# Microbial Dynamics

Microbial population dynamics modeling and analysis.

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Marketing
- License: MIT license
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill microbial-dynamics -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill microbial-dynamics -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill microbial-dynamics -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill microbial-dynamics -g -a gemini-cli -y`

## Description

Microbial population dynamics modeling and analysis. Bacterial growth curve fitting (logistic, Gompertz, Baranyi), Lotka-Volterra community dynamics, Gillespie stochastic simulation, biofilm quantification, CFU enumeration, and genome annotation. For metabolic modeling use cobrapy; for sequence analysis use biopython.

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

# Microbial Dynamics: Population Dynamics & Modeling

## Overview

Microbial Dynamics provides computational tools for modeling and analyzing microbial populations. This skill covers bacterial growth curve fitting using standard models (logistic, Gompertz, Baranyi), multi-species community dynamics via Lotka-Volterra equations, stochastic population simulation using the Gillespie algorithm, biofilm quantification from crystal violet assays, colony-forming unit enumeration with statistical analysis, bacterial genome annotation via Prokka, and simplified anaerobic digestion modeling.

## When to Use This Skill

- Fitting bacterial growth curves from OD600 time-series data
- Extracting growth parameters: lag phase duration, maximum growth rate (mu_max), carrying capacity (K)
- Modeling multi-species microbial community interactions
- Running stochastic simulations of gene expression or population dynamics
- Processing crystal violet biofilm assay data
- Calculating CFU/mL from serial dilution plating
- Annotating bacterial genomes and extracting gene statistics
- Modeling biogas production from anaerobic digestion

**Related Skills:** For constraint-based metabolic modeling use `cobrapy`. For sequence manipulation and BLAST use `biopython`. For statistical analysis use `statistical-analysis`.

## Installation

```bash
uv pip install scipy numpy pandas matplotlib
```

For genome annotation (optional):
```bash
# conda install -c bioconda prokka
```

## Quick Start

```python
from scipy.optimize import curve_fit
import numpy as np

# Logistic growth model
def logistic(t, y0, K, r, lag):
    return K / (1 + ((K - y0) / y0) * np.exp(-r * (t - lag)))

# Example OD600 data
time = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24])
od600 = np.array([0.02, 0.02, 0.03, 0.06, 0.15, 0.38, 0.72, 1.05, 1.25, 1.42, 1.48, 1.50, 1.51, 1.51, 1.52])

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/microbial-dynamics/SKILL.md