It would take hours and hours to learn a little bit of Copasi. A quick answer. There is a simulation mode, meaning that you have a model with all parameters. Then, you simulate the kinetics and time profile of all intermediates. There is a fitting mode (parameter estimation). You build a hypothetical model and fit the experimental curves. The software finds the best set of parameters, which gives the theoretical curves close to the experimental. If you fail to achieve the global fitting, then your model is likely to be wrong, or your variable parameters are not in the right range. If you can't achieve a global fit then modify your model. The main problem in Copasi is to build the correct chemical model.

Here's a complete guideline for performing a global fit using multiple experimental datasets in COPASI, especially suited for kinetic experiments where you have many parameters and only a few experimental observables (like Time and concentrations).

🧪 Goal:

You want to simulate and fit multiple experimental datasets simultaneously (global fitting) in COPASI to estimate shared parameters across all experiments.

✅ Prerequisites:

• You’ve built your kinetic model in COPASI.
• You have multiple experimental datasets (e.g., replicates or experiments under different initial conditions).
• Each dataset is a .txt or .csv file, containing columns like Time, Concentration1, Concentration2, etc.

🔧 Step-by-Step Guide:

Step 1: Prepare Your Experimental Data Files

You must prepare multiple CSV/TXT files, one for each experiment. All must have:

• A "Time" column (usually the first)
• Columns matching model observables (species concentrations)
• Ensure all files use the same format (delimiter, header names, etc.)

Example (experiment1.csv):

Time, A, B, C 0.0, 1.2, 0.0, 0.0 1.0, 1.1, 0.1, 0.0 ...

Step 2: Set Up Your Model in COPASI

• Load or build your model (reactions, species, compartments, rate laws).
• Make sure your species names match the column headers in your data files.
• Define the global parameters you want to fit.

Step 3: Define Tasks for Global Parameter Estimation

🔄 Instead of using “Parameter Estimation” alone, use "Parameter Estimation + Experimental Conditions + Multiple Datasets":

Step 4: Global Parameter Sharing

• All added experiments share the same model and parameters.
• Parameters listed in the “Optimization Items” section will be fitted globally, i.e., COPASI will try to fit all experiments at once using the same parameter values.
• If some parameters are experiment-specific, you’ll need to: Clone the model for each set (advanced workaround), or Introduce parameters as functions of a dummy variable (not typical, but possible with scripting).

Step 5: Configure Optimization Settings

• Choose an optimization method (e.g., Genetic Algorithm, Levenberg–Marquardt).
• Set bounds and initial guesses for parameters.
• Use “Reduced Chi-Square” or “Likelihood” as the objective function.

Step 6: Run the Parameter Estimation Task

• Click “Run” and COPASI will perform a global fit across all datasets.
• After completion, results will show fitted parameter values, along with the residuals per experiment.

Step 7: Validate and Analyze

• Use the “Plot” tab in each experiment to visualize fit vs. data.
• Export fitted curves and residuals if needed.

💡 Tips:

• Ensure Time units and concentration units are consistent across model and data files.
• If simulations don't match well, try tweaking initial parameter guesses or adjusting noise levels in the experiment tabs.
• For reproducibility, save your COPASI project as .cps regularly.

📚 Optional Advanced: Scripting with COPASI

• You can also perform batch fitting or global sensitivity analysis using COPASI's Python API (PyCoTools) or via command-line for automation.

❓ Common Mistakes:

Problem Likely Cause. Only one dataset is being fitted. You didn’t add all the experiments correctly. Wrong observable mapping, Column names in CSV ≠ , species names, Fit doesn't converge, Bad initial guesses or poor model fit, Parameters seem overfitted, Too few data points or under-constrained