Welcome to LOICA’s documentation!

Introduction

Welcome to the LOICA (Logical Operators for Integrated Cell Algorithms) repository, our Python package for designing, modeling and characterizing genetic networks.

As you may have noticed, our logo features a beautiful bird—loica (Leistes loyca); a bird native to Chile known for its particular red chest and legendary kindness, with which we share name.

Installation

Installing LOICA is way easier than pronuncing it!

pip install loica

For more details please refer to our Wiki for installation instructions and developer guides.

LOICA allows you to:

• Compile Code into DNA fragments that execute Cell Algorithms

• Easy programation of genetic network models

• Generation of synthetic data

• Communicate with Flapjack

• Use and output SBOL files

• Use all sorts of cellular computation

• Easy, fluid and customisable DNA design

Tutorials

Now that you have LOICA installed you can familiarize yourself with the tool using the Jupyter notebook tutorials designed for this purpose.

API Reference

This page contains auto-generated API reference documentation 1.

loica

Subpackages

loica.operators

Submodules

loica.operators.hill1

Module Contents

Classes

Hill1

A class that represents a DNA fragment that encode a genetic operator.

class Hill1(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill1 Operator is an abstraction of a repressible or inducible promoter that maps an input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

expression_rate(self, t, dt)

forward_model(self, a_j, b_j, n_i=2, K_i=1, a_A=100.0, b_A=0, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=0, odval=[1] * 100, gamma=0, p0_1=0, p0_2=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, gamma)

loica.operators.hill2

Module Contents

Classes

Hill2

A class that represents a DNA fragment that encode a genetic operator.

class Hill2(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='orange')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill2 Operator is an abstraction of a set of two repressible or inducible promoters that maps an 2 inputs into an output using a Hill function.

…

inputList [Regulator | Supplement]

The inputs of the operator that regulates the expression of the output

outputRegulator | Reporter | List

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver1, receiver2, chemical1, chemical2, nor_inverter, media, strain, signal, biomass_signal, gamma, lower_bounds=[0] * 8, upper_bounds=[100000000.0, 8, 100000000.0, 8, 100000000.0, 100000000.0, 100000000.0, 100000000.0], init_x=[1, 2, 1, 2, 1, 0, 0, 0])

expression_rate(self, t, dt)

forward_model(self, rep1_K=1, rep1_n=2, rep2_K=1, rep2_n=2, alpha0=1, alpha1=0, alpha2=0, alpha3=0, a_A=100.0, b_A=0, K_A=1, n_A=2, a_B=100.0, b_B=0, K_B=1, n_B=2, Dt=0.05, sim_steps=10, A=0, B=0, odval=[1] * 100, gamma=0, rep1_0=0, rep2_0=0, fp_0=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, a_B, b_B, K_B, n_B, chem1, chem2, gamma)

loica.operators.operator

Module Contents

Classes

Operator

A class that represents a DNA fragment that encode a genetic operator.

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

loica.operators.receiver

Module Contents

Classes

Receiver

A class that represents a DNA fragment that encode a genetic operator.

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

loica.operators.source

Module Contents

Classes

Source

A class that represents a DNA fragment that encode a genetic operator.

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

Package Contents

Classes

Hill1	A class that represents a DNA fragment that encode a genetic operator.
Hill2	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Receiver	A class that represents a DNA fragment that encode a genetic operator.
Receiver	A class that represents a DNA fragment that encode a genetic operator.
Receiver	A class that represents a DNA fragment that encode a genetic operator.
Source	A class that represents a DNA fragment that encode a genetic operator.
Source	A class that represents a DNA fragment that encode a genetic operator.
Source	A class that represents a DNA fragment that encode a genetic operator.
Source	A class that represents a DNA fragment that encode a genetic operator.

class Hill1(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill1 Operator is an abstraction of a repressible or inducible promoter that maps an input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

expression_rate(self, t, dt)

forward_model(self, a_j, b_j, n_i=2, K_i=1, a_A=100.0, b_A=0, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=0, odval=[1] * 100, gamma=0, p0_1=0, p0_2=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, gamma)

class Hill2(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='orange')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill2 Operator is an abstraction of a set of two repressible or inducible promoters that maps an 2 inputs into an output using a Hill function.

…

inputList [Regulator | Supplement]

The inputs of the operator that regulates the expression of the output

outputRegulator | Reporter | List

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver1, receiver2, chemical1, chemical2, nor_inverter, media, strain, signal, biomass_signal, gamma, lower_bounds=[0] * 8, upper_bounds=[100000000.0, 8, 100000000.0, 8, 100000000.0, 100000000.0, 100000000.0, 100000000.0], init_x=[1, 2, 1, 2, 1, 0, 0, 0])

expression_rate(self, t, dt)

forward_model(self, rep1_K=1, rep1_n=2, rep2_K=1, rep2_n=2, alpha0=1, alpha1=0, alpha2=0, alpha3=0, a_A=100.0, b_A=0, K_A=1, n_A=2, a_B=100.0, b_B=0, K_B=1, n_B=2, Dt=0.05, sim_steps=10, A=0, B=0, odval=[1] * 100, gamma=0, rep1_0=0, rep2_0=0, fp_0=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, a_B, b_B, K_B, n_B, chem1, chem2, gamma)

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

Submodules

loica.assay

Module Contents

Classes

Assay

Assay measures a set of samples in parallel at a set of timepoints.

class Assay(samples, n_measurements, interval, name='Loica assay', description='', biomass_signal_id=None)

Assay measures a set of samples in parallel at a set of timepoints. Connects to flapjack to generate data, and to fit parameters to data.

…

samplesList[Sample]

List of Samples that belongs to the Assay

n_measurementsint

Number of measurements to take

intervalint

Time in hours between each measurements

namestr

Name of the Assay

description: str

Descriptioin of the Assay

biomass_signal_idint

Flapjack ID of the Assay that is associated with the Assay

run(substeps=10, nsr=0, biomass_bg=0, fluo_bg=0)

Runs the Assay time series

upload(flapjack, study)

Upload the data produced by running the Assay to Flapjack into the Study

Assay measures a set of samples in parallel at a set of timepoints Connects to flapjack to generate data, and to fit parameters to data

run(self, substeps=10, nsr=0, biomass_bg=0, fluo_bg=0, stochastic=False)

Run the assay measuring at specified time points, with simulation time step dt

upload(self, flapjack, study)

loica.colony

Module Contents

Classes

Colony

class Colony(circuit=None, r0=1, mu0=1)

fun(self, x)

kymograph(self, nx, t0, tmax)

map_kymo(self, kymo)

norm_kymo(self, kymo)

loica.geneproduct

Module Contents

Classes

GeneProduct	A class that represents a gene product, protein or RNA.
Regulator	Representation of a regulatory gene product.
Reporter	Representation of a regulatory gene product.

class GeneProduct(name, init_concentration=0, degradation_rate=0, uri=None, sbol_comp=None, type_='PRO', color='silver')

A class that represents a gene product, protein or RNA.

…

namestr

Name of the gene product

init_concentrationint | float

Initial concentration of the gene product in Molar

degradation_rateint | float

Degradation rate of the gene product

type_str, optional

Molecular type of the gene product, could be ‘PRO’ or ‘RNA’

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

shape = ^

__str__(self)

Return str(self).

express(self, rate)

initialize(self)

step(self, growth_rate, dt)

class Regulator(name, init_concentration=0, degradation_rate=0, sbol_comp=None, color='lightgreen')

Bases: GeneProduct

Representation of a regulatory gene product. Child of GeneProduct.

class Reporter(name, init_concentration=0, degradation_rate=0, signal_id=None, color='w', sbol_comp=None)

Bases: GeneProduct

Representation of a regulatory gene product.

signal_idstr, optional

Flapjack ID of the signal that the reporter is associated with.

colorstr, optional

Color of the reporter

loica.genetic_network

Module Contents

Classes

GeneticNetwork

Representation of a genetic netowrk composed by a set of Operators, Regulators and Reporters.

class GeneticNetwork(vector=None)

Representation of a genetic netowrk composed by a set of Operators, Regulators and Reporters.

…

operatorsList[Operator]

List of Operators that are part of the genetic network

regulatorsList[Regulator]

List of Regulators that are part of the genetic network

reportersList[Reporter]

List of Reporters that are part of the genetic network

vectorint

Flapjack ID of the vector that is associated with the genetic network

to_graph()

Builds a graph representation of the genetic netwok

draw()

Generates a plot of the graph representation builded by to_graph()

to_sbol(sbol_doc=None)

Generates a SBOL3 Document representation of the genetic network on sbol_doc

add_operator(self, ops)

add_regulator(self, regs)

add_reporter(self, reps)

draw(self, node_shape='o', node_size=500, linewidths=0, alpha=0.5, arrowsize=10, font_size=6, font_family='Tahoma', font_weight='bold', pos=nx.kamada_kawai_layout, contracted=False)

initialize(self)

step(self, growth_rate=1, t=0, dt=0.1)

step_stochastic(self, growth_rate=1, t=0, dt=0.1)

substep_stochastic(self, t=0, dt=0.1, growth_rate=1)

to_contracted_graph(self)

to_graph(self)

to_sbol(self, sbol_doc: sbol3.Document = None) → sbol3.Document

Convert the genetic network to SBOL. :param sbol_doc: The SBOL document to add the genetic network to.

loica.metabolism

Module Contents

Classes

DataMetabolism	Characterized context for gene expression, incorporates biomass and growth rate.
Metabolism	Context for gene expression, incorporates biomass and growth rate.
SimulatedMetabolism	Simulated context for gene expression, incorporates biomass and growth rate.

Functions

gompertz(t, y0, ymax, um, l)
gompertz_growth_rate(t, y0, ymax, um, l)
ramp_biomass(t, od0, start, slope)
ramp_growth_rate(t, start, slope)
step_biomass(t, od0, start)
step_growth_rate(t, start)

class DataMetabolism(name, fj, media, strain, vector, biomass_signal)

Bases: Metabolism

Characterized context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

fjFlapjack

Flapjack instance used to fetch data from

mediastr

Name of the media to query

strainstr

Name of the strain to query

vectorstr

Name of the vector to query

biomass_signalstr

Name of signal to query and use as biomass

biomass(t)

Return biomass at a given time from characterization data

growth:rate(t)

Return growth rate at a given time from characterization data

biomass(self, t)

growth_rate(self, t)

class Metabolism(name=None)

Context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

class SimulatedMetabolism(name, biomass, growth_rate)

Bases: Metabolism

Simulated context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

biomass

A function of time that describes biomass f(t)=biomass

growth_rate

A function of time that describes the growth rate f(t)=growth rate

gompertz(t, y0, ymax, um, l)

gompertz_growth_rate(t, y0, ymax, um, l)

ramp_biomass(t, od0, start, slope)

ramp_growth_rate(t, start, slope)

step_biomass(t, od0, start)

step_growth_rate(t, start)

loica.sample

Module Contents

Classes

Sample

Representation of a sample that encapsulates GeneticNetwork and Metabolism.

class Sample(genetic_network=None, metabolism=None, assay=None, media=None, strain=None)

Representation of a sample that encapsulates GeneticNetwork and Metabolism. Incorporate environment information such as Supplements or chemicals, strain and media. Ex: 1 well in a plate, single cell. …

genetic_networkGeneticNetwork

genetic network that is part of the sample

metabolismMetabolism

metabolism that drives the genetic network in the sample

assayAssay

assay to which this sample belongs

mediastr

Name of the media in the sample

strainstr

Name of the strain in the sample

Methods

add_supplement(supplement, concentration)

stablishes the concentration of Supplement

initialize(self)

set_regulator(self, name, concentration)

set_reporter(self, name, concentration)

set_supplement(self, supplement, concentration)

step(self, t, dt, stochastic=False)

loica.supplement

Module Contents

Classes

Supplement

Representation of a chemical

class Supplement(name, pubchemid=None, supplier_id=None, sbol_comp=None, color='pink')

Representation of a chemical

…

namestr

Name of the supplement

concentrationint | float

concentration of the supplement in Molar

pubchemidstr

PubChemID URI of the supplement

supplier_idstr

Supplier ID of the supplement. An URL of the product that you aquire. Accepts list of the form [product URL, catalog number, batch].

sbol_compstr

SBOL component of the supplement.

__str__(self)

Return str(self).

loica.util

Module Contents

Functions

characterize_growth(flapjack, vector, media, strain, biomass_signal, n_gaussians, epsilon)
forward_model_growth(Dt=0.05, sim_steps=10, muval=[0] * 100, od0=0, nt=100)
load_loica(filename)
residuals_growth(data, epsilon, dt, t, n_gaussians)
save_loica(obj, filename)

characterize_growth(flapjack, vector, media, strain, biomass_signal, n_gaussians, epsilon)

forward_model_growth(Dt=0.05, sim_steps=10, muval=[0] * 100, od0=0, nt=100)

load_loica(filename)

residuals_growth(data, epsilon, dt, t, n_gaussians)

save_loica(obj, filename)

Package Contents

Classes

Assay	Assay measures a set of samples in parallel at a set of timepoints.
Colony
DataMetabolism	Characterized context for gene expression, incorporates biomass and growth rate.
GeneProduct	A class that represents a gene product, protein or RNA.
GeneticNetwork	Representation of a genetic netowrk composed by a set of Operators, Regulators and Reporters.
Hill1	A class that represents a DNA fragment that encode a genetic operator.
Hill1	A class that represents a DNA fragment that encode a genetic operator.
Hill2	A class that represents a DNA fragment that encode a genetic operator.
Hill2	A class that represents a DNA fragment that encode a genetic operator.
Metabolism	Context for gene expression, incorporates biomass and growth rate.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Operator	A class that represents a DNA fragment that encode a genetic operator.
Receiver	A class that represents a DNA fragment that encode a genetic operator.
Receiver	A class that represents a DNA fragment that encode a genetic operator.
Regulator	Representation of a regulatory gene product.
Regulator	Representation of a regulatory gene product.
Reporter	Representation of a regulatory gene product.
Reporter	Representation of a regulatory gene product.
Sample	Representation of a sample that encapsulates GeneticNetwork and Metabolism.
SimulatedMetabolism	Simulated context for gene expression, incorporates biomass and growth rate.
Source	A class that represents a DNA fragment that encode a genetic operator.
Source	A class that represents a DNA fragment that encode a genetic operator.
Supplement	Representation of a chemical
Supplement	Representation of a chemical

Functions

characterize_growth(flapjack, vector, media, strain, biomass_signal, n_gaussians, epsilon)
forward_model_growth(Dt=0.05, sim_steps=10, muval=[0] * 100, od0=0, nt=100)
gompertz(t, y0, ymax, um, l)
gompertz_growth_rate(t, y0, ymax, um, l)
load_loica(filename)
ramp_biomass(t, od0, start, slope)
ramp_growth_rate(t, start, slope)
residuals_growth(data, epsilon, dt, t, n_gaussians)
save_loica(obj, filename)
step_biomass(t, od0, start)
step_growth_rate(t, start)

class Assay(samples, n_measurements, interval, name='Loica assay', description='', biomass_signal_id=None)

Assay measures a set of samples in parallel at a set of timepoints. Connects to flapjack to generate data, and to fit parameters to data.

…

samplesList[Sample]

List of Samples that belongs to the Assay

n_measurementsint

Number of measurements to take

intervalint

Time in hours between each measurements

namestr

Name of the Assay

description: str

Descriptioin of the Assay

biomass_signal_idint

Flapjack ID of the Assay that is associated with the Assay

run(substeps=10, nsr=0, biomass_bg=0, fluo_bg=0)

Runs the Assay time series

upload(flapjack, study)

Upload the data produced by running the Assay to Flapjack into the Study

Assay measures a set of samples in parallel at a set of timepoints Connects to flapjack to generate data, and to fit parameters to data

run(self, substeps=10, nsr=0, biomass_bg=0, fluo_bg=0, stochastic=False)

Run the assay measuring at specified time points, with simulation time step dt

upload(self, flapjack, study)

class Colony(circuit=None, r0=1, mu0=1)

fun(self, x)

kymograph(self, nx, t0, tmax)

map_kymo(self, kymo)

norm_kymo(self, kymo)

class DataMetabolism(name, fj, media, strain, vector, biomass_signal)

Bases: Metabolism

Characterized context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

fjFlapjack

Flapjack instance used to fetch data from

mediastr

Name of the media to query

strainstr

Name of the strain to query

vectorstr

Name of the vector to query

biomass_signalstr

Name of signal to query and use as biomass

biomass(t)

Return biomass at a given time from characterization data

growth:rate(t)

Return growth rate at a given time from characterization data

biomass(self, t)

growth_rate(self, t)

class GeneProduct(name, init_concentration=0, degradation_rate=0, uri=None, sbol_comp=None, type_='PRO', color='silver')

A class that represents a gene product, protein or RNA.

…

namestr

Name of the gene product

init_concentrationint | float

Initial concentration of the gene product in Molar

degradation_rateint | float

Degradation rate of the gene product

type_str, optional

Molecular type of the gene product, could be ‘PRO’ or ‘RNA’

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

shape = ^

__str__(self)

Return str(self).

express(self, rate)

initialize(self)

step(self, growth_rate, dt)

class GeneticNetwork(vector=None)

Representation of a genetic netowrk composed by a set of Operators, Regulators and Reporters.

…

operatorsList[Operator]

List of Operators that are part of the genetic network

regulatorsList[Regulator]

List of Regulators that are part of the genetic network

reportersList[Reporter]

List of Reporters that are part of the genetic network

vectorint

Flapjack ID of the vector that is associated with the genetic network

to_graph()

Builds a graph representation of the genetic netwok

draw()

Generates a plot of the graph representation builded by to_graph()

to_sbol(sbol_doc=None)

Generates a SBOL3 Document representation of the genetic network on sbol_doc

add_operator(self, ops)

add_regulator(self, regs)

add_reporter(self, reps)

draw(self, node_shape='o', node_size=500, linewidths=0, alpha=0.5, arrowsize=10, font_size=6, font_family='Tahoma', font_weight='bold', pos=nx.kamada_kawai_layout, contracted=False)

initialize(self)

step(self, growth_rate=1, t=0, dt=0.1)

step_stochastic(self, growth_rate=1, t=0, dt=0.1)

substep_stochastic(self, t=0, dt=0.1, growth_rate=1)

to_contracted_graph(self)

to_graph(self)

to_sbol(self, sbol_doc: sbol3.Document = None) → sbol3.Document

Convert the genetic network to SBOL. :param sbol_doc: The SBOL document to add the genetic network to.

class Hill1(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill1 Operator is an abstraction of a repressible or inducible promoter that maps an input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

expression_rate(self, t, dt)

forward_model(self, a_j, b_j, n_i=2, K_i=1, a_A=100.0, b_A=0, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=0, odval=[1] * 100, gamma=0, p0_1=0, p0_2=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, gamma)

class Hill1(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill1 Operator is an abstraction of a repressible or inducible promoter that maps an input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

expression_rate(self, t, dt)

forward_model(self, a_j, b_j, n_i=2, K_i=1, a_A=100.0, b_A=0, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=0, odval=[1] * 100, gamma=0, p0_1=0, p0_2=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, gamma)

class Hill2(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='orange')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill2 Operator is an abstraction of a set of two repressible or inducible promoters that maps an 2 inputs into an output using a Hill function.

…

inputList [Regulator | Supplement]

The inputs of the operator that regulates the expression of the output

outputRegulator | Reporter | List

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver1, receiver2, chemical1, chemical2, nor_inverter, media, strain, signal, biomass_signal, gamma, lower_bounds=[0] * 8, upper_bounds=[100000000.0, 8, 100000000.0, 8, 100000000.0, 100000000.0, 100000000.0, 100000000.0], init_x=[1, 2, 1, 2, 1, 0, 0, 0])

expression_rate(self, t, dt)

forward_model(self, rep1_K=1, rep1_n=2, rep2_K=1, rep2_n=2, alpha0=1, alpha1=0, alpha2=0, alpha3=0, a_A=100.0, b_A=0, K_A=1, n_A=2, a_B=100.0, b_B=0, K_B=1, n_B=2, Dt=0.05, sim_steps=10, A=0, B=0, odval=[1] * 100, gamma=0, rep1_0=0, rep2_0=0, fp_0=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, a_B, b_B, K_B, n_B, chem1, chem2, gamma)

class Hill2(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='orange')

Bases: loica.operators.operator.Operator

A class that represents a DNA fragment that encode a genetic operator. The Hill2 Operator is an abstraction of a set of two repressible or inducible promoters that maps an 2 inputs into an output using a Hill function.

…

inputList [Regulator | Supplement]

The inputs of the operator that regulates the expression of the output

outputRegulator | Reporter | List

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

Return str(self).

characterize(self, flapjack, receiver1, receiver2, chemical1, chemical2, nor_inverter, media, strain, signal, biomass_signal, gamma, lower_bounds=[0] * 8, upper_bounds=[100000000.0, 8, 100000000.0, 8, 100000000.0, 100000000.0, 100000000.0, 100000000.0], init_x=[1, 2, 1, 2, 1, 0, 0, 0])

expression_rate(self, t, dt)

forward_model(self, rep1_K=1, rep1_n=2, rep2_K=1, rep2_n=2, alpha0=1, alpha1=0, alpha2=0, alpha3=0, a_A=100.0, b_A=0, K_A=1, n_A=2, a_B=100.0, b_B=0, K_B=1, n_B=2, Dt=0.05, sim_steps=10, A=0, B=0, odval=[1] * 100, gamma=0, rep1_0=0, rep2_0=0, fp_0=0, nt=100)

residuals(self, df, oddf, a_A, b_A, K_A, n_A, a_B, b_B, K_B, n_B, chem1, chem2, gamma)

class Metabolism(name=None)

Context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Operator(output, name=None, uri=None, sbol_comp=None, color='skyblue')

A class that represents a DNA fragment that encode a genetic operator.

…

outputRegulator | Reporter

The output of the operator that is regulated by the input

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

__str__(self)

Return str(self).

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Receiver(input, output, alpha, K, n, name=None, uri=None, sbol_comp=None, color='skyblue')

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Receiver Operator is an abstraction of an inducible promoter that maps an external input into an output using a Hill function.

…

inputRegulator | Supplement

The input of the operator that regulates the expression of the output

outputRegulator | Reporter

The output of the operator that is regulated by the input

alphaList

[Basal expression rate, Regulated expression rate in MEFL/second]

Kint | float

Half expression input concentration in Molar

nint | float

Hill coefficient, cooperative degree (unitless)

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

unit: str, optional

Units of the characterization data

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, a=0, b=1, K_A=1, n_A=2, Dt=0.05, sim_steps=10, A=[0], odval=[1] * 100, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Regulator(name, init_concentration=0, degradation_rate=0, sbol_comp=None, color='lightgreen')

Bases: GeneProduct

Representation of a regulatory gene product. Child of GeneProduct.

class Regulator(name, init_concentration=0, degradation_rate=0, sbol_comp=None, color='lightgreen')

Bases: GeneProduct

Representation of a regulatory gene product. Child of GeneProduct.

class Reporter(name, init_concentration=0, degradation_rate=0, signal_id=None, color='w', sbol_comp=None)

Bases: GeneProduct

Representation of a regulatory gene product.

signal_idstr, optional

Flapjack ID of the signal that the reporter is associated with.

colorstr, optional

Color of the reporter

class Reporter(name, init_concentration=0, degradation_rate=0, signal_id=None, color='w', sbol_comp=None)

Bases: GeneProduct

Representation of a regulatory gene product.

signal_idstr, optional

Flapjack ID of the signal that the reporter is associated with.

colorstr, optional

Color of the reporter

class Sample(genetic_network=None, metabolism=None, assay=None, media=None, strain=None)

Representation of a sample that encapsulates GeneticNetwork and Metabolism. Incorporate environment information such as Supplements or chemicals, strain and media. Ex: 1 well in a plate, single cell. …

genetic_networkGeneticNetwork

genetic network that is part of the sample

metabolismMetabolism

metabolism that drives the genetic network in the sample

assayAssay

assay to which this sample belongs

mediastr

Name of the media in the sample

strainstr

Name of the strain in the sample

Methods

add_supplement(supplement, concentration)

stablishes the concentration of Supplement

initialize(self)

set_regulator(self, name, concentration)

set_reporter(self, name, concentration)

set_supplement(self, supplement, concentration)

step(self, t, dt, stochastic=False)

class SimulatedMetabolism(name, biomass, growth_rate)

Bases: Metabolism

Simulated context for gene expression, incorporates biomass and growth rate. …

namestr, optional

Name of the metabolism or correponding strain

biomass

A function of time that describes biomass f(t)=biomass

growth_rate

A function of time that describes the growth rate f(t)=growth rate

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Source(output, rate, uri=None, sbol_comp=None, color='blue', name=None)

Bases: Operator

A class that represents a DNA fragment that encode a genetic operator. The Source Operator is an abstraction of a constitutive promoter that produces output.

…

outputRegulator | Reporter

The output of the operator that is constitutively expressed

ratefloat

Output constitutive expression rate in MEFL/second

uristr, optional

SynBioHub URI

sbol_compSBOL Component, optional

SBOL Component

namestr, optional

Name of the operator displayed on the network representation

color: str, optional

Color displayed on the network representation

characterize(flapjack, receiver, inverter, media, strain, signal, biomass_signal, gamma)

Parameterize the Operator model that maps Input concentration into Output expression rate

__str__(self)

characterize(self, flapjack, vector, media, strain, signal, biomass_signal)

expression_rate(self, t, dt)

forward_model(self, Dt=0.25, sim_steps=10, odval=[1] * 97, rate=1, gamma=0, p0=0, nt=100)

residuals(self, df, oddf)

class Supplement(name, pubchemid=None, supplier_id=None, sbol_comp=None, color='pink')

Representation of a chemical

…

namestr

Name of the supplement

concentrationint | float

concentration of the supplement in Molar

pubchemidstr

PubChemID URI of the supplement

supplier_idstr

Supplier ID of the supplement. An URL of the product that you aquire. Accepts list of the form [product URL, catalog number, batch].

sbol_compstr

SBOL component of the supplement.

__str__(self)

Return str(self).

class Supplement(name, pubchemid=None, supplier_id=None, sbol_comp=None, color='pink')

Representation of a chemical

…

namestr

Name of the supplement

concentrationint | float

concentration of the supplement in Molar

pubchemidstr

PubChemID URI of the supplement

supplier_idstr

Supplier ID of the supplement. An URL of the product that you aquire. Accepts list of the form [product URL, catalog number, batch].

sbol_compstr

SBOL component of the supplement.

__str__(self)

Return str(self).

characterize_growth(flapjack, vector, media, strain, biomass_signal, n_gaussians, epsilon)

forward_model_growth(Dt=0.05, sim_steps=10, muval=[0] * 100, od0=0, nt=100)

gompertz(t, y0, ymax, um, l)

gompertz_growth_rate(t, y0, ymax, um, l)

load_loica(filename)

ramp_biomass(t, od0, start, slope)

ramp_growth_rate(t, start, slope)

residuals_growth(data, epsilon, dt, t, n_gaussians)

save_loica(obj, filename)

step_biomass(t, od0, start)

step_growth_rate(t, start)

1

Created with sphinx-autoapi

Introduction

Welcome to the LOICA (Logical Operators for Integrated Cell Algorithms) repository, our Python package for designing, modeling and characterizing genetic networks.

As you may have noticed, our logo features a beautiful bird—loica (Leistes loyca); a bird native to Chile known for its particular red chest and legendary kindness, with which we share name.

Installation

Installing LOICA is way easier than pronuncing it!

pip install loica

For more details please refer to our Wiki for installation instructions and developer guides.

LOICA allows you to:

• Compile Code into DNA fragments that execute Cell Algorithms

• Easy programation of genetic network models

• Generation of synthetic data

• Communicate with Flapjack

• Use and output SBOL files

• Use all sorts of cellular computation

• Easy, fluid and customisable DNA design

Tutorials

Now that you have LOICA installed you can familiarize yourself with the tool using the Jupyter notebook tutorials designed for this purpose.