Free Online BLI Data Analysis
Upload your Octet BLI data, fit binding kinetics, and export publication-ready results — directly in your browser, no software installation required.
What Is Bio-Layer Interferometry (BLI)?
Bio-layer interferometry (BLI) is a label-free, real-time optical technique used to measure biomolecular interactions. Developed by Fortébio (now part of Sartorius), BLI has become one of the most widely used methods in drug discovery, antibody characterization, and protein engineering for determining binding kinetics and affinity constants.
BLI works by analyzing the interference pattern of white light reflected from two surfaces: an internal reference layer and a biolayer at the tip of a fiber-optic biosensor. When molecules bind to or dissociate from the biosensor tip, the optical thickness of the biolayer changes, causing a measurable shift in the interference pattern. This shift is recorded in real time as a sensorgram — a plot of binding response (in nanometers) over time.
Unlike surface plasmon resonance (SPR), which uses continuous microfluidic flow over a gold-coated sensor chip, BLI operates in a "dip-and-read" format. Biosensor tips are dipped into microplate wells containing analyte solutions. This eliminates the need for complex fluidics, reduces sample consumption, and makes BLI inherently less sensitive to refractive index artifacts from buffer mismatches — a common challenge in SPR experiments.
BLI data analysis involves extracting kinetic rate constants — the association rate constant (ka), the dissociation rate constant (kd), and the equilibrium dissociation constant (KD = kd / ka) — by fitting sensorgram data to appropriate binding models. KinetiHub makes this entire workflow accessible online, for free.
KinetiHub's BLI Data Analysis Features
Everything you need to go from raw Octet data to publication-ready kinetic parameters — in your browser.
Native Octet File Support
Upload your Octet experiment folder directly — FRD files, experiment metadata, and sensor configuration are all parsed automatically. No file conversion or reformatting required. KinetiHub reconstructs the full experiment including baseline, loading, association, and dissociation steps.
AI-Powered Smart Upload
KinetiHub's intelligent upload system automatically detects your experiment type, identifies step boundaries, maps sensor assignments to wells, and suggests reference sensors — reducing setup time and minimizing human error in data preparation.
Interactive Curve Viewer
Visualize your BLI sensorgrams with an interactive, zoomable curve viewer. Select individual wells, toggle between raw and processed data, overlay fits, and inspect residuals — all in real time with no lag, even for 96-well experiments.
Multiple Fitting Models
Choose from a comprehensive set of kinetic models: 1:1 Langmuir for simple interactions, mass transport-limited for diffusion-limited binding, heterogeneous ligand for mixed surfaces, two-state (conformational change) for induced-fit mechanisms, and steady-state affinity for equilibrium analysis.
Data Cleanup Tools
Clean your BLI data with reference subtraction (double referencing), baseline correction, Y-axis alignment, step cropping, and outlier removal. Proper data preprocessing is essential for reliable kinetic fitting, and KinetiHub makes each step transparent and reversible.
Export & Share
Export your fitted parameters, processed sensorgrams, and residual plots for publications and presentations. Share experiments with collaborators through KinetiHub's platform — no more emailing zip files of Octet folders.
BLI vs SPR: Key Differences
🔬 BLI
- •Dip-and-read format using fiber-optic biosensor tips
- •Measures optical thickness changes via white light interference
- •No fluidics — less prone to clogging and air bubbles
- •Higher throughput with 8- or 16-channel parallel measurements
- •Less sensitive to refractive index changes from buffer mismatches
- •Disposable biosensor tips (single-use or limited regeneration)
🌊 SPR
- •Continuous microfluidic flow over a gold sensor chip
- •Measures refractive index changes via evanescent wave coupling
- •Higher sensitivity — detects smaller molecules more reliably
- •Reusable sensor chips with well-characterized surface chemistries
- •Lower noise floor for high-quality kinetic measurements
- •More stringent buffer matching and running buffer requirements
Both techniques measure the same fundamental kinetic parameters (ka, kd, KD). The choice depends on throughput needs, sample type, and sensitivity requirements. Read our full BLI vs SPR comparison →
Supported BLI Instruments
KinetiHub supports BLI data from all major instrument platforms.
Sartorius / Fortébio Octet
The Octet platform is the most widely used BLI system in the biopharmaceutical industry. KinetiHub natively parses Octet experiment folders.
- ✓ Octet RED96 / RED96e (8-channel, 96-well)
- ✓ Octet R8 (8-channel, next-gen)
- ✓ Octet R2 (2-channel, benchtop)
- ✓ Octet K2 (2-channel, entry-level)
- ✓ Octet HTX / RH16 / RH96 (high-throughput)
Gator Bio
Gator Bio instruments offer BLI analysis with competitive pricing and compatible biosensor tips. Export your Gator data and upload it to KinetiHub.
- ✓ Gator Plus (8-channel)
- ✓ Gator Prime (16-channel)
Gator Bio data may require export to CSV format before upload. Check our BLI guide for details.
Common BLI Biosensor Types
Choosing the right biosensor tip is critical for BLI experiment success. Here are the most commonly used types and their applications.
SA (Streptavidin)
Captures biotinylated molecules. The most versatile biosensor — used for biotinylated antibodies, peptides, nucleic acids, and small molecules.
AHC (Anti-Human IgG Fc)
Captures human IgG antibodies via Fc region. Ideal for antibody screening, epitope binning, and affinity ranking without biotinylation.
AR2G (Amine Reactive 2nd Gen)
Covalent amine coupling for any protein with primary amines. Similar to SPR amine coupling. Good for ligands that cannot be biotinylated.
HIS1K (Anti-Penta-HIS)
Captures His-tagged proteins with high specificity. Popular for recombinant proteins expressed with polyhistidine tags.
NTA (Ni-NTA)
Captures His-tagged proteins via nickel chelation. Offers oriented immobilization and easy regeneration with imidazole.
AHQ (Anti-Human Fab-CH1)
Captures human Fab fragments. Useful when Fc-mediated effects need to be avoided, or for Fab-displayed antibody libraries.
AMC (Anti-Murine IgG Fc)
Captures mouse IgG antibodies. Essential for murine hybridoma screening and mouse antibody characterization.
SSA (Super Streptavidin)
Enhanced streptavidin surface with higher binding capacity. Suitable for larger biotinylated molecules or when higher signal is needed.
FAB2G (Anti-Human IgG Fc)
Second-generation anti-human IgG Fc biosensor with improved binding capacity and performance over AHC.
Sensor choice affects immobilization orientation, regeneration options, and data quality. Learn more in our kinetics fundamentals guide.
BLI Data Analysis Workflow
From raw sensorgrams to kinetic constants in five steps. Here's how BLI data analysis works on KinetiHub.
Load Your Data
Upload your Octet experiment folder (or drag-and-drop individual FRD files). KinetiHub's smart parser automatically identifies the experiment structure, maps wells to sensors, and reconstructs all assay steps. You'll see your raw sensorgrams within seconds.
Reference Subtraction
Subtract reference sensors to remove systematic drift, non-specific binding, and buffer artifacts. Double referencing (subtracting both a reference sensor and a zero-concentration control) is the gold standard for BLI data and is fully supported. This step is critical for obtaining clean binding curves.
Alignment & Baseline Correction
Align your sensorgrams to a common baseline and zero the Y-axis at the start of the association phase. Proper alignment ensures that the fitting algorithm can accurately model the binding kinetics. KinetiHub offers both automatic and manual alignment options.
Kinetic Fitting
Select a binding model and fit your processed sensorgrams. KinetiHub performs global fitting across all concentrations simultaneously, extracting ka (association rate), kd (dissociation rate), and KD (equilibrium constant). Inspect residuals to validate the quality of your fit and ensure the chosen model is appropriate.
Interpret & Export
Review your fitted parameters, assess the quality metrics (chi-squared, R², residual distribution), and export your results. KinetiHub provides fitted curves overlaid on experimental data, parameter tables, and residual plots ready for publication or internal reporting.
Common BLI Pitfalls to Avoid
Skipping Reference Subtraction
Without proper reference subtraction, systematic drift and non-specific binding will distort your kinetic parameters. Always include reference sensors in your experiment design.
Overloading the Biosensor
Excessive ligand loading leads to mass transport artifacts, rebinding effects, and avidity. Aim for low ligand density (0.5–1.0 nm loading response) for kinetic experiments.
Insufficient Analyte Concentrations
Use at least 5 analyte concentrations spanning the expected KD (ideally 0.1× to 10× KD). Too few concentrations or a narrow range leads to poorly constrained fits.
Choosing the Wrong Model
A 1:1 Langmuir model won't accurately fit heterogeneous surfaces or conformational change interactions. Inspect residuals — structured patterns indicate model mismatch.
Ignoring Dissociation Phase Length
Short dissociation phases underestimate kd for slow off-rate interactions. Ensure the dissociation phase is long enough to observe at least 5–10% signal decay.
Buffer Mismatch Effects
While BLI is more tolerant than SPR, large differences between sample and running buffer can still cause step artifacts. Match buffers as closely as possible.
Struggling with your BLI data? Check our comprehensive troubleshooting guide for step-by-step solutions to common problems.
Frequently Asked Questions
Can KinetiHub analyze Octet data?
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Yes. KinetiHub fully supports Octet BLI data analysis. Upload your Octet experiment folder (containing FRD files, experiment metadata, and sensor information) directly into KinetiHub. The platform automatically parses the folder structure, identifies wells, and prepares your data for reference subtraction, alignment, and kinetic fitting — no file conversion needed.
What Octet file formats are supported?
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KinetiHub supports the native Octet folder structure including FRD (Fortébio Raw Data) files. Simply upload the entire experiment folder as exported by your Octet instrument. KinetiHub's smart upload system automatically detects the folder hierarchy, parses sensor assignments, and reconstructs the full experiment with all steps (baseline, loading, association, dissociation).
Is KinetiHub free for BLI analysis?
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Yes. KinetiHub offers free BLI data analysis with no installation required. You can upload Octet data, perform reference subtraction, align sensorgrams, and fit kinetic models directly in your browser. Advanced features and larger datasets may be available through premium tiers, but the core BLI analysis workflow is freely accessible.
How does BLI data analysis differ from SPR?
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BLI measures the interference pattern of white light reflected from a biosensor tip, detecting changes in optical thickness as molecules bind. SPR measures refractive index changes near a gold surface. While both techniques extract the same kinetic parameters (ka, kd, KD), BLI data tends to have higher noise but is less sensitive to buffer artifacts. The same fitting models apply to both. KinetiHub supports both BLI and SPR data analysis.
Can I use KinetiHub instead of Octet Analysis Studio?
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KinetiHub can serve as a complementary or alternative tool to Octet Analysis Studio (formerly Fortébio Data Analysis). KinetiHub offers browser-based analysis with no installation, multiple fitting models, interactive curve viewing, and modern export options. It is particularly useful for collaborative work, cross-platform access, and when Octet Analysis Studio licenses are limited.
Analyze Your BLI Data Now
Upload your Octet experiment, fit binding kinetics, and get publication-ready results — all for free, right in your browser.