Quick start guide and examples

Brief Introduction

One of the major functions of BST model kit is the modeling of the concentration of metabolites in a metabolic network as a dynamic S-system.
For example, we need to model a metabolic network that consists of a species set \(\mathcal{M}\) and a reaction set \(\mathcal{R}\). Then we can wirte a concentration balance equation aroound each member of the species set.

\[\frac{dX_{i}}{dt} = \sum_{r\in\mathcal{R}}\sigma_{i,r}\cdot\theta_{r}\prod_{m\in\mathcal{M}}X_{m}^{\gamma_{r,m}}-\mu{X}_{i}\qquad\forall{i}\in\mathcal{M}\]

\(\sigma_{i,r}\) denotes the stoichiometric coefficient speices \(i\) in reaction \(r\).
\(X_{m}\) denotes the concentration of species \(m\).
\(\gamma_{r,m}\) denotes the reaction order of species \(m\) in reaction \(r\).
\(\mu\) denotes the specific growth rate.

Case study

Setup environment

import BSTModelKit.jl

Build the model object from the model file

The BST model file hold information to build the model equation. But it does not contain any information about parameter values. We build the model file using the build method:

model_dictionary = build(joinpath(_PATH_TO_MODELS, "Feedback.bst"));

model_dictionary

model_dictionary["list_of_dynamic_species"]

G = model_dictionary["G"]

“Setting initial condition”

icv = [10.0, 0.1, 0.1, 1.1, 0.0];
model_dictionary["initial_condition_array"] = icv;

“Setting values of parameters”

model_dictionary["G"]

model_dictionary["total_species_list"]

model_dictionary["S"]

model_dictionary["α"] = [0.0, 10.0, 10.0, 10.0, 0.1, 0.1];

model_dictionary["static_factors_array"] = [0.1, 0.1, 0.1];

Set test values for the model parameters in the \(G\)-matrix and \(\alpha\)-vector

(T, X, Z) = evaluate(model_dictionary; tspan=(0.0,100.0), Δt = 0.1);

X

plot(T,X)