# Simbiology Fit Model

Fit SimBiology model parameters to data, fitproblem, population NLME, virtual...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill simbiology-fit-model -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill simbiology-fit-model -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill simbiology-fit-model -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill simbiology-fit-model -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Fit SimBiology model parameters to data, fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Fit SimBiology Models

Estimate parameters from data using `fitproblem`, fit population models
with NLME, generate virtual patients, and compute NCA metrics.

## When to Use

- "fit", "estimate", "calibrate" model parameters
- Parameter estimation from experimental/observed data
- Population PK/PD, NLME, mixed effects, inter-individual variability
- Virtual patients, virtual cohorts
- NCA, AUC, Cmax, Tmax, half-life, clearance
- Keywords: "fit", "estimate", "calibrate", "population", "NCA", "AUC"

## When NOT to Use

- Model construction or diagram (use `simbiology-build-model`)
- Simulation without fitting (use `simbiology-simulate-model`)
- Sensitivity analysis (use `simbiology-simulate-model`)

## Must-Follow Rules

### 1. Use `fitproblem` for parameter estimation

Always use `fitproblem` instead of calling `sbiofit` or `sbiofitmixed`
directly. `fitproblem` provides a unified, declarative interface:
```matlab
prob = fitproblem;
prob.Model = model;
prob.Data = data;
prob.ResponseMap = "Species = DataColumn";
prob.Estimated = estimatedInfo({'param'}, 'Bounds', [lo hi]);
results = fit(prob);
```
Do NOT call `sbiofit(model, data, ...)` or `sbiofitmixed(model, data, ...)`
directly, their positional argument signatures are error-prone.

### 2. Fitting requires `groupedData`, NOT a plain table

Always wrap data:
```matlab
data = groupedData(table(...));
data.Properties.IndependentVariableName = 'Time';
```

### 3. `ResponseMap` maps model outputs to data columns

Format is always `"ModelOutput = DataColumnName"`:

```matlab
% Single compartment, use species name on the left
prob.ResponseMap = "Drug = DrugConc";

% Multi-compartment, use qualified name to disambiguate
prob.ResponseMap = "Central.Drug = DrugConc";

% When species name matches data column name, still use the = format
prob.ResponseMap = "Drug = Drug";
```

Use the unqualified species name unless the same species name exists
in multiple compartments (then qualify with `Compartment.Species`).

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/computational-biology/simbiology-fit-model/SKILL.md