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tooluniverse-computational-biophysics

Solve quantitative biophysics problems across pharmacokinetics, epidemiology, toxicology, and enzyme kinetics.

1.7k|254|Updated Mar 3, 2025
Search Tags:#calculation#epidemiology#quantitative biology#biophysics#toxicology#pharmacokinetics#enzyme kinetics
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-computational-biophysics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-computational-biophysics
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-computational-biophysics
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-computational-biophysics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Quantitative biology problems—drug dosing, epidemic thresholds, environmental risk, enzyme kinetics—require multi-step calculations where mental arithmetic introduces errors and unit mismatches cause silent failures. This Skill provides a structured reasoning framework plus verified Python scripts to compute accurate answers from first principles.

Core Features & Use Cases

  • Domain Reasoning Patterns: Maps problems to physical processes (exponential decay, saturation/binding, conservation/dilution, threshold crossover) covering pharmacokinetics, epidemiology, population genetics, and toxicology.
  • Bundled Calculation Scripts: Ready-to-run Python scripts for epidemiology (R0, herd immunity, NNT, diagnostic tests, Bayesian post-test probability), burn resuscitation fluids (Parkland/Brooke/Galveston), enzyme kinetics (Km/Vmax, Hill, Ki), IV drip rates, dilutions, and environmental hazard quotients.
  • ToolUniverse Integration: Looks up real-world values (molecular weight, Kd, allele frequencies, PK properties) via PubChem, ChEMBL, BindingDB, and gnomAD tools when a specific drug, compound, or gene is named.
  • Use Case: A clinician asks for the burn resuscitation fluid rate for a 25 kg child with 45% TBSA burns—run burn_fluids.py to get hourly rates for the first 8h and next 16h with verification steps.

Quick Start

Ask the AI to calculate the herd immunity threshold and required vaccination coverage for a disease with R0 of 3.5 and vaccine efficacy of 90%.

Frequently Asked Questions about tooluniverse-computational-biophysics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate herd immunity threshold and vaccination coverage?▼

Run epidemiology.py with --type r0_herd, providing --R0 and --VE values. It computes the herd immunity threshold (1 - 1/R0), the VE-adjusted minimum vaccination coverage, and optionally the effective reproduction number at a given coverage level.

How to determine Km and Vmax from enzyme kinetics data?▼

Use enzyme_kinetics.py with --type km_vmax, passing comma-separated substrate concentrations and velocities. It applies both Lineweaver-Burk linear regression and nonlinear grid-search fitting, reporting R-squared and predicted versus observed values.

Does the burn fluid calculator support pediatric patients?▼

Yes, burn_fluids.py supports pediatric resuscitation via the Galveston formula (BSA-based) and modified Brooke with 4-2-1 maintenance fluids. Provide --age_years and --bsa_m2 for pediatric mode; patients under 30 kg are auto-detected.

What Python packages are required to run the calculation scripts?▼

The bundled scripts use only the Python standard library (argparse, math, sys), so no external packages are needed. Optional scipy usage is suggested in SKILL.md templates for curve fitting and statistical tests but is not required by the scripts.

When should I look up data instead of computing directly?▼

Look up values via ToolUniverse tools when a specific drug, compound, gene, or variant is named—use PubChem for molecular weight and log Kow, ChEMBL for PK properties, BindingDB for Kd/Ki, and gnomAD for allele frequencies. Compute directly when the problem provides all numbers.

Why do multi-step dose calculations give wrong answers when done mentally?▼

Mental arithmetic on multi-step problems introduces rounding and unit-conversion errors, such as mixing mg with grams or hours with minutes. The Skill mandates writing and executing Python code with unit tracking and substituting the answer back into the original problem for verification.

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