BioMASS

Mathematical modeling is a powerful method for the analysis of complex biological systems. Although there are many researches devoted on producing models to describe dynamical cellular signaling systems, most of these models are limited and do not cover multiple pathways. Therefore, there is a challenge to combine these models to enable understanding at a larger scale. Nevertheless, larger network means that it gets more difficult to estimate parameters to reproduce dynamic experimental data needed for deeper understanding of a system.

To overcome this problem, we developed BioMASS, a Python framework for Modeling and Analysis of Signaling Systems. The BioMASS framework allows efficient optimization of multiple parameter sets simultaneously and generates the multiple parameter candidates that explain the signaling dynamics of interest. These parameter candidates can be further evaluated by their distribution and sensitivity analysis as a part of alternative information about the hidden regulatory mechanism of the system.

Features

Parameter estimation of ODE models
Local sensitivity analysis
Effective visualization of simulation results

Installation

The BioMASS library is available on PyPI.

$ pip install biomass

BioMASS supports Python 3.7 or newer.

Example

Parameter estimation

from biomass import Model, optimize
from biomass.models import Nakakuki_Cell_2010

model = Model(Nakakuki_Cell_2010.__package__).create()

optimize(model, start=1, end=10)

estimated_parameter_sets

from biomass import run_simulation

run_simulation(model, viz_type="average")

simulation_average Points (blue diamonds, EGF; red squares, HRG) denote experimental data, solid lines denote simulations.

Sensitivity analysis

from biomass import run_analysis

run_analysis(model, target='reaction', metric='integral')

sensitivity_PcFos Control coefficients for integrated pc-Fos are shown by bars (blue, EGF; red, HRG). Numbers above bars indicate the reaction indices, and error bars correspond to simulation standard deviation.

Author

Hiroaki Imoto

License

Apache License 2.0

Metadata

Release files for biomass 0.4.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for biomass 0.4.1
File	Size	Uploaded
biomass-0.4.1.tar.gz	72.6 kB	Jun 1, 2021	Details

Built distribution (wheel)

Table of built distributions (wheels) for biomass 0.4.1
File	Interpreter	ABI	Platform	Reset
biomass-0.4.1-py3-none-any.whl 96.7 kB Jun 1, 2021	Python 3	none	any	Details

Total release size: 169.3 kB

Release files / biomass-0.4.1.tar.gz

Download URL	biomass-0.4.1.tar.gz
Size	72.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`47e8c167079dfbdc313d4af96a44057b4e2fa1fe94261d586dacd70be14cdd12`
BLAKE2b-256 checksum How to use checksums	`723c0232bd0c667498d9cbbda863f73304285bec4290dd99e1458b9f036bb575`
Upload date	Jun 1, 2021
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Uploaded via	`twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10`

Release files / biomass-0.4.1-py3-none-any.whl

Download URL	biomass-0.4.1-py3-none-any.whl
Size	96.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c04b5190fc8c8d1efb95c6f2e9650cc31449df1db1a80de19c5d7911dbc63ff6`
BLAKE2b-256 checksum How to use checksums	`ebd188b7f30a1615f8134a899fca18059d4e8734401bb813a8d8cf8a8e5c43ce`
Upload date	Jun 1, 2021
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.4.1 importlib_metadata/4.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.8.10`