microbial-dynamics

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

uv pip install scipy numpy pandas matplotlib

For genome annotation (optional):

# conda install -c bioconda prokka

Quick Start

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])

popt, pcov = curve_fit(logistic, time, od600, p0=[0.02, 1.5, 0.5, 2.0], maxfev=10000)
print(f"y0={popt[0]:.4f}, K={popt[1]:.3f}, r={popt[2]:.3f} h^-1, lag={popt[3]:.2f} h")