Metadata-Version: 2.1
Name: biomass
Version: 0.3.3
Summary: A Python Framework for Modeling and Analysis of Signaling Systems
Home-page: https://github.com/okadalabipr/biomass
Author: Hiroaki Imoto
Author-email: himoto@protein.osaka-u.ac.jp
License: MIT
Keywords: systems,biology,modeling,optimization,sensitivity,analysis
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy (>=1.2.0)
Requires-Dist: seaborn
Provides-Extra: dev
Requires-Dist: black (==20.8b1) ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

# BioMASS

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## **M**odeling and **A**nalysis of **S**ignaling **S**ystems

<img align="left" src="https://raw.githubusercontent.com/okadalabipr/biomass/master/resources/images/logo.png" width="300">

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 modeling platform tailored to optimizing mathematical models of biological processes. By using BioMASS, users can efficiently optimize kinetic parameters to fit user-defined models to experimental data, while performing analysis on reaction networks to predict critical components affecting cellular output.

## Features

- parameter estimation of ODE models
- sensitivity analysis
- effective visualization of simulation results

## Installation

The BioMASS library is available on [PyPI](https://pypi.org/project/biomass/).

```
$ pip3 install biomass
```

BioMASS supports Python 3.7 or newer.

## Model Construction

```python
from biomass.models import Nakakuki_Cell_2010

Nakakuki_Cell_2010.show_info()
```

```
Nakakuki_Cell_2010 information
------------------------------
36 species
115 parameters, of which 75 to be estimated
```

```python
model = Nakakuki_Cell_2010.create()
```

## Parameter Estimation of ODE Models (_n_ = 1, 2, 3, · · ·)

Parameters are adjusted to minimize the distance between model simulation and experimental data.

```python
from biomass import optimize

optimize(
    model=model, start=1, options={
        "popsize": 3,
        "max_generation": 1000,
        "allowable_error": 0.5,
        "local_search_method": "DE",
    }
)
```

The temporary result will be saved in `out/n/` after each iteration.

Progress list: `out/n/optimization.log`

```
Generation1: Best Fitness = 1.726069e+00
Generation2: Best Fitness = 1.726069e+00
Generation3: Best Fitness = 1.726069e+00
Generation4: Best Fitness = 1.645414e+00
Generation5: Best Fitness = 1.645414e+00
Generation6: Best Fitness = 1.645414e+00
Generation7: Best Fitness = 1.645414e+00
Generation8: Best Fitness = 1.645414e+00
Generation9: Best Fitness = 1.645414e+00
Generation10: Best Fitness = 1.645414e+00
Generation11: Best Fitness = 1.645414e+00
Generation12: Best Fitness = 1.645414e+00
Generation13: Best Fitness = 1.645414e+00
Generation14: Best Fitness = 1.645414e+00
Generation15: Best Fitness = 1.645414e+00
Generation16: Best Fitness = 1.249036e+00
Generation17: Best Fitness = 1.171606e+00
Generation18: Best Fitness = 1.171606e+00
Generation19: Best Fitness = 1.171606e+00
Generation20: Best Fitness = 1.171606e+00
```

- If you want to continue from where you stopped in the last parameter search,

```python
from biomass import optimize_continue

optimize_continue(
    model=model, start=1, options={
        "popsize": 3,
        "max_generation": 1000,
        "allowable_error": 0.5,
        "local_search_method": "DE",
    }
)
```

- If you want to search multiple parameter sets (e.g., from 1 to 10) simultaneously,

```python
from biomass import optimize

optimize(
    model=model, start=1, end=10, options={
        "popsize": 5,
        "max_generation": 2000,
        "allowable_error": 0.5,
        "local_search_method": "mutation",
        "n_children": 50
    }
)
```

- Exporting optimized parameters in CSV format

```python
from biomass.result import OptimizationResults

res = OptimizationResults(model)
res.to_csv()
```

## Visualization of Simulation Results

```python
from biomass import run_simulation

run_simulation(model, viz_type='average', show_all=False, stdev=True)
```

![simulation_average](https://raw.githubusercontent.com/okadalabipr/biomass/master/resources/images/simulation_average.png)

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

## Sensitivity Analysis

The single parameter sensitivity of each reaction is defined by<br>

_s<sub>i</sub>_(_q_(**v**),_v<sub>i</sub>_) = _∂_ ln(_q_(**v**)) / _∂_ ln(_v<sub>i</sub>_) = _∂_ _q_(**v**) / _∂_ _v<sub>i</sub>_ · _v<sub>i</sub>_ / _q_(**v**)

where _v<sub>i</sub>_ is the _i_<sup>th</sup> reaction rate, **v** is reaction vector **v** = (_v<sub>1</sub>_, _v<sub>2</sub>_, ...) and _q_(**v**) is a target function, e.g., time-integrated response, duration. Sensitivity coefficients were calculated using finite difference approximations with 1% changes in the reaction rates.

```python
from biomass import run_analysis

run_analysis(model, target='reaction', metric='integral', style='barplot')
```

![sensitivity_PcFos](https://raw.githubusercontent.com/okadalabipr/biomass/master/resources/images/sensitivity_PcFos.png)

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.

## Citation

When using BioMASS, please cite:

- Imoto, H., Zhang, S. & Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. _Cancers_. **12**, 2878 (2020). https://doi.org/10.3390/cancers12102878

## Author

[Hiroaki Imoto](https://github.com/himoto)


