Parameter Estimation

Core functions


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

name2idx/Names of model parameters and species
ode.jlDifferential equation, parameters and initial condition
observalbe.jlModel observables for correlating simulation results with experimental observations
simulation.jlSimulation condition
experimental_data.jlExperimental measurements
search_param.jlLower and upper bounds of model parameters to be estimated
problem.jlAn 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.

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.

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)

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]))

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

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


$ mkdir errout
$ 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)