple Frank-Starling model written in pseudo-code to generate cardiac function curves. The code is intended to run in the "Scientist" differential equation solving program.Click here for fileThe Frank-Starling-Baroreceptor model. The equations for the Frank-Starling-Baroreceptor model with baroreceptor control written in pseudo-code to generate cardiac function curves.Click here for fileThe Constrained Frank-Starling-Baroreceptor model. The equations used for the final cardiovascular model written in pseudo-code to generate cardiac function curves.Click here for fileThe final pharmacokinetic-pharmacodynamic model. The equations used for the final model written in pseudo-code.Click here for file


Functional but silent peroxisomal targeting signals have been found in non- peroxisomal proteins. This discovery has important implications for sequence-based signal prediction and for evolution. Can sequence segments coding for subcellular targeting or for posttranslational modifications occur in proteins that are not substrates in either of these processes? Although considerable effort has been invested in achieving low false-positive prediction rates, even accurate sequence-analysis tools for the recognition of these motifs generate a small but noticeable number of protein hits that lack the appropriate biological context but cannot be rationalized as false positives.MRP7) - are imported into peroxisomes in vivo if their original sorting signals are disguised. We also show that even prokaryotic proteins can contain peroxisomal targeting sequences.We show that the carboxyl termini of a set of definitely non-peroxisomal proteins with predicted peroxisomal targeting signals interact with the peroxisomal matrix protein receptor peroxin 5 (PEX5) in a yeast two-hybrid test. Moreover, we show that examples of these proteins - chicken lysozyme, human tyrosinase and the yeast mitochondrial ribosomal protein L2 (encoded by Thus, functional localization signals can evolve in unrelated protein sequences as a result of neutral mutations, and subcellular targeting is hierarchically organized, with signal accessibility playing a decisive role. The occurrence of silent functional motifs in unrelated proteins is important for the development of sequence-based function prediction tools and the interpretation of their results. Silent functional signals have the potential to acquire importance in future evolutionary scenarios and in pathological conditions. In addition to sequence homology and annotation transfer considerations [For an increasing number of otherwise uncharacterized protein sequences from genome-sequencing projects, function assignment is attempted solely with erations , these fAlthough considerable effort has been invested in achieving low false-positive prediction rates, our experience with tools for recognizing glycosyl phosphatidylinositol (GPI) lipid ,3 and myin vivo. Only experimental tests can resolve this contradiction. As a case study, we report the results of an experimental analysis that demonstrates the existence of naturally occurring peroxisomal targeting signals in several known non-peroxisomal proteins. We also discuss the evolutionary perspective of functional localization signals in unrelated proteins as well as the consequences for experimental localization determination and function prediction from sequence.Are these false-positive predictions? All three of the sequence-analysis tools mentioned above check query sequences for a recognition pattern that is explicitly described in terms of its physical properties and it is possible to check the concordance between pattern descriptions and query sequence individually. Nevertheless, this visual inspection is frequently unable to rationalize the findings as false-positive predictions, as all known components of the pattern appear to be present. Even in the case of high accuracy of the prediction tool, an erroneous prediction cannot be excluded. Alternatively, these predicted sequence motifs may occur by chance and be functional in an appropriate test system, but still have no biological meaning because the necessary cellular context is absent The major mechanism for targeting proteins to the matrix of peroxisomes, which are membrane-bounded organelles  of eukarDrosophila sevenless ; precursor of lysosomal bovine cathepsin D (P80209); and a mitochondrial ribosomal protein from yeast (P12687). We also examined the carboxyl terminus of a mouse dihydrofolate reductase construct with an added SKL peptide, which has been shown not to be imported into yeast peroxisomes [Screening of SWISS-PROT  entries oxisomes ,17.Depending on their taxonomic origin, the carboxyl termini of the eukaryotic sequences were assayed for interaction with the tetratricopeptide repeat domains of either human or yeast PEX5 using published methodologies . The quein vivo could be explained by the non-accessibility of their carboxyl termini. These could either be hidden in the native structure of the mature protein or of its functional complexes, or competing translocation machineries could lead to a removal of the respective proteins from the cytosol before their recognition by PEX5.The fact that the peroxisomal translocation machinery fails to import naturally occurring mature proteins carrying PTS1 signals into peroxisomes SKL, sequences appended to DHFR are in bold type, see results in Table in vivo the complete construct is not imported into peroxisomes, thus confirming the prediction [GGKSKL and IKYKFEVYEKKNIESKL) are predicted to be peroxisomally targeted. Their scores calculated with the PTS1 predictor [The first possibility is exemplified by DHFR-SKL. The carboxy-terminal 16 residues of the DHFR-SKL construct  was appended to the amino terminus of each of the selected proteins. It can be assumed that translocation into the endoplasmic reticulum (ER) or mitochondria is disrupted by the resulting shift of the signal peptide from the amino terminus to the center of the protein. The resulting molecules are expected to be redirected into peroxisomes if their carboxyl termini can act as PTS1 signals. Targeting of the GFP-constructs Escherichia coli glutamate-1-semialdehyde 2,1-aminomutase (P23893), E. coli transaldolase A (P78258), Methanopyrus kandleri riboflavin synthase (NCBI-Refseq accession NP_613646) and Archaeoglobus fulgidus 2-nitropropane dioxygenase (NCBI-Refseq accession NP_070998). Indeed, these proteins harbor carboxyl termini that qualify as PTS1 signals  could not be found in peroxisomes [In families of orthologous proteins, peroxisomal location and its targeting signal in the amino-acid sequence are not necessarily conserved. For example, in plants the five enzymes of the glyoxylate cycle are localized to peroxisomes, but in oxisomes . Thus, iIn dually localized proteins such as AGT , the PTSThe case of lysozyme is particularly noteworthy because a large number of homologous proteins were systematically hit when performing a SWISS-PROT screen using the prediction tool  of the carboxy-terminal exons for human tyrosinase (GenBank accession AP000720.4), fly sevenless (GenBank accession AE003484.2) and chicken lysozyme (GenBank accession AF410481.1) reach far into the functional domains of their proteins, rather supports an evolutionary mechanism of several point substitutions. The occurrence of functional PTS1 sequences in non-eukaryotic species further supports a stochastic model for the evolution of PEX5-interacting protein carboxyl termini.N-myristoylation of glycines behaves as an exchangeable functional module, as protein families do exist where it has been substituted by alternative sequence determinants that facilitate membrane association [Arabidopsis thaliana Rab5 ortholog Ara7 and its paralog Ara6. Ara7 is geranylgeranylated on carboxy-terminal cysteines just as Rab5 is in other species. However, the closely related paralog Ara6 lacks the carboxy-terminal cysteines and has an experimentally verified amino-terminal myristoylation motif [In non-globular regions of proteins, sequences that code for targeting to other subcellular compartments, or for posttranslational modifications, might appear in similar ways during evolution. For example, the sequence motif coding for amino-terminal ociation . This ison motif .Many of these signals seem to remain silent under normal physiological conditions  but have the potential to become important in some future evolutionary scenarios or in pathological situations. Alternatively, the PTS1 signal might have become obsolete and the corresponding sequence segment is now subject to evolutionary alterations. Apparently, the cell exploits only a fraction of the potential molecular capabilities of its proteins.Futhermore, subcellular targeting is organized in a hierarchy of cellular recognition mechanisms. The co-translational sorting into the ER serves as a first decision node. Posttranslational processes such as interaction with chaperones, folding, and covalent modifications are concomitant with the appropriate exposure of targeting signals. The amino-terminal signals are made first and are therefore favored when it comes to recognition by receptors. PEX5 needs only to categorize the remaining unsorted proteins with accessible carboxyl termini into 'stay here' or 'let's go into peroxisomes'. This might also explain why the PTS1 signal is comparatively short and permissive for a wide range of residues.Clearly, the fact that functional sequences for subcellular targeting occur in unrelated proteins needs to be considered for prediction-tool development. The construction of a negative learning set  on the basis of proteins with differing cellular localization is problematic. For example, a set of non-peroxisomal but organellar localized , viral  or bacteN-myristoyltransferases (NMT) in bacteria. Are these false-positive predictions? A literature search revealed that myristoylation by host NMTs has physiological relevance for several secreted proteins of intracellular bacterial parasites [N-myristoylation is typical for eukaryotes but occurs also in bacteria. In many cases, it remains without phenotypic effect for bacteria but may become evolutionarily important in the case of host-parasite interactions.Surprisingly, when Maurer-Stroh and Eisenhaber applied their myristoylation site predictor for eukaryotic proteins to bacterial proteomes , systemaarasites . Thus, tIn the case of the endothelin-converting enzyme 1 and the neprilysin-like zinc metallopeptidase family, the carboxy-terminal CXAW motif is a valid prenylation motif. This carboxy-terminus is functionally hidden because the protein is exported to the extracellular side of the cytomembrane and the carboxy-terminal residues are apparently involved in folding and enzyme function .Clearly, the accessibility of the recognition motif in the substrate protein to the respective receptor or protein-modifying enzyme is a major issue. For PTS1 signal prediction from the amino-acid sequence, carboxy-terminal exposure needs to be assessed both from the steric point of view as well as in the context of competing translocation mechanisms. Analyzing only the carboxy-terminal dodecamer peptide ,13 mightIn databases, it should also be routine to flag proteins that contain several competing targeting signals with differing priority. Finally, silent localization signals might become active in mutant protein constructs and lead to non-native localizations, an issue that needs to be assessed especially in localization screens of proteins with uniformly incorporated fluorescent dyes such as GFP. It cannot be excluded that the subcellular location of a considerable number of proteins has not been correctly determined in published large-scale studies that rely on this methodology ,31.To conclude, sequence segments coding for subcellular targeting or for posttranslational modifications can occur in proteins that are not substrates in either of these processes. Accurate prediction techniques reveal candidate proteins carrying hidden sequence signals. Many of these can be experimentally confirmed. In the case of the PTS1 predictor program, there is no reasonable argument to assume a difference in prediction accuracies for real and hidden PTS1s as, in both cases, productive interaction of the carboxyl terminus with PEX5 is the criterion for a functional PTS1.E. coli strain DH5α, Bethesda Research Laboratories) was used for all transformations and plasmid isolations. For the yeast two-hybrid-assay, the hybridized oligonucleotide pairs coded for the carboxy-terminal 16-mers of the selected proteins flanked by BamHI (5') and EcoRI (3') restriction sites. Each oligonucleotide pair was introduced into a BamHI-EcoRI-digested pGAD.GH fragment, generating plasmids containing the Gal4p activation domain in addition to the desired carboxy-terminal 16-mer extension . All pGAD.GH constructs were sequenced . The plasmids pAH987 and hP87 contain the binding domain of Gal4p fused to the TPR domain of S. cerevisiae or Homo sapiens PEX5, respectively  [Oligonucleotides were purchased from MWG Biotech . The pBD-TPR) .EcoRI/BamHI and ligated into pEGFP-C1 (Clontech). The DsRed2-SKL construct was obtained by PCR using Pfu-polymerase (Promega) and the plasmid pDsRed2-C1 (Clontech) as template  according to the manufacturer's instructions. Reverse transcription was performed using RNA-PCR Core Kit (Applied Biosystems) following the manufacturer's instructions. For the amplification of tyrosinase, we used cDNA from the melanoma cell line 29 WUBI . The coding regions of lysozyme and tyrosinase were gained by PCR  after the MLS1 promoter in the vector YEplac181. DNA fragments coding for MRP7 and GSA were obtained by PCR (see Table BamHI-SphI) in-frame with GFP to give rise to the expression of GFP-MRP7 and GFP-GSA, respectively, all of them under the control of the MLS1 promoter.Standard procedures were used for cloning of the GFP-MRP7 and GFP-GSA constructs including control sequencing (VBC Genomics). The plasmids expressing GFP and GFP-SKL under control of the eviously . The DNAMATα, his3Δ200, ade2-101, trp1Δ63, leu2, gal4Δ, gal80Δ, lys2::GAL1-HIS3, ura3::GAL1-lacZ) [A600 = 0.3 into the same medium and finally harvested at absorptions of A600 between 0.9 and 1.1.According to the Matchmaker two-hybrid protocol, yeast strain PCY3 (L1-lacZ)  was tranL1-lacZ) , yeast cCOS7 cells were transfected with the pEGFP-C1-constructs and DsRed2-SKL by electroporation using 920 μF and 220 mV , grown on coverslips for 36 h, washed, fixed with 0.5% formaldehyde in PBS for 15 min and covered with geltol. Cells were analyzed using the Olympus BX51 fluorescence microscope (60 × enlargement).S. cerevisiae CB80 . Yeast transformants were selected and grown on minimum medium containing 0.67% yeast nitrogen bases without amino acids (Difco Laboratories), 2% glucose and amino acids (20-150 μg/ml) as required (SC-leu-ura). For fluorescence microscopy, yeast cells were grown at 30°C with shaking in selective media with 0.5% glucose as sole carbon source until the glucose concentration was very low , harvested by centrifugation and resuspended in the original volume of induction medium containing 0.67% yeast nitrogen bases without amino acids, 0.1% yeast extract, 30 mM potassium phosphate pH 6.0, 0.125% oleate, 0.2% Tween-80 and amino acids as required. Cells were grown for 16 h in induction medium and observed live for fluorescence. Briefly, cells were collected by centrifugation and washed twice in water. Cell pellets were resuspended in induction medium without oleate and aliquots were spotted onto multitest slides  previously coated with concanavalin A . Cells were allowed to attach for 5 min at room temperature and the slides were washed twice with induction medium and a coverslip applied for observation. Fluorescence was viewed with a Zeiss Axioplan 2 fluorescence microscope using a 63 × (1.4 NA) lens. Digital images were captured with a Quantix CCD camera using Lightview software without further modification. The pictures were mounted and false-color overlays were made in Adobe Photoshop.The yeast strain used in this study is


The enormous amount of genome sequence data asks for user-oriented databases to manage sequences and annotations. Queries must include search tools permitting function identification through exploration of related objects.The GenoList package for collecting and mining microbial genome databases has been rewritten using MySQL as the database management system. Functions that were not available in MySQL, such as nested subquery, have been implemented., a set of 17 specialized end-user-oriented microbial databases  has been made publicly available. These databases allow the user to browse genome sequence and annotation data using standard queries. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation updates on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences, taking into account a clustering of genes into formal operons, as well as providing extra facilities to query sequences using predefined sequence patterns.Inductive reasoning in the study of genomes starts from "islands of knowledge", centered around genes with some known background. With this concept of "neighborhood" in mind, a modified version of the GenoList structure has been used for organizing sequence data from prokaryotic genomes of particular interest in China. GenoChore Thermoanaerobacter tencongensis, LeptoList, with two different genomes of Leptospira interrogans and SepiList, Staphylococcus epidermidis) associated to related organisms for comparison.This growing set of specialized microbial databases organize data created by the first Chinese bacterial genome programs (ThermaList,  While this shows that we implicitely possess an enormous wealth of information about the functions carried out by genes and genomes, the very fact that this amount is enormous makes it extremely difficult to mine that information easily. The role of specialized databases is to make this task easier for end-users. Many types of microbial genome databases exist. Most of them have been developed in a context of bioinformatics centres or laboratories purely favoring in silico research rather than the coupling between experiments using computers and experiments at the bench, and this is reflected in the structure and aims of the databases [Escherichia coli genome data, well before the whole genome sequence was known [Bacillus subtilis genome program data access [, [We are facing a deluge of genome sequences. As of January 14th, 2005, the GOLD site identified 1248 completed or ongoing genome programs as known . Later oa access . Many daccess [, ). Howeveccess [,  and we dBecause we wished to shift from a commercial DBMS to an open-source one, there were some applications that could not be implemented readily due to the lack of certain advantages possessed by the commercial DBMSs. Hence, we had to alter the data structure in order to cope with this situation. The core data schema used in this work was that of GenoList version 3.1 , with sl. Most function transfers were straightforward. However some functions such as nested subquery that were not available in MySQL had to be dealt with indirectly. The nested subquery has been entirely circumvented in the PERL code and is dealt with in the Extended Search algorithm by concatenating different SQL queries simply using the "AND" or "OR"connector.In the present GenoList databases, the DBMS used is Sybase™. While this is convenient because of excellent stability and maintenance, this may pose problems in terms of commercial policies, especially if the structure has to be exported. We therefore decided to rewrite the management of the GenoList structure using MySQL . The current version is Version 6.2 Oct 15, 2004. While this specification is rigid, there is still a significant degree of freedom in annotation, so that a large number of individual situations have to be taken care of semi-automatically. The basic idea of the parser is firstly to read through the input file at the INSD and check file formats. Subsequently, the information is collected and distributed into several temporary files using a set of predefined keywords and their qualifiers (i.e. those characterizing the data schema). Subsequently, a check process is initiated to identify all situations that do not fit the specifications, so that they can be corrected manually. Usually, most of the process of creating tables is automatic and only a few exceptions have to be corrected individually. A second type of input is also provided as an interactive interface to tell the database curator what information has to be collected: once collected this information can be loaded into the databases directly  withth Dimension™). The modification of the database schema needed for using MySQL required additional adaptations of the application interfaces. Using the original package would have required iterative work that was systematically adding complexity into the system. Current best Web interfaces and application interfaces (i.e. friendly for sharing parties) are often based on Perl scripts. For this reason a new core management script was recreated, written in Perl, while keeping the package architecture and the Web interfaces. Among other languages that have comparable functionalities, the choice of Perl to create the system was motivated by its powerful capability to glue different programs or scripts together. In addition it is widely used by the INSD, and at the European Bioinformatics Institute in particular within the BioSapiens program . Furthermore, this choice allowed us to keep the optimized fast C code that has been constructed for searching pattern (strings of symbols) inside the DNA or amino acids sequences. The GenoList C/C++ package chose to use the GD library  for generating graphic representations of genome regions. The GD graphics library is an open source library which allows programmers to easily generate PNG, JPEG, and WBMP images from many different programming languages. We used here a newer version of the same library (perl module perl-GD version 2.11) to make use of its improvements in creating dynamic pictures. In rewriting the core of the program we used the Perl module DBI . A DBI is a middle layer between the outside applications and the communicator (DBD). Different DBMSs have their own communication mechanism to talk with outside applications, and in the present version the choice of the DBI module has been implemented in such a way that we could change the DBMS if necessary with minimal work. In this way, when changing the DBMS, it will only be necessary to tell the DBI about the specifications of the new DBMS without having to modify any other code. Finally, we used the Perl module CGI to facilitate the production of the WebPage interfaces. As a consequence further developments of the GenoChore package should be performed with minimal effort.We kept the interface of GenoList as published, except that a box providing access to protected curation of annotations is now provided, aiming initially at helping the first party (sequencing teams) annotators. The front page is made of three frames. Briefly, the vertical frame on the left contains the controls necessary to get access to the content of the database. The upper part of this frame contains text fields for querying the database according to five types of queries: gene name(s), chromosome region around a gene, chromosome region defined by positions, free text, functional classification (more detailed information about each type of query can be obtained by clicking on the question mark near the query title). The "Extended Search" button gives access to a search form allowing the user to perform multicriteria searches on all the database fields. The lower part of this frame allows one to launch sequence analysis tools: BLAST and FASTA database searches (on the sequence data), and DNA or protein pattern searches. In the former case, the user can choose to explore sequences located upstream of putative operons. In the latter case, the user can search for patterns anywhere in proteins, but also restrict the search to the beginning or end of the protein. The upper frame on the right can contain various types of information, depending on the genome and on the query. It can contain a graphical representation of a chromosome region, that can be obtained in several ways: usually from a gene in the bottom frame. This frame may contain launch forms and result lists from the sequence analysis tools available . The bottom frame on the right always contains detailed information about one given gene, including regularly updated BLAST searches and Teraprot reports as well as related bibliographic references. The original package managing the interface of GenoList databases was written in C/C++, following the first database schema , that haIn the original GenoList structure, the central table corresponding to genomic objects carried all relevant features that are associated to genes and gene products. For the sake of future developments and to accommodate new feature annotation present in genome flatfiles, we separated this table into several gene product tables, specific for RNAs and proteins. The current data structure remains open to include tables for other types of data, such as regulation properties annotations when they will become available. Figure Streptomyces coelicolor database, CoeliList).It is important here to notice that, in contrast to a rather ubiquitous practice, we explicitely separate between Open Reading Frames (ORFs) that are simply sequences multiple of 3 between two termination codons  and CDSs, that begin with a specific codon, usually ATG (in the DNA text), preceded by a ribosome binding site (RBS), typically AAGGAGGT in many bacterial genomes. One must remember that in most genomes the beginning of CDSs has not been experimentally identified. Identification of CDS starts is however much easier in low G+C Firmicutes that do not possess a counterpart of ribosomal protein S1 found in gamma proteobacteria . In the PA3004 for a gene found in the genome of Pseudomonas aeruginosa). In the absence of knowledge of a gene name it takes some time to identify it (often using BlastP), for example when aiming at the study of its neighborhood . Orthologues were identified as reciprocal best hits [B. subtilis for Firmicutes and another organism of the same family, such as Listeria monocytogenes, and E. coli for gamma-proteobacteria, with another one of the same family, such as Photorhabdus luminescens). Finding putative orthologues in the three organisms was considered as substantiating evidence for the use of a gene name. Then, in each triplet, we did not transfer the model organism gene name to all orthologues that were not simultaneously present in the three genomes or that gave different correspondences in different comparisons. In another comparison where the orthologues were found with at least 50% similarity, the model organism gene names to be transferred were preceded by the letter 'y'. In order to help users recognize gene names  we used as reference names those in the model bacteria, trying to comply as much as possible with the names used at SwissProt in the HAMAP project [Users know that the system used for naming genes in genome databases is extremely unwieldy and completely lacks standardization. This is usually because genes are simply labelled in databases by access numbers corresponding to the annotation phase of the relevant genome program (e.g.  article ). Natura project .rpsA can be accessed directly or using its synonym PA3162: it is then found downstream of cmk (a context similar to that found in many Gram negative bacteria) and upstream of himD. We are aware that some erroneous identification (or propagation of erroneous identifications) must have occurred in some cases, but we think that this is a trade-off (which will be continuously corrected) for a much more user-friendly usage of the databases. A 'y' letter starting a gene name indicates that it has not been experimentally identified, nor convincingly identified after in silico analysis yet. We provide curation pages (see below) to help users to correct annotation errors and improve annotation in a continuous way.This allows the users to have "anchor" points to start to use the databases in a more efficient way. Naturally, the names previously used in the corresponding genome programs are kept as synonyms, so that access to the sequences with these names is still allowed. For example, in AeruList, gene Escherichia coli created by Monica Riley and her collaborators [An important feature for allowing users to explore biological functions is to investigate the genes neighborhoods . Relatedborators  (Additio12–25C). This facility has already, in a quite unobtrusive but efficient way, permitted discovery of many unexpected functions. We have also provided means to explore the beginning and the end of protein sequences, as well as DNA regions upstream of putative operons, computed as strings of genes transcribed in the same orientation and separated by a maximum number of nucleotides (60 nt by default).In addition to using keyword queries or sequence tags (such as molecular mass or isoelectric point of a protein) the database provides a versatile way to identify sequences from the biological knowledge viewpoint. In particular, as in many other databases, it allows the user to use Fasta, BlastP and BlastN to compare a sequence of interest to that of those present in the database. Furthermore, in contrast to most cases, it allows the user to extract information using motifs, that can be either continuous or discontinuous  are provided, allowing the user to look for remote kinships.Genome annotation is continuously updated by scientists all over the world, at a time when new genome sequences appear every three days or so. In order to cope with this enormous flux of information, a facility for browsing automatically new entries in major data libraries has been implemented. In the gene information panel, where each gene of interest is described after being identified as the result of a query (including resulting from a Blast or Pattern search), an "Automatic Blast" link provides a list of updated blast searches against the UniProt library (SWISSPROT+TREMBL). In addition, when the genome belongs to the 'Teraprot' Smith and Waterman Z-score family To discuss the use of the databases we shall restrict our exploration to two databases from the package. LeptoList, that comprises two genomes (each one having two chromosomes) for Bacteria, and CunicuList, that describes the genome sequence and annotation of a small eukaryote.Leptospira interrogans serovar Lai, the paradigm of leptospirosis causative agents [L. interrogans serovar Copenhageni in order to allow easy comparison [L. interrogans has two chromosomes (this feature was not yet displayed in GenoList databases). Using the regular comparison to the CDS to the non-redundant INSD protein database allowed us to suspect that a significant proportion of the short putative CDSs in the genome are likely ORFs and not authentic CDSs. This fits with the recent sequencing of the second Leptospira genome [LeptoList is the reference database dedicated to the genome of e agents . It is pmparison . The WWWa genome ,21.B. subtilis (frr gene product) and compared it using BLAST with the functionality implemented in LeptoList. This search led to a single gene, LA3295, located downstream of gene pyrH . This synteny is obviously highly significant. In the same way, the gene immediately upstream from pyrH (LA3297), as in other bacteria, is likely to be coding for elongation factor EFTs (tsf). When curating the database, we suggest to the curator that it would be of excellent policy to replace the gene numbers by the corresponding gene name. In another type of investigation, looking for patterns of the type TTGACA (1 ambiguity) – 17 nt – TATAAT# (1 ambiguity) (consensus sequence of the σ70-type promoter) in the 300 nt region upstream of genes revealed 70 sequences in chromosome I, many of which are likely to be promoters (at least they would be good guesses to start investigating promoters in L. interrogans). In the same way, the putative DNA binding site located in the 300 nt nucleotide region upstream of genes, TGTGA (1 ambiguity) – 2 nt – KK – 2 nt – TCACA (1 ambiguity) , yielded 130 matches in chromosome I of serovar Lai and 72 matches in serovar Copenhageni and 2 in chromosome II of serovar Lai and 0 in serovar Copenhageni, allowing one to start investigating possible regulatory elements.A couple of examples of its use are given here. We looked for counterparts of RRF, the ribosome release factor. In order to find the gene we used a known sequence, from fadH or prfC), accounting for the higher total number of putative binding sites in that organism. It seems most interesting that genes involved in the control of respiration (cytochrome c biosynthesis), control of the TCA cycle (pyruvate dehydrogenase synthesis), control of the coupling between translation and transcription (stringent control) or translation itself (release factor 3 synthesis) are present in the list. While there are several putative adenylyl cyclase genes present in the organism, as well as several homologs of crp, it is plausible to propose that cAMP plays an important role in the life cycle of L. interrogans, perhaps suggesting ways to allow multiplication on plates of this elusive organism.This result is interesting as it suggests that chromosome I genes are submitted to a regulation recognizing that particular DNA-protein binding site. Furthermore, most genes found with the site in serovar Copenhageni are also found in serovar Lai, with sometimes several repeats in the latter, occuring upstream of some genes . Using MesJ (TilS) [yacF B. subtilis gene. Looking for counterparts of genes in the methionine salvage pathway [The GenoList structure has been initially constructed for organizing sequence data from prokaryotic genomes. However it may be extended to other organisms as well (the "genomic object" type must be extended accordingly). We have therefore tested the implementation of the structure for the genome of J (TilS)  as well J (TilS)  we found pathway , we fail pathway  are appa. Table Several other similar bacterial databases are accessible at URL Despite of – or because of – the large number of genome programs, once a sequence has been deposited at the INSD, its annotation is seldom updated. This is because the cost of curating annotations is extremely high, and usually not considered, despite its enormous importance. One of our aims was therefore to allow curation by selected teams by creating a curator page where such teams would input their annotations, that would then be propagated to the databases. The basic schema of the curator interface is shown in Figure A set of 17 specialized end-user-oriented microbial databases (including one instance of Microsporidia) has been implemented in Hong Kong. They allow one to browse genome sequence and annotation data using the most frequent queries that end-users would like to ask. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences present in the databases. All comments, bug reports and suggestions for improvement are more than welcome: this work is meant to be useful for the community of microbiologists interested in genomics.The author(s) declare that they have no competing interests.Bacillus subtilis and Escherichia coli), created the link to Teraprot for identification of gene functions, and implemented parts of the PERL package; YQ implemented parts of the Perl package; CC and VC implemented most of the databases into the core structure; ZY wrote part of the new parsers, implemented the two chromosomes of LeptoList by changing the data structure in the database and set up with CC the first LeptoList database; FC set up and administered the Apache web server and MySQL database; IM over the years designed most of the GenoList data schema and user interface; AD was at the origin of the project, participated in the design and evolution of the data schema, was the systematic tester and end-user and wrote the core of the article.GF wrote the parsers used to create the preliminary C/C++ MySQL package, and created important sections of the Perl package; CH created the procedure for renaming orthologs with reference to accepted names for model bacteria (Functional categories. The genes' roles are listed into six major categories. The three first ones are directly linked to biological roles, while the remaining categories are created ad hoc: adaptation to atypical conditions correspond to miscellaneous roles, while the two last categories correspond to roles that have not yet been ascribed to genes because of lack of in vivo or in silico dataClick here for file


A small rodent rustles through a field in the still night, making just enough noise to betray its location to a circling barn owl. A female frog sits on the bank of a pond amid a cacophony of courting bullfrogs, immune to the mating calls of all but her own species. Thanks to a sophisticated sensory processing system, animals can cut through a vast array of ambient auditory stimuli to extract meaningful information that allows them to tell where a sound came from, for example, or whether they should respond to a particular mating call.An acoustic stimulus arrives at the ear as sound energy in the form of air pressure fluctuations. The sound signal triggers oscillations in mechanical resonators such as the eardrum and hair sensilla. These oscillations convert sound energy into mechanical energy, opening ion channels in auditory receptor cells and producing electrical currents that change the neuron's membrane potential. This, in turn, produces the action potential that carries the sound signal to the brain. This multistep signal transduction process takes less than a millisecond, but exactly how it occurs at this time scale remains obscure. Direct measurements of the individual steps can't be made without destroying the mechanical structure; consequently, most measurements are taken downstream of the mechanical oscillations at locations like the auditory nerve. Likewise, the temporal resolution of most stimulus–response trials is far too imprecise to analyze processing at the sub-millisecond level.Given these experimental limitations, Tim Gollisch and Andreas Herz turned to computational methods and showed that it's possible to reveal the individual steps of complex signal processing by analyzing the output activity alone. Using grasshopper auditory receptors as models, the authors identified the individual signal-processing steps from eardrum vibrations to electrical potential within a sub-millisecond time frame and propose a model for auditory signaling.fixed output (response). To get the parameters to describe the final output, the authors generated a sound stimulus (two short clicks) and recorded axon responses of receptor neurons in a grasshopper auditory nerve. From these recordings, they defined the fixed output as the probability of a receptor neuron firing a single action potential. They then asked how the various parameters, which were associated with different time scales, could produce the same predefined firing probability.The crucial step in their study is the search for those sets of inputs (stimuli) that would yield a given By varying the stimulus parameters and comparing the obtained values within their mathematical framework—and making certain assumptions, for example, that the steps signal through a “feedforward” process—they could then tease out the individual processing steps that contribute to the desired output within the required time frame. With this approach, Gollisch and Herz disentangled individual steps of two consecutive integration processes—which they conclude are the mechanical resonance of the eardrum and the electrical integration of the receptor neuron—down to the microsecond level. Surprisingly, this fine temporal resolution is achieved even though the neuron's action potentials jitter by about one millisecond.Thus, using just the final output, this approach can extract the temporal details of the individual processes that contribute to the chain of auditory transduction events. While this method is best-suited for deconstructing unidirectional pathways, the authors suggest it could also help separate “feedforward” from feedback signaling components, especially when feedback is triggered by the final steps. But since many sensory systems share the same basic signal-processing steps, this method is likely applicable to a broad range of problems.


Researchers who use MEDLINE for text mining, information extraction, or natural language processing may benefit from having a copy of MEDLINE that they can manage locally. The National Library of Medicine (NLM) distributes MEDLINE in eXtensible Markup Language (XML)-formatted text files, but it is difficult to query MEDLINE in that format. We have developed software tools to parse the MEDLINE data files and load their contents into a relational database. Although the task is conceptually straightforward, the size and scope of MEDLINE make the task nontrivial. Given the increasing importance of text analysis in biology and medicine, we believe a local installation of MEDLINE will provide helpful computing infrastructure for researchers.We developed three software packages that parse and load MEDLINE, and ran each package to install separate instances of the MEDLINE database. For each installation, we collected data on loading time and disk-space utilization to provide examples of the process in different settings. Settings differed in terms of commercial database-management system (IBM DB2 or Oracle 9i), processor (Intel or Sun), programming language of installation software (Java or Perl), and methods employed in different versions of the software. The loading times for the three installations were 76 hours, 196 hours, and 132 hours, and disk-space utilization was 46.3 GB, 37.7 GB, and 31.6 GB, respectively. Loading times varied due to a variety of differences among the systems. Loading time also depended on whether data were written to intermediate files or not, and on whether input files were processed in sequence or in parallel. Disk-space utilization depended on the number of MEDLINE files processed, amount of indexing, and whether abstracts were stored as character large objects or truncated..Relational database (RDBMS) technology supports indexing and querying of very large datasets, and can accommodate a locally stored version of MEDLINE. RDBMS systems support a wide range of queries and facilitate certain tasks that are not directly supported by the application programming interface to PubMed. Because there is variation in hardware, software, and network infrastructures across sites, we cannot predict the exact time required for a user to load MEDLINE, but our results suggest that performance of the software is reasonable. Our database schemas and conversion software are publicly available at  MEDLINE is a large biomedical bibliographic database that is well known to users around the globe. It contains over 12 million citations from over 4,600 journals. MEDLINE is a rich source of biomedical text that lends itself well to research on text mining, information extraction, and natural language processing in biomedical domains. The usual way in which users query MEDLINE is through PubMed, the web-based interface and search engine provided by the National Library of Medicine (NLM) . By contThe entire content of MEDLINE is available as a set of text files formatted in XML (eXtensible Markup Language) . The NLMAlternatives to relational databases are XML-based databases, which have recently emerged as another option for storing information transmitted in XML format.XML databases may exist as standalone databases or as add-ons to relational systems. The MEDLINE data set would be an excellent test of the capabilities of these databases because of its size and complexity. We focused on relational databases because they are currently more ubiquitous and standardized, and interested users are more likely to be comfortable with relational database technology.In the remainder of this report, we describe the software tools we developed for converting MEDLINE in XML files to MEDLINE in a relational database, and provide a few sample queries that demonstrate the flexibility of the resulting system.The NLM provides a DTD (Document Type Definition) that defines the structure of data in the MEDLINE XML files -7. From There are multiple ways in which one can design a schema from the same DTD, because DTD elements and attributes can be mapped to tables and fields in different ways. Certain design decisions may favor speed at loading time, and others may favor speed and ease of use at query time. Loading records associated with 12 million citations into a database is very time consuming, and the time can be minimized if lookups to the database are minimized during loading. In general, we aimed to minimize lookups even if that meant repeating information in the database. As developers often do in the design of data warehouses, we chose to de-normalize the schema in order to improve read-only query performance, which is the typical data access pattern in our workload.The typical table contains a PubMed identifier (PMID) in one column, and data related to that PMID in the remaining columns. Figures Our development team included a group of researchers from the University of California at Berkeley and another group from Stanford University. We shared similar goals in that we all wanted to load MEDLINE into a relational database, but because we were in two different departments at two different institutions, we had different project constraints and timelines. Thus, our groups were loosely associated in the software development process, but not closely integrated, and therefore, the original schema that we shared diverged.The result was three MEDLINE schemas and three software variants: One schema was used with Java code developed at Berkeley, another schema was used with Berkeley's code modified to run at Stanford, and the third schema was used with Perl code developed at Stanford. Here we describe the underlying design that influenced all three of the schemas. (The schema used for the Java program did not include information from DTD elements DataBankList and AccessionNumberList. This has been corrected in the most recent version of the software available on the Berkeley website.)medline_citation. The medline_citation table contains the PMID as the primary key and has additional columns that correspond to single-valued elements in the DTD, where the values of those elements depend on the PMID. The medline_abstract table is similar in that it has a PMID as the primary key and columns of data that depend on the PMID. Since document abstracts are larger than the other data types, we placed them in a separate table. However, since abstracts are stored as CLOBs (Character Large Objects), they are not stored in the same pages as the rest of the fields in the medline_abstract table. Therefore, in a more recent implementation, we removed the medline_abstract table from the schema, and added the abstract_text field as a CLOB in the medline_citation table. This change reduces the number of tables by one, and eliminates the need for a join between the medline_citation and medline_abstract tables.The main table in the schema is medline_keyword_list, which stores multiple values of keyword for a given PMID, and medline_gene_symbol_list, which stores multiple values of gene_symbol for a given PMID.Some tables in the schema have more than one row corresponding to the same PMID. Columns in these tables map to multi-valued elements in the DTD. Examples are the table Article in the DTD has a one-to-one relationship between an article and a PubMed identifier. Rather than giving Article its own table, we put single-valued data from Article into the table medline_citation.The element xml_file_name to the medline_citation table. This field does not correspond to any element in the DTD structure, but allows the database administrator to go back to the original XML file if necessary to find the original source of the data.To keep track of the name of the file from which data are read for a given citation, we added the field We could have stored each author only once in a table of its own, and assigned each author a unique integer primary key to serve as an author identifier. An author is represented by a combination of values in fields for last name, forename, first name, middle name, initials, suffix, affiliation, and collective name. Another table would have stored the set of author identifiers associated with each PMID, and because integer joins are fast, this design would have facilitated rapid search for all PMIDs associated with a given author, by joining the author table with the table of author identifiers and citations. However, there are several drawbacks to this approach. Generating integer primary keys during loading requires that either a lookup be done to see if each author of each citation already exists or not (35 million lookups), or all authors and primary keys must be kept in memory. The former approach is very time consuming during loading; the latter approach strains memory resources. In addition, regardless of how primary keys are managed during loading, it is not possible to determine algorithmically if two different representations of one author are actually the same author, or if one representation is actually two different authors. We therefore avoided generating unique primary keys and repeated all eight fields representing the author for every citation occurrence of that author.medline_journal is a parent of thirteen other tables . One of the other tables, medline_mesh_heading, is a parent of medline_mesh_heading_qualifier. Multiple qualifiers can be associated with each MeSH heading for a given citation.Figure We implemented three versions of software that parses and loads MEDLINE. The first was Java MedlineParser, which was developed at Berkeley [see additional file 1]. The second was the same Java code, modified to run at Stanford. The third was Perl ParseMedline, which was developed at Stanford.All versions of the software perform two basic tasks: (1) they parse the XML files to collect data, and (2) they load the data into the database. Figure There are two types of application programming interfaces (APIs) for parsing XML files – the tree-based DOM (Document Object Model) and the event-based SAX (Simple API to XML) . We chosThe Java version uses the Java SAX parser to parse the XML files, and JDBC (Java Database Connectivity) to communicate with the database. The Perl version uses the Perl SAX parser, and Perl DBI (Database Interface) to communicate with the database. We provide additional detail about the Java implementation here.The SAX parser requires the developer to write code that specifies the data model for objects in the domain. The data model is an object model that represents tables in the schema. The SAX parser also requires code that listens for SAX events and that maps elements – or nodes in the XML tree – to the object model. We created two main classes upon which our code is based: GenericXMLParser and NodeHandler. GenericXMLParser is responsible for generating events when nodes that correspond to objects in the object model are encountered in the document, and NodeHandler provides the event listener. Together, these two classes form a generic approach to reading in XML data and writing out those data to tables; they are independent of the DTD or table structure.As the parser processes the document, it decides how to handle the semantics of data at each node and determines whether to store parsed data at that node or to delegate the event to a child handler. For each node that corresponds to a table, there is a handler class that extends NodeHandler. A handler defines metadata for the node, and encodes any non-standard behavior at that node. An example of metadata is shown in Table Since NodeHandler and GenericXMLParser are generic, they can be used to write similar parsers for other XML documents. We have, for example, used these classes to write a parser for MeSH  XML files, which are also distributed by the NLM. The MeSH files are small compared to MEDLINE. MeSH 2003 comes in three XML files that total less than 600 MB.An optional feature is validation. XML files provided by the NLM are validly formatted, but we provide additional checks to ensure that all element tags in the XML data file have been handled by the parser and that all data have been inserted into the database. A developer who is extending the software to cover new tables can use this feature to ensure that metadata definitions are correct in classes that extend NodeHandler.In the course of our work, we applied the software tools we were developing to three different relational database products. Our Berkeley team initially experimented with PostgreSQL, since PostgreSQL is an open-source relational database and is freely available and modifiable. For the final implementation, however, we chose IBM's DB2 8.1 over PostgreSQL because we found that it could load our data more efficiently and because DB2 has a text-search extender. Our Stanford team used Oracle 9i, which like DB2, offers word-based indexing of text fields. Word-based indexing is essential to support keyword search of MEDLINE titles and abstracts.At Berkeley, we used a Pentium IV Intel Xeon 2.00-GHz dual-processor system, with 1 GB of random access memory (RAM). It had an Integrated Drive Electronics (IDE) hard disk with a rotational speed of 7200 revolutions per minute (RPM). At Stanford, we used a Sun Fire V880 server configured with four 750-MHz processors, 8 GB of RAM, and storage-area-network (SAN) storage for the relational database. We also used a Sun Enterprise 3500 server with eight 400-MHz processors and 4 GB of RAM for reading input files and writing intermediate output files in the Perl version.In this section, we describe loading time and disk-space utilization for the three implementations, followed by examples of queries, emphasizing differences between our system and PubMed.The first implementation used the Java software, run on an Intel system (Linux), using IBM's DB2 database management system. The second implementation also used the Java software, run on a Sun server (SunOS), using Oracle's 9i database-management system. The third implementation used the Perl program, run on networked Sun servers, also using Oracle 9i. Table It took 76 hours (3 days and 4 hours) for the Berkeley group to run Java MedlineParser to load MEDLINE, and 196 hours (8 days and 4 hours) for the Stanford group to do so in Oracle. There were numerous differences between the two systems, and it was not possible to test each variable independently. Therefore, we present our data as a range of possibilities, and recognize that other users will have systems that are not the same as either of ours. We believe that differences in processor speed, memory, disk read-write efficiency, and optimization methods employed in commercial database-management systems may have affected loading times. In addition, the code diverged slightly after the initial transfer of code from Berkeley to Stanford, with the main difference being that the Berkeley version used CLOBs for abstracts, whereas the Stanford version used text fields truncated to 4000 characters (size limit imposed on VARCHAR datatype). The Stanford run was also slower because a log file was generated, whereas this feature was turned off in the Berkeley run.The space requirement for the DB2 instance of MEDLINE at Berkeley was 46.3 GB, of which 10.4 GB are consumed by the abstract text CLOBs, 18.1 GB by the other tables, and 17.8 GB by indexes. The space requirement for the Oracle instance of MEDLINE at Stanford was 37.7 GB. The difference in size is primarily due to differences in the number of records that were loaded. The Berkeley group loaded data from XML files that included all of 2002 (early 2003 release) but also included additional files through April 2003. The Stanford group loaded data from 2002 XML files only. Berkeley parsed and loaded 500 input files (44.4 GB); Stanford parsed and loaded 396 input files (40.8 GB).The Stanford group used Perl ParseMedline to load an additional instance of MEDLINE. Parsing and loading of this instance of the database took place in a two-stage process. In the first stage, Perl ParseMedline parsed the XML files and wrote the data to disk in comma-separated-value files. To reduce processing time, the 396 XML input files were divided into 8 sets of about 50 files each, and sets were processed in parallel. The maximum time required for processing one set was 31 hours (1 day and 7 hours). The output comma-separated-value files required 25.6 GB of disk space. In the second stage, the Stanford team loaded data from the comma-separated-value files into the Oracle database using SQL*Loader, a data loading tool provided by Oracle. This stage took 33 hours (1 day and 9 hours) and used 31.6 GB of space in the Oracle database. This version used less space than the other two primarily because it had less indexing and fewer key constraints. Relaxation of constraints is reasonable because the data are well curated by the NLM, and we can count on data in the XML files released by the NLM to be of high quality.The total time required to parse and load the files in this two-stage process is the sum of the time required to parse the largest file if all files are processed in parallel (first stage) and the time required to load the resulting comma-separated-value files into the database (second stage). Alternatively, if the input files are parsed in series, the time for the first stage would be the sum of the input-file processing times. In our case, we overlapped the runs in a way that was convenient for us, given space and user constraints, and therefore mixed the parallel and serial approach. The overall time for our first stage was 99 hours (4 days and 3 hours); adding this time to the second stage gave a total time of 132 hours (5 days and 12 hours). Given the length of time to process each of our eight batches, we can estimate a lower limit of 64 hours (2 days and 16 hours), and an upper limit of 253 hours (10 days and 13 hours), if we had run the files completely in parallel or in series, respectively.Certain queries that cannot be done easily through the PubMed application programming interface (API) can be done in a single SQL query to our relational database. In this section, we show the results of a several sample queries, run on a version of MEDLINE that contains citations through April 2003.Timing data is presented in terms of "cold" caches and "warm" cache. The cold cache represents the worst case for timing, assuming the database server has just been restarted and the buffer pool is empty. The warm cache represents the best possible performance: running the same query a second time. A typical timing number should fall somewhere between the two; hence these times represent the range of expected times to run the sample queries.pmid is a column in table medline_citation  "Leukemia" is shown for each journal .Many articles in MEDLINE are assigned terms from MeSH. Another capability of this system that distinguishes it from PubMed is the ability to rank order journals according to how many articles those journals have published that have been assigned a particular MeSH term. In the query shown below Table , the numThe result of this query is a table consisting of journals paired with number of publications Table ; note thbegin with "Leukemia". The query would thus include terms such as "Leukemia, Subleukemic" and "Leukemia, Feline." This results in dramatically more results, although the rank ordering is not all that different , and adThe results of this query are shown in Table This query ran in 4 minutes with a cold cache, and in 41 seconds with a warm cache.The DB2 version of the system implementation makes use of the text index that is incorporated into the RDBMS system, using the operator "CONTAINS" which is not part of standard SQL. The following query asks how many papers in the last three years of MEDLINE have been published by authors with affiliations at Berkeley or Stanford Table .This yields the results in Table This query ran in 2.5 minutes with a cold cache, and in 7 seconds with a warm cache.When we ran a similar query to determine the number of articles published by Berkeley, Stanford, MIT, Yale, and Harvard, the increase in time to run the query was minimal. This modified query ran in 3 minutes and 35 seconds with cold cache, and in 15 seconds with warm cache.Thus, SQL makes it easy to quantify and rank order results, and does not require a post-processing step as would be necessary with queries to the PubMed API. Similarly, results retrieved from previous queries can be stored directly in the same database, and reused in later queries by simply joining MEDLINE tables with user-created tables. Again, the power of SQL may alleviate the need for a post-processing step. Instead of writing custom code to integrate results from the current query to PubMed with results from previous queries, the user could use SQL joins to integrate current and previous results.Although our system offers capabilities that the PubMed API does not, we point out that PubMed offers functionality that is not available in our system. For example, the "Related Articles" feature found in PubMed is not available, and links to full text are not available. Also, PubMed provides a user interface that is more intuitive than SQL for an end user who is not a database expert, and will be preferred by users who simply want to look up an article.The value of our system is that it offers greater flexibility for innovative software developers who want to experiment with novel techniques for searching biomedical text, and for system developers who want to build systems of which MEDLINE is a component. If such developers want to offer their systems to end users , they will need to create more intuitive user interfaces. With direct access to the underlying database, developers can create interfaces that are specifically designed to serve the needs of their particular users.In this work, we developed highly customizable Java parsing code and a relational database schema that others may be interested in using or modifying. We developed software that uses the Java programming language and the SAX parser to parse XML-formatted MEDLINE files and load the data into a relational database. We loaded one copy into DB2 and another into Oracle, using our Java tool.We also created a similar tool in Perl. The Perl code is less flexible and not as readily extensible as the object-oriented code of our Java software, but the functionality offered by the resulting database implementations is very similar.Differences in loading time among the three installations of MEDLINE were due to a multiple factors, including differences in processors, disk storage devices, memory, operating systems, database-management systems, methods implemented in the software, and choices made by the user. Factors that affected disk-space utilization included the fact that one group loaded more data than the other, abstracts were stored as CLOBs at one site and as truncated text at the other, and indexes differed. Other groups will have system setups that differ from ours, and may make their own modifications to the code that affect their loading times. By presenting data on three examples, we have demonstrated a range of performance results as a guide to what other users might expect at their sites.Future work includes adding functionality to update the system to new versions of MEDLINE, and to accommodate MEDLINE update files. The Stanford group has begun to use MEDLINE to extract drug-gene relationships from the literature, and the Berkeley group used the system, augmented with data from MeSH and LocusLink, to compete in the TREC 2003 genomics track competition . As we c.An updated version of our Java code accepts MEDLINE XML input files released in early 2004 that conform to the latest DTD (November 2003). The open-source code for this most current version of MedlineParser is available at Project name: Java MedlineParserProject web page: Operating system: Platform independentProgramming language: JavaOther requirements: Java 1.4.1 or higher, JAXP, relational database, and JDBC driver appropriate for the particular target databaseLicense: NoneAny restrictions on use by non-academics: NoneProject name: Perl ParseMEDLINEProject web page: Operating system: Platform independentProgramming language: PerlOther requirements: Perl 5.8 or higher to handle MEDLINE Unicode data (if writing directly to database), or earlier version of Perl , Perl modules DBI and XML::Parser::PerlSAX, relational database, and Perl database driver appropriate for the particular database License: NoneAny restrictions on use by non-academics: NoneDO, GB, and AS, developed the MEDLINE database schemas. GB and AS designed and implemented the Java MedlineParser. GB and AS ran MedlineParser to install MEDLINE in DB2 at Berkeley. DO ran MedlineParser to install MEDLINE in Oracle 9i at Stanford. DO developed Perl ParseMedline and ran it to install the second version of MEDLINE at Stanford. DO and GB were primary authors of the article, and the remaining authors added their contributions to the manuscript. MH supervised the work at Berkeley; RB supervised the work at Stanford.