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Introduction Version

FVA

TASK
Typing name :  TASK.gws_gena.FVA Brick :  gws_gena v

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

Output

Simulated digital twin
The simulated digital twin
FVA result table
The FVA result tables

Configuration

biomass_optimization

Optional

Biomass optimization

Type : stringAllowed values :   maximize  minimize 

fluxes_to_maximize

OptionalAdvanced parameter

The fluxes to maximize

Type : list

fluxes_to_minimize

OptionalAdvanced parameter

The fluxes to minimize

Type : list

solver

OptionalAdvanced parameter

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

Type : stringAllowed values : quad  highs-ds  highs-ipm  highs  interior-point  Default value : quad

relax_qssa

OptionalAdvanced parameter

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

Type : bool

qssa_relaxation_strength

OptionalAdvanced 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

OptionalAdvanced parameter

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

Type : float

number_of_simulations

OptionalAdvanced 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

OptionalAdvanced 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 : floatDefault value : 1