biomass-dev/biomass.jl

Julia interface to BioMASS

The BioMASS module for Julia

This module provides a Julia interface to the BioMASS parameter estimation.

Installation

The package is a registered package, and can be installed with Pkg.add.

julia> using Pkg; Pkg.add("BioMASS")

or through the pkg REPL mode by typing

] add BioMASS

Python package requirements:

• numpy - https://numpy.org
• scipy - https://scipy.org
• matplotlib - https://matplotlib.org

Example

Model development

This example shows you how to build a simple Michaelis-Menten two-step enzyme catalysis model.

E + S ⇄ ES → E + P

pasmopy.Text2Model allows you to build a BioMASS model from text. You simply describe biochemical reactions and the molecular mechanisms extracted from text are converted into an executable model.

Prepare a text file describing the biochemical reactions (e.g., michaelis_menten.txt)

E + S ⇄ ES | kf=0.003, kr=0.001 | E=100, S=50
ES → E + P | kf=0.002

@obs Substrate: u[S]
@obs E_free: u[E]
@obs E_total: u[E] + u[ES]
@obs Product: u[P]
@obs Complex: u[ES]

@sim tspan: [0, 100]

Convert the text into an executable model

$ python  # pasmopy requires Python 3.7+

>>> from pasmopy import Text2Model
>>> description = Text2Model("michaelis_menten.txt", lang="julia")
>>> description.convert()  # generate 'michaelis_menten_jl/'

Simulate the model using BioMASS.jl

$ julia

using BioMASS

model = Model("./michaelis_menten_jl");
run_simulation(model)

Parameter estimation

using BioMASS

model = Model("./examples/fos_model");

# Estimate unknown model parameters from experimental observations
scipy_differential_evolution(model, 1)  # requires scipy package

# Save simulation results to figure/ in the model folder
run_simulation(model, viz_type="best", show_all=true)

References

• 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

• Imoto, H., Yamashiro, S. & Okada, M. A text-based computational framework for patient -specific modeling for classification of cancers. iScience 25, 103944 (2022). https://doi.org/10.1016/j.isci.2022.103944

License

MIT

biomass.jl's Issues

Drop GA parameter estimation

From the next stable release, I will drop parameter estimation method with GA (genetic algorithm).
For parameter estimation, use scipy_differential_evolution function.

Register

@JuliaRegistrator register()

Bifurcation analysis

The document regarding bifurcation analysis is not user-friendly and therefore needs to be updated.

TagBot trigger issue

This issue is used to trigger TagBot; feel free to unsubscribe.

If you haven't already, you should update your TagBot.yml to include issue comment triggers.
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If you'd like for me to do this for you, comment TagBot fix on this issue.
I'll open a PR within a few hours, please be patient!

Move constants in `continuation.jl` to arguments of new_curve! function

• Turn this:

function new_curve!(
    model_path::Union{String,SubString{String}},
    p::Vector{Float64},
    diffeq::Function,
    get_derivatives::Function,
    get_steady_state::Function;
    direction::Bool=false,
    bifparam::Int,
    n_state::Int,
    n_param::Int=1,
    n_variable::Int=n_state + 1,
    dim_newton::Int=n_state)

into this:

function new_curve!(
    model_path::Union{String,SubString{String}},
    p::Vector{Float64},
    diffeq::Function,
    get_derivatives::Function,
    get_steady_state::Function;
    direction::Bool=false,
    bifparam::Int,
    n_state::Int,
    n_param::Int=1,
    n_variable::Int=n_state + 1,
    dim_newton::Int=n_state;
    MC = 100000,
    IVAL = 1e-2,
    RATE = 1e-3,
    NEPS = 1e-12)