goo.gene

class goo.gene.Circuit

Bases: object

class goo.gene.CircuitEngine

Bases: object

copy() → CircuitEngine

Return copy of engine.

Return type:

CircuitEngine

load_circuits(*circuits: Circuit)

Load engine with model network.

Parameters:

circuits (Circuit)

retrieve_concs() → dict[Gene, float]

Get current metabolite concentrations.

Return type:

dict[Gene, float]

step(metabolite_concs: dict, dt: float)

Take 1 frame step of time dt to calculate new metabolite concentrations.

Parameters:

• metabolite_concs (dict)

• dt (float)

class goo.gene.DegFirstOrder(x: goo.gene.Gene, k: float)

Bases: Circuit

Parameters:

• x (Gene)

• k (float)

k: float

d[x]/dt = -k[x]

x: Gene

class goo.gene.Gene(name: str)

Bases: object

Gene class to represent a gene in the gene regulatory network.

Parameters:

name (str) – Name of the gene.

class goo.gene.GeneRegulatoryNetwork(gene_concs: dict = {}, circuit_engine: CircuitEngine = None)

Bases: object

Gene regulatory network to simulate gene expression.

Parameters:

• concs – Initial concentrations of genes.

• circuit_engine (CircuitEngine) – Engine to simulate gene regulatory networks

• gene_concs (dict)

copy() → GeneRegulatoryNetwork

Return type:

GeneRegulatoryNetwork

load_circuits(*circuits: Circuit)

Load engine with model network.

Parameters:

circuits (Circuit)

update_concs(center: Vector = None, radius: float | None = None, dt=1)

Update network concentrations based on underlying diffusion systems, # cell center and radius.

Parameters:

• center (Vector)

• radius (float | None)

update_gene_concs(iter=5, dt=1)

Update network concentrations based on the model.

class goo.gene.ProdActivation(y: Gene, x: Gene, kcat: float, Km: float = 1, n: float = 2, s: Gene = None, a0: float = 0)

Bases: Circuit

d[y]/dt = kcat * [x]**n / (Km + [x]**n), optional substrate s consumed, and optional leaky factor a0.

Parameters:

• y (Gene)

• x (Gene)

• kcat (float)

• Km (float)

• n (float)

• s (Gene)

• a0 (float)

Km: float = 1

a0: float = 0

kcat: float

n: float = 2

s: Gene = None

x: Gene

y: Gene

class goo.gene.ProdAnd(z: Gene, x: Gene, y: Gene, k: float, nx: float = 2, ny: float = 2, s: Gene = None, a0: float = 0)

Bases: Circuit

d[z]/dt = k([x] AND [y]), optional substrate s consumed, and optional leaky factor a0.

Parameters:

• z (Gene)

• x (Gene)

• y (Gene)

• k (float)

• nx (float)

• ny (float)

• s (Gene)

• a0 (float)

a0: float = 0

k: float

nx: float = 2

ny: float = 2

s: Gene = None

x: Gene

y: Gene

z: Gene

class goo.gene.ProdRepression(y: Gene, x: Gene, kcat: float, Km: float = 1, n: float = 2, s: Gene = None, a0: float = 0)

Bases: Circuit

d[y]/dt = kcat / (Km + [x]**n), optional substrate s consumed, and optional leaky factor a0.

Parameters:

• y (Gene)

• x (Gene)

• kcat (float)

• Km (float)

• n (float)

• s (Gene)

• a0 (float)

Km: float = 1

a0: float = 0

kcat: float

n: float = 2

s: Gene = None

x: Gene

y: Gene

class goo.gene.RoadRunnerEngine

Bases: CircuitEngine

Engine to simulate gene regulatory networks using RoadRunner.

Parameters:

• model – Model of the gene regulatory network.

• result – Result of the simulation.

copy()

Return copy of engine.

load_circuits(*circuits: Circuit)

Load engine with model network.

Parameters:

circuits (Circuit)

load_sbml(sbml)

retrieve_concs()

Get current gene concentrations.

step(metabolite_concs: dict[Gene, float], iter: int = 5, dt: float = 1) → None

Calculates new gene concentrations.

Parameters:

• metabolite_concs (dict[Gene, float])

• iter (int)

• dt (float)

Return type:

None