Kinetics Kalculator

Kinetics Kalculator is a Python package designed for analyzing biochemical kinetics data. It provides tools for loading experimental data, calculating reaction rates, adjusting for background activity, and visualizing results using various plots. This package is particularly useful for experiments involving enzyme kinetics and Michaelis-Menten analysis.

For the sphinx-compiled documentation, see here.

Installation

You can install Kinetics Kalculator from PyPI using pip:

pip install kinetics-kalculator

Alternatively, you can install it in editable mode if you're working with the source code:

git clone https://github.com/yourusername/kinetics-kalculator.git
cd kinetics-kalculator
pip install -e .

Usage

Below is a an illustrative example of how to use KineticsKalculator. This example demonstrates how to load data, label conditions, convert measurements, calculate rates, and visualize results. For more details and a worked example, see the examples folder.

Example Workflow

import pandas as pd
from pathlib import Path
from kinetics_kalculator.kinetics_kalculator import KineticsKalculator

# Define paths to your data files
kinetics_data_path = Path("examples/EX_kinetics_data.csv")
standard_curves_path = Path("examples/EX_standard_curves.json")

# Load standard curves into a dictionary
all_standard_curves = pd.read_json(standard_curves_path, typ="series").to_dict()
experiment_specific_standard_curve = all_standard_curves[21062324]["mscarlett"]

# Initialize the KineticsKalculator
kalculator = KineticsKalculator(
    data_path=kinetics_data_path,
    standard_curve_parameters=experiment_specific_standard_curve,
)

# Label replicates and conditions
kalculator.label_replicates_and_conditions(
    condition_columns=["substrate_concentration", "enzyme_concentration"]
)

# Convert "value" column to concentration using the standard curve
kalculator.convert_values_to_concentration_with_standard_curve()

# Visualize concentration vs. time for different conditions
kalculator.plot_concentration_vs_time_for_each_condition()

# Subset data to a specific time range
kalculator.subset_dataframe_to_time_range(100, 400)

# Calculate rates of change
kalculator.calculate_rates(
    time_column="time",
    value_column="value",
    group_by_columns=["condition"],
    print_fit_summary=True,
)

# Adjust rates for background activity
kalculator.adjust_rates_for_background(
    rate_column="rate",
    sample_type_column="sample_type",
    negative_control="negative_control",
)

# Plot Michaelis-Menten curve
kalculator.plot_michaelis_menten_curve()

Output graph examples:

Data Dictionary

The KineticsKalculator relies on a dataframe to perform calculations. The required columns are below; any of the "generated" columns may also be pre-populated.

Input DataFrame (Required)

Additional Columns (Generated)