Generic non-linear curve fitting in Synthace lets you define and fit your own mathematical models — such as Hill slopes or Michaelis–Menten equations — directly inside the Prepare Data app.
This feature is ideal for advanced users who need flexibility beyond the standard four- or five-parameter logistic (4PL/5PL) models.
💡 Note: If you’re new to curve fitting, start with the pre-built 4PL/5PL tutorials and the data reshaping guide before using generic fitting.
What you can do
With generic non-linear curve fitting, you can:
Fit custom equations to experimental data (e.g., Hill or Michaelis–Menten models)
Run fits across multiple executions at once
Constrain parameters with upper and lower bounds for stability and accuracy
Review fit diagnostics and visualizations directly in Prepare Data
Save fitted parameters, ready for downstream analysis steps
Step-by-step guide
Reshape your data
Open your workflow and launch the Prepare Data app.
Reshape or pivot your data against the independent variable you want to fit (for example, time for a Hill equation).
Confirm Synthace has automatically recognized your independent variable as the x-axis.
Select the dependent variable (for example, fluorescence signal or reaction product concentration).
Define your equation
In the Calculate tab, you can write your own mathematical model.
Select the data series you want to fit.
Type or paste your custom equation into the formula field.
Use curly braces
{ }to define the parameters you want the model to fit.Provide the parameter names i.e. the values that you want Synthace to fit, in the parameter fields
For example, a Hill equation might look like:
{ymax} * t**{hill} / ( {ec50}**{hill} + t**{hill} )Here:
tis your independent variable{ymax},{ec50}, and{hill}are the parameters Synthace will fit
You can either type equations manually or use the formula helper menu at the bottom of the editor to insert variable automatically.
‼️ Note: Variables and parameters, as defined in your formula, use the same syntax, i.e., they must be contained within {}.
Ensure that the parameters you are fitting are defined in the model parameters menu shown above. These values will be estimated by the curve fitting algorithm and saved to your data table for use in downstream analysis or model fitting.
Apply parameter constraints
Because the generic fitter supports any custom equation, Synthace uses broad default bounds.
To improve accuracy, you can constrain parameters to sensible ranges.
Click each parameter to add lower and/or upper bounds
For example, set
{ymax}to have a lower bound of 0 if it must remain positiveAdjust bounds based on your expected biological limits
🧭 Setting sensible parameter constraints helps the model converge faster and avoid implausible results.
Run the calculation
When your model is ready:
Click Calculate.
Synthace will:
Fit the custom equation to all selected runs simultaneously
Use your parameter bounds during optimization
Display visualizations of the fitted curves
Depending on complexity, fitting may take a few seconds per run.
After the calculation, you’ll see:
Fitted curves for each run
Fit parameters, summaries and diagnostics
Any warnings about parameter limits or convergence issues
Review and apply the fit
Check the plotted curves to confirm they match the experimental trend.
Hover over points to see raw and fitted values.
Review RMSE or any fit quality indicators provided.
If a fit looks poor:
Verify that your data is correctly reshaped.
Inspect for outliers or missing values.
Tighten or loosen parameter bounds and re-run the fit.
When you’re satisfied:
Click Apply to save the fitted parameters.
Click Save Draft to store the step in Prepare Data — ready for analysis or DOE.
Edit or repeat fits
You can return to the Prepare Data step at any time to edit or re-fit your equations.
Put the step back into an editable state.
Update formulas or parameter constraints as needed.
Recalculate — Synthace will generate new fitted values and update your results table.
Example models
The video demonstration includes:
Hill slope fit using time as the independent variable
Michaelis–Menten model for enzyme kinetics
Both are defined manually using the same equation builder process.
Check out the table below for more example formulas:
Model | Formula | Syntax | Parameters |
Michelis-Menten kinetics |  | {Va} / ({km} + {T}) | Va, Km |
Hill Equation |  | ({Ymax} * {T}**{n_hill}) / ({EC50}**{n_hill} + {T}**{n_hill}) | EC50, n_hill |
Exponential decay |  | {FP - Time (s)} * exp(-1 * {L} * (t - {x_value})) | L |
Double exponential decay |  | {A1}*exp(-1 * {K1} * {T}) + {A2}*exp(-1 * {K2} * {T}) | A1, A2, K1, K2 |
Quadratic |  | {A}*x**2+{B}*x+C | A,B,C |
Cubic |  | {A}*x**3+{B}*x**2 x+C*x + D | A,B,C,D |