Generic consistency and nondegeneracy of vertically parametrized systems

This repository contains files for the manuscript Generic consistency and nondegeneracy of vertically parametrized systems (2304.02302) by Elisenda Feliu, Oskar Henriksson, and Beatriz Pascual-Escudero, as well as a forthcoming manuscript on applications to chemical reaction network theory.

Warning

Parts of the code is still experimental. Use at own risk!

File descriptions

The repository contains the following files:

• A Julia file julia/functions.jl that contains functions for testing whether a network admits positive nondegenerate steady states when modeled with (generalized) mass action kinetics.
• An analogous Maple file maple/functions.mpl.
• A directory results that contains the following files:
  • investigated_models.csv with all networks in ODEbase (as of November 2, 2023) with at least one reaction.
  • nondegenerate_networks.csv with all networks from investigated_models.csv that admit a positive nondegenerate steady state.
  • degenerate_networks.csv with all networks from investigated_models.csv that have a positive steady states, but all of them are degenerate.
  • generic_local_acr.csv with all networks from investigated_models.csv that satisfy the following criteria:
    • admits nondegenerate positive steady states
    • is not of full rank (after removing nonparticipating species)
    • has generic local ACR in at least one speceis.

Dependencies

The Julia portion of the code is based on Catalyst v14.4.1 and Oscar v1.1.1. For exact dependencies, see the file julia/Manifest.toml.

The Maple portion of the code was written for Maple 2023.

Julia example

We begin by loading the functions:

include("julia/functions.jl");

Consider the following isocitrate dehydrogenase that appears in Shinar–Feinberg's work on absolute concentration robustness, entered in catalyst format.

rn = @reaction_network begin 
    k1, X1 + X2 --> X3
    k2, X3 --> X1 + X2
    k3, X3 --> X1 + X4
    k4, X3 + X4 --> X5
    k5, X5 --> X3 + X4
    k6, X5 --> X2 + X3 
end;

The following command returns true, which means that the network admits positive steady states:

julia> is_consistent(rn)
true

The following command returns true, which means that there is a nondegenerate steady state with respect to its stoichiometric compatibility classes:

julia> has_nondegenerate_steady_state(rn, use_conservation_laws=true)
true

We check for generic local ACR with respect to the first and fourth species:

julia> generic_local_acr(rn, 1)
false

julia> generic_local_acr(rn, 4)
true

We could also do these checks on the level of the matrices that describe the associated augmented vertical system:

N = matrix(QQ, netstoichmat(rn))
B = matrix(ZZ, substoichmat(rn))
L = martix(QQ, conservationlaws(rn))

has_nondegenerate_zero(N, B, L)
generic_local_acr(N, B, 1)
generic_local_acr(N, B, 4)

Maple example

Suppose we want to investigate the properties of a network with the following stoichiometric matrix and reactant matrix (this corresponds to the network BIOMD0000000520 in ODEbase):

Gamma := Matrix([[-1, 0, 1, 0, 0, 0, 0], [0, 1, 0, -1, 0, 1, 0], [0, 0, 0, 0, 1, 0, -1]]);
B := Matrix([[1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1]]);

We begin by loading our Maple functions:

read("functions.mpl"):

The following command returns true, which means that the network admits positive steady states:

IsConsistent(Gamma)

The following command returns false, which means that all steady states are degenerate:

HasNondegenerateSteadyState(Gamma,B);

The following command returns false; with the notation from equation (4.2) in the paper, this means that all zeros of $f_\kappa$ are degenerate for all $\kappa$:

HasNondegenerateZero(Gamma,B);