FVA - gws_gena - Community

Typing name : TASK.gws_gena.FVA

Brick : gws_gena

Flux variability analysis

FVA class

Performs Flux Variability Analysis (FVA). It calculates the minimum and maximum flux values for each reaction in a metabolic network while satisfying certain constraints;

It is based on the paper of (Gudmundsson and Thiele, Bioinformatics 2010). See also:

Steinn Gudmundsson & Ines Thiele, Computationally efficient flux variability analysis, BMC Bioinformatics, volume 11, Article number: 489 (2010), https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-489

Input

Digital twin

The digital twin to analyze

Twin

Output

Simulated digital twin

The simulated digital twin

Twin

FVA result table

The FVA result tables

FVA result

Configuration

biomass_optimization

Optional

Biomass optimization

Type : string

fluxes_to_maximize

Optional

Advanced parameter

The fluxes to maximize

Type : list

fluxes_to_minimize

Optional

Advanced parameter

The fluxes to minimize

Type : list

solver

Optional

Advanced parameter

The optimization solver. It is recommended to use `quad`. Other solvers are in `beta` versions.

Type : string

Default value : quad

relax_qssa

Optional

Advanced parameter

True to relaxing the quasi-steady state assumption (QSSA) constrain (`quad` solver is used). False otherwise.

Type : bool

qssa_relaxation_strength

Optional

Advanced parameter

Used only if the QSSA is relaxed. The higher is the strength, the stronger is the QSSA. Hint: Set to the number of reactions to have strong QSSA contrain.

Type : float

parsimony_strength

Optional

Advanced parameter

Set True to perform parsimonious FBA (pFBA). In this case the quad solver is used. Set False otherwise

Type : float

number_of_simulations

Optional

Advanced parameter

Set the number of simulations to perform. You must provide at least the same number of measures in the context. By default, keeps all simulations.

Type : int

gamma

Optional

Advanced parameter

γ determines whether the analysis is conducted with respect to suboptimal network states (where 0 ≤ γ < 1) or to the optimal state (where γ = 1). A value of 0.9 implies that the objective must be at least 90% of its maximum.

Type : float

Default value : 1