BioLab/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

What is BioLab

BioLab Parameter Explorer is a scientific web application that estimates kinetic parameters for microbial growth and substrate consumption models. Given time-series measurements of biomass (cells, g/L) and residual substrate (g/L), it runs Particle Swarm Optimization (PSO) over seven built-in kinetic models and ranks them by the Akaike Information Criterion (AIC).

The app is packaged with Apache Cordova and targets Electron (desktop), Android, and Browser.

Commands

Tests (run from www/)

cd www && node tests/demo.mjs
cd www && node tests/synthetic-search.mjs
# or both at once:
cd www && npm test

Tests use Node.js directly — no test runner or build step needed.

Run / build (Electron)

cordova run electron           # dev run
cordova build electron --release --verbose   # release build

Run on Android

# Set up environment first (or source env.sh):
source env.sh
# env.sh sets ANDROID_HOME, ANDROID_SDK_ROOT, PATH entries for the SDK,
# and isolates Gradle cache to ./.gradle, then calls `cordova run android`.

Serve in browser

cordova run browser
# or just open www/index.html via any static file server

Architecture

www/
  index.html        — single-page app; all UI logic is an inline <script type="module">
  src/
    conhecidos.js   — pure functions: the seven kinetic µ(S) equations + Pirt
    runge-kutta.js  — RK4 solver (RK4) and point interpolator (RK4getvalue)
    Objective.js    — Objective class: wraps experimental data + ODE, computes normalized SSR
    PSO.js          — PSO class: initializes swarm, runs iterations, exposes pos_best_g / err_best_g
    search.js       — orchestrates everything: builds ODE functions (model × Pirt coupling),
                       runs PSO per model, renders Plotly charts, computes AIC, sorts results
    rrandom.js      — Math.random wrapper used by PSO
    solve.js        — standalone legacy helper (not imported by search.js)
  tests/
    demo.mjs              — smoke test: PSO converges to a finite error
    synthetic-search.mjs  — accuracy test: PSO recovers known Monod params from synthetic data
  assets/
    dados.json      — default experimental dataset loaded on page start

Data flow

1. index.html collects experimental data, PSO hyperparameters, and per-model parameter bounds from the form.
2. It calls main(data, { alg, bounds, onProgress }) exported from search.js.
3. For each of the seven models, search.js constructs an ODE function (<model>Pirt) that couples a growth-rate formula from conhecidos.js with the Pirt substrate consumption equation.
4. An Objective instance wraps the ODE and experimental data. It normalizes residuals by column mean before summing squared errors — this makes SSR dimensionless and comparable across variables with different scales.
5. PSO minimizes the objective over the parameter bounds, producing pos_best_g (best parameter vector).
6. RK4 integrates the ODE at 500 internal steps; RK4getvalue interpolates from that solution to exact experimental time points.
7. After all models complete, results are sorted by AIC and a comparison table is rendered.

Key design constraints

• The frontend is no-build: ES modules loaded directly in the browser; no bundler.
• www/package.json has "type": "module" so Node can run the tests with native ESM imports.
• External CDN dependencies: KaTeX (math rendering), Plotly (charts). Both are loaded in index.html; search.js assumes Plotly and katex are globals.
• The Objective constructor detects column order from header keywords (Portuguese and English), so column order in the data table matters only when headers are absent or unrecognized.
• PSO uses Math.random directly — synthetic-search.mjs patches it with a deterministic LCG for reproducible test results.
• solve.js is a legacy standalone function that references globals (RK4, Plotly, document) and is not imported anywhere in the current codebase.