Eliminate the guesswork for experiment design and data analysis
Fluidity Insight is the powerful analytical platform that maximizes the value of your Fluidity One-M data.
It enables unique assays but most importantly can support you by guiding you through your experiment. This is not simply an assay wizard or guided setup tool, but will let you know which datapoints you can add to your existing dataset to best improve the fit quality of your experiment.
Easy data management
An intuitive interface ensures short training times – importing data is as simple as drag and drop, and you can combine data from different days, different users, and even different sites to create and analyze comprehensive datasets.
Analyzing the experiment results
Fluidity Insight makes analysis easy – simply select your assay, choose the data to include from the self-organizing list and the machine learning-powered platform will take care of the rest. Automated outlier identification removes the need for manual data curation.
Easy AI/ML driven experiment design
Fluidity Insight uses Bayesian inference, the same approach that underpins artificial intelligence and machine learning technology, to predict which concentration combinations to measure to most rapidly improve fit convergence. This greatly accelerates experimental progress by eliminating guesswork and ensures that the minimum of data is collected to get publication-quality results.
Get to the destination faster with templates for different assays
Fluidity Insight offers a range of assays to suit your needs.
SAffCon
The SeroAffinity and Concentration assay allows the determination of affinity and calibration-free concentration of a target, even in complex matrices. This is ideally suited to characterization of antibodies directly in serum.
MAffCon
The Membrane Affinity and Concentration assay is ideal for working with crude membrane protein libraries for which it can determine the affinity of an interaction to a receptor or other membrane protein of interest and the copy number of that protein in the source cell membrane.
NeSt
The Neuroaffinity and Stoichiometry assay determines the affinity and interaction stoichiometry of an interaction. It has been used primarily with neurodegeneration-associated amyloid fibrils but can be usefully applied to any system for which binding stoichiometry is of interest.
Complexity simplified
Setting up experiments when nothing is known about the samples can be very challenging, which is why we have established a simple and robust workflow to get publication-quality data using as little sample, time and effort as possible.
Step 1
Run standard conditions for initial dataset, irrespective of sample.
Step 2
Analyze initial data using Fluidity Insight. If convergence is satisfactory, experiment is complete. Make a cup of tea.
Step 3
If fit is not yet sufficiently converged, refer to Bayesian Experimental Design (BED) plot for guidance of additional datapoint(s) to run.
Step 4
Run recommended datapoints and add data to existing dataset
Step 5
Analyze expanded data using Fluidity Insight. Repeat cycle if needed.
Extract the full picture from your binding data
Determination of calibration-free concentration
Relying on calibration standards for concentration determination adds cost and risk to a project. Sourcing and maintaining high quality standards can be time consuming and expensive. Running calibration curves takes time away from more productive experiments. SAffCon eliminates these drawbacks by directly determining concentration of a binding target in your sample of interest using only a fluorescently labeled probe as additional reagent.
Determination of membrane protein copy number
RNA copy numbers are readily determined using modern genomic methods, however the expression levels of membrane proteins are usually still only divided into crude brackets. Quantitative determination of copy number can provide deeper understanding of therapeutic function, enable fine-grained patient selection, and facilitate normalization of bio-assays.