Metadata-Version: 2.1
Name: biomass
Version: 0.3.4
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
Download-URL: https://pypi.org/project/biomass/
Description: # BioMASS
        
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        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 **M**odeling and **A**nalysis of **S**ignaling **S**ystems. 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](https://pypi.org/project/biomass/).
        
        ```
        $ pip3 install biomass
        ```
        
        BioMASS supports Python 3.7 or newer.
        
        ## Example
        
        - [Circadian clock](biomass/models/circadian_clock/README.md)
        - [MAPK cascade](biomass/models/mapk_cascade/README.md)
        - [Immediate-early gene response](biomass/models/Nakakuki_Cell_2010/README.md)
        - [NF-κB pathway](biomass/models/nfkb_pathway/README.md)
        - [TGF-β/SMAD pathway](biomass/models/tgfb_smad/README.md)
        
        We will use the model of immediate-early gene response ([Nakakuki_Cell_2010](biomass/models/Nakakuki_Cell_2010)) for parameter estimation, visualization of simulation results and sensitivity analysis.
        
        ### 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 = 5.864228e+00
        Generation2: Best Fitness = 5.864228e+00
        Generation3: Best Fitness = 4.488934e+00
        Generation4: Best Fitness = 3.793744e+00
        Generation5: Best Fitness = 3.652047e+00
        Generation6: Best Fitness = 3.652047e+00
        Generation7: Best Fitness = 3.652047e+00
        Generation8: Best Fitness = 3.452999e+00
        Generation9: Best Fitness = 3.180878e+00
        Generation10: Best Fitness = 1.392501e+00
        Generation11: Best Fitness = 1.392501e+00
        Generation12: Best Fitness = 1.392501e+00
        Generation13: Best Fitness = 1.392501e+00
        Generation14: Best Fitness = 7.018051e-01
        Generation15: Best Fitness = 7.018051e-01
        Generation16: Best Fitness = 7.018051e-01
        Generation17: Best Fitness = 7.018051e-01
        Generation18: Best Fitness = 7.018051e-01
        Generation19: Best Fitness = 6.862063e-01
        Generation20: Best Fitness = 6.862063e-01
        ```
        
        - 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
        
          ```
          @article{imoto2020computational,
            title={A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway},
            author={Imoto, Hiroaki and Zhang, Suxiang and Okada, Mariko},
            journal={Cancers},
            volume={12},
            number={10},
            pages={2878},
            year={2020},
            publisher={Multidisciplinary Digital Publishing Institute}
          }
          ```
        
        ## Author
        
        [Hiroaki Imoto](https://github.com/himoto)
        
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
Provides-Extra: dev
