Simulation

Basics

You can run any of the available simulation methods by importing it and applying it to the model.

> from reframed import FBA
> FBA(model)

Objective: 0.8739215069684306
Status: Optimal

Ideally you will want to store the solution object for further inspection.

> sol = FBA(model)
> sol.show_values()

R_ACONTa      6.00725
R_ACONTb      6.00725
R_AKGDH       5.06438
R_ATPM        8.39
R_ATPS4r      45.514
(...)

You can perform some basic queries on the solution values using regular expressions.

> sol.show_values(pattern="R_EX", sort=True)

R_EX_o2_e    -21.7995
R_EX_glc__D_e -10
R_EX_nh4_e   -4.76532
R_EX_pi_e    -3.2149
R_EX_h_e      17.5309
R_EX_co2_e    22.8098
R_EX_h2o_e    29.1758

All methods support additional constraints. These are temporary constraints used for simulation that will not be permanently stored in the model.

> from reframed import MOMA
> sol = MOMA(model, constraints={"R_PGI": 0, "R_EX_o2_e": (-15, 0)})

For simulating multiple knockouts you can use higher level methods:

> from reframed import gene_knockout
> sol = gene_knockout(model, ["G_b1723", "G_b3916"], constraints={"R_EX_o2_e: (-15, 0)"})

> from reframed import reaction_knockout
> sol = reaction_knockout(model, ["R_PGI", "R_PFK", "R_MDH"])

There are multiple methods for other tasks:

> from reframed import essential_genes
> essential = essential_genes(model)

> from reframed import blocked_reactions
> blocked = blocked_reactions(model)

Using Environments

The Environment class supports many helper functions to create environmental conditions for a model.

> from reframed import Environment
> Environment.empty(model)

R_EX_ac_e           0       inf
R_EX_acald_e        0       inf
R_EX_akg_e          0       inf
(...)

> Environment.complete(model, max_uptake=10)

R_EX_ac_e           -10     inf
R_EX_acald_e        -10     inf
R_EX_akg_e          -10     inf
(...)

> Environment.from_compounds(["glc", "o2", "nh4"])

R_EX_glc_e  -10.0   inf
R_EX_o2_e   -10.0   inf
R_EX_nh4_e  -10.0   inf

You can create an environment and use it as temporary simulation constraints:

> env = Environment.complete(model)
> sol = FBA(model, constraints=env)

Or you can apply the environment as permanent constraints to the model:

> Environment.complete(model, inplace=True)
> sol = FBA(model)