Drugs have tests {drug experiment}.
experimental design
Samples can test properties and activities. Experiment uses numbers and sample types from population, as well as methods and instruments. Three-level design assigns three levels (-1,0,1) to each factor to determine how responses vary with factors or variables, for making mechanistic physico-chemical models, using physical chemical properties as factors, or empirical polynomial models, using arbitrary variables as factors. Three-level mixture design determines whether factor is useful or significant or not. Two-level design assigns two levels (0,1) to each factor to determine whether factor is useful or significant, for screening, searching, or filtering.
kinetics
Experiments can read samples multiple times over time to find reaction rate or inhibition constant.
Biological reaction series can makes protocols {assay, experiment}. Protocol or method series use reagents to identify compounds, genes, proteins, or quantities.
Methods {protocol, experiment}| can run experiments. Experiments can perform steps or tasks on samples: prepare samples, mix with reagents, hybridize, wash, detect, and analyze.
Samples can be read more than once {replication, sample}.
In screening, calculated results translate into given ranges {scoring}, like high, medium, or low.
three-level design {Box-Behnken design}.
Experimental designs {CARSO approach} can be for random compound screening in experiment series.
three-level design {central composite design}.
Experimental designs {Craig plot} can be for random compound screening in experiment series.
Test-set selection methods {D-optimal design} can try to ensure design-space sampling, if positions vary, and can account for excluded volumes.
Experiment designs {sequential optimization} can use steps toward optimum.
Experimental designs {Topliss tree} can be for random compound screening in experiment series.
Experiments {dose response curve} can read samples at different concentrations and fit IC50 curves.
Experiments {ELISA} can back-calculate sample concentration, using reference curve.
Automated assays {High-Throughput Screening} (HTS) can test many diverse compounds against enzymes or cell targets, to identify possible new drugs.
More than one sample can be in wells {pooling} in screening experiment. Samples mix in plate wells according to patterns, so system measures all samples the same number of times. Total well number is fewer than with one sample per well.
Experiments {ratio experiment} can read samples twice, for agonist vs. antagonist, to determine activity ratios.
Automated assays {screening} {high-throughput screening} can identify promising compounds from compound libraries.
users and groups
Roles (types of users) have a set of privileges. Users have roles.
inventory
Inventory database has sample IDs.
experiment type: ELISA
Plate has high and low controls (averages), dose-response titration (IC50), and replicated samples (averages of % inhibition); Fit the result into the standard curve for a first estimate IC50, and back calculate the concentration of an expressed protein by using a standard reference curve on each plate. Look for the well that has a result which falls on the linear portion of the standard curve.
experiment type: ratio
Each plate is read twice, first reading the growth of a specific protein, and second measuring the survival of various cells (or agonist vs. antagonist receptors). % inhibitions are calculated for each of these readings, and then the ratio. Sort on any of the three results. Since plates are carried from one reader station to the next, they may get out of order, may be put into the reader backwards, or may get dropped, so data system should be able to deal with all of these problems. This is a case of 'multiple data points per well'.
experiment type: dose response, multiple calculations
Several different models calculate, fit, and display the IC50 curves simultaneously, like straight line regression and 4-parameter curve fitting, with constant high and low inflection points. Then register one or more, with model name, parameters, and comments.
experiment type: titration
From each daughter plate, 3 to 4 assay plates at different concentrations are made. An activity value is calculated for each well, as well as the activity changes as a function of concentration.
experiment type: titration and dose response across plates
A single 96 well plate is filled with 48 samples in duplicate. The plate is then copied across 8 other plates at different concentrations. The result from the 9 plates is used to calculate a dose response curve and IC50.
experiment type: no controls on data plates
All controls (high, low, reference, dose-response curve) are on one or more reference plates, interspersed throughout the runset. Calculate a 'curve' for the standards on the reference plates, by plate sequence or time. Could use controls only on nearest reference plate.
experiment type: whole plant experiment
Growth of different plant species at time intervals, with qualitative and quantitative (Scores) results. At the end of the experiment, a report of the changes over time. A ''score' is based on observing the well and is a coded value that means something like 'severe yellowing of stem' or "intense chlorosis of stem" with more than one score per well.
experiment type: colormetric assay
Inhibition makes well white, but experiment failure makes "milky white". A scientist can see the difference, but the automated reader can be fooled. so scientist marks specific wells as bogus BEFORE the results are read into the data system.
experiment type: kinetics
Each well is read multiple times over time and the calculated value is based on the multiple raw values. All raw data is saved or only the final derived value. Plot of the timed values.
experiment type: Ki values
Dose response experiment, with a ligand of known activity value and concentration recorded. The IC50, and further calculations based on IC50, ligand activity, and ligand concentration are saved for each sample.
experiment type: pooling and replicates
Pooled plates have replicated mixtures. Same as HTS and REP, but with a reference to mixture in Inventory.
experiment type: non-plate based experiments
Row of tubes, lawn format, and so on.
variable group
A candidate variable group is selected or built. The group should take account of the dimension variables to be used for the RFM upload and for the layout. The group should take account of the layout actual and placeholder values. The group should have clauclations from raw data and places to mark data invalid or promotable. The group might have a Review check.
layout
A candidate layout for the Well table is developed. The layout should have an actual concentration, actual well types for High/Low/Data/Reference wells, and placeholders for the sample IDs. The dimension, calculation, and set variables should work with the layout.
calculations
Candidate calculations are selected or made for the calculation variables of the variable group. The calculations need to follow the default or nondefault rules for calcs, especially for parent-child relations, performance, and using ASSOC, MATCHALL, and CONDITION= correctly.
reader file and format
An actual or realistic reader file is available from such an experiment, and the reader file sections are made and tested, and the sections assigned to variables of the variable group. Ordinals are assigned to Row and Column variables, along with dimensions that match the variable group and layout.
protocols and templates
Protocol assays or attributes might be added to the protocol. (Sections or templates might be built for the protocol display.) A protocol is selected or made, which contains the variable group, layout(s), and RFM formats, plus any required fields.
dictionaries and terms
Dictionary terms and dictionaries might be added for use in the protocol.
result tables
Result tables are selected or made, together with the result maps from the variable group to the result table(s).
experiments
The completed protocol is used to start an experiment, the layout and the RFM format are added, then the reader file(s) are selected. Any placeholders in the layout must be filled in. (Assembler). The experiment is calculated and stored.
analysis
The experiment is analyzed to determine if any data is invalid or questionable, and a recalculation and store occurs. A rule might be used for automatic checking for bad controls, dropped plates, and so on. The analysis might include a mod to a calc formula, data, or a change of model for a curve.
decision
The experiment is analyzed to determine if any samples are worthy of further experimentation. Such samples are marked for further testing. A score might be assigned based on rules.
review and/or release
A review is made (typically by a higher authority) of the results. That such a review has been made is noted somewhere. Perhaps the data is allowed to be used or seen by other workgroups, or is sent to corporate database.
browsing
Other persons at a company want to see a summary of the validated results, in a set format for all researchers, to avoid duplication and error.
Outline of Knowledge Database Home Page
Description of Outline of Knowledge Database
Date Modified: 2022.0225