Parameter Estimation

Core functions

Model(`path_to_model`::String)

Load a BioMASS model. The model must include the following files:

Name	Content
`name2idx/`	Names of model parameters and species
`ode.jl`	Differential equation, parameters and initial condition
`observalbe.jl`	Model observables for correlating simulation results with experimental observations
`simulation.jl`	Simulation condition
`experimental_data.jl`	Experimental measurements
`search_param.jl`	Lower and upper bounds of model parameters to be estimated
`problem.jl`	An objective function to be minimized, i.e., the distance between model simulation and experimental data

Parameters
- path_to_model::String
  - The model folder to read.
Returns
- model::Model
  - The executable model in BioMASS.

Note

pasmopy.Text2Model allows you to build a BioMASS model from text [Imoto et al., 2022]. You simply describe biochemical reactions and the molecular mechanisms extracted from text are converted into an executable model. To build a model for BioMASS.jl, please set lang="julia".

scipy_differential_evolution(`model`::Model, `ix_id`::Int, `kwargs`...)

Estimate model parameters from experimental data.

Parameters
- model::Model
  - Model for parameter estimation.
- x_id::Int
  - Index of parameter set to estimate.
- kwargs...
  - Keyword arguments to pass to scipy.optimize.differential_evolution.

run_simulation(`model`::Model, `viz_type`::String, `show_all`::Bool=false, `stdev`::Bool=false)

Save simulation results with optimized parameter values.

Parameters
- viz_type::String
  - "average"
  - "best"
  - "original"
  - "experiment"
- show_all::Bool (default: false)
  - Whether to show all simulation results.
- stdev::Bool (default: false)
  - If True, the standard deviation of simulated values will be shown (only available for "average" visualization type).
- save_format::String (default: "pdf")
  - Either "png" or "pdf", indicating whether to save figures as png or pdf format.

Estimate unknown model parameters

using BioMASS

model = Model("./examples/fos_model");
initpop = generate_initial_population(model)
scipy_differential_evolution(model, 1, init=initpop)

Simultaneous parameter optimization

Using module `Distributed`

using Distributed
addprocs(); # add worker processes
@everywhere using BioMASS

@everywhere begin
    model = Model("./examples/fos_model")
    function optimize_parallel(i)
        scipy_differential_evolution(model, i)
    end
end

pmap(optimize_parallel, 1:10)

Calling multiple bash scripts

main.jl

using BioMASS

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

if abspath(PROGRAM_FILE) == @__FILE__
    scipy_differential_evolution(model, parse(Int64, ARGS[1]))
end

optimize_parallel.sh

#!/bin/sh

for i in $(seq 1 10); do
    nohup julia main.jl $i >> errout/$i.log  2>&1 &
done

# To terminate the process,
# $ pgrep -f main.jl | xargs kill -9

Run optimize_parallel.sh

$ mkdir errout
$ sh optimize_parallel.sh

How to track optimization process

The temporary result will be saved in path_to_model/fitparam/n/optimization.log.

$ tail examples/fos_model/fitparam/1/optimization.log

Visualization of simulation results

The simulation results will be saved in figure/.

run_simulation(model, viz_type="best", show_all=true)