Skip to main content

A chemical reaction network compiler for generating large biological circuit models

Project description

BioCRNPyler — Biomolecular Chemical Reaction Network Compiler

Python toolbox to create CRN models in SBML for biomolecular mechanisms

Build Status codecov PyPI version Binder

BioCRNPyler (pronounced Bio-Compiler) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex biochemical networks.

Example 1: Building Simple CRNs by Hand

BioCRNpyler allows for CRNs to be built by hand, adding Species and Reactions manually.

from biocrnpyler import *
# let's build the following CRN
# A -->[k1] 2B
# B -->[k2] C+D
# Species
A = Species("A")
B = Species("B")
C = Species("C")
D = Species("D")

#Reaction Rates
k1 = 3.
k2 = 1.4

#Reaction Objects
R1 = Reaction.from_massaction([A], [B, B], k_forward = k1)
R2 = Reaction.from_massaction([B], [C, D], k_forward = k2)

#Make a CRN
CRN = ChemicalReactionNetwork(species = [A, B, C, D], reactions = [R1, R2])
print(CRN)

Example 2: Compiling Complex CRNs from Specifications

BioCRNpyler also allows for higher level descriptions to be compiled into a CRN. In the below example, a piece of synthetic DNA with two promoters pointing in opposite directions is constructed from a list of DNAparts which are combined together in a DNA_construct and then simulated in a TxTlExtract context, which represents a cell-free bacterial lysate with machinery like Ribosomes and Polymerases modeled explicitly.

Specification to CRN Illustration

from biocrnpyler import *

#Define a set of DNA parts
ptet = RegulatedPromoter("ptet",["tetr"],leak=True) #this is a promoter repressed by tetR and has a leak reaction
pconst = Promoter("pconst") #constitutive promoter
pcomb = CombinatorialPromoter("pcomb",["arac","laci"], leak=False, tx_capable_list = [["arac"], ["laci"]]) #the Combinations A and B or just A or just B be transcribed
utr1 = RBS("UTR1") #regular RBS
utr2 = RBS("UTR1") #regular RBS
gfp = CDS("GFP","GFP") #a CDS has a name and a protein name. so this one is called GFP and the protein is also called GFP
fusrfp = CDS("fusRFP","RFP",no_stop_codons=["forward"]) #you can say that a protein has no stop codon. This is a little different from a fusion protein, because in this case you are saying that the ribosome reads through two proteins but still produces two distinct proteins, rather than one fused protein. This can happen in the case of the ta peptide which causes a peptide bond not to be formed while making a protein.
rfp = CDS("RFP","RFP") #regular RFP
cfp = CDS("CFP","CFP") #cfp
t16 = Terminator("t16") #a terminator stops transcription

#Combine the parts together in a DNA_construct with their directions
construct = DNA_construct([[ptet,"forward"],[utr1,"forward"],[gfp,"forward"],[t16,"forward"],[t16,"reverse"],[rfp,"reverse"],[utr1,"reverse"],[pconst,"reverse"]])

#some very basic parameters are defined - these are sufficient for the whole model to compile!
parameters={"cooperativity":2,"kb":100, "ku":10, "ktx":.05, "ktl":.2, "kdeg":2,"kint":.05}

#Place the construct in a context (TxTlExtract models a bacterial lysate with machinery like Ribosomes and Polymerases modelled explicitly)
myMixture = TxTlExtract(name = "txtl", parameters = parameters, components = [construct])

#Compile the CRN
myCRN = myMixture.compile_crn()

#plotting not shown - but BioCRNpyler automatically produces interactive reaction network graphs to help visualize and debug complex CRNs!

More advanced examples can be found in the example folder, here's the first file in the Tutorial series: Building CRNs

Installation

Install the latest version of BioCRNPyler::

$ pip install biocrnpyler

Install with all optional dependencies::

$ pip install biocrnpyler[all]

(Note that on some operating systems you made need to use "[all]" to avoid shell errors.)

Further details about the installation process can be found in the BioCRNPyler wiki.

Bugs

Please report any bugs that you find here. Or, even better, fork the repository on GitHub, and create a pull request (PR). We welcome all changes, big or small, and we will help you make the PR if you are new to git (just ask on the issue and/or see contribution guidelines).

Versions

BioCRNpyler versions:

  • 1.1.2 (latest stable release): To install run pip install biocrnpyler
  • 1.1.1 (previous stable release, compatible only with python <= 3.10): To install run pip install biocrnpyler==1.1.1
  • 0.9.0 (beta release): To install run pip install biocrnpyler==0.9.0
  • 0.2.1 (alpha release): To install run pip install biocrnpyler==0.2.1

License

Released under the BSD 3-Clause License (see LICENSE)

Copyright (c) 2024, Build-A-Cell. All rights reserved.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

biocrnpyler-1.1.2.tar.gz (124.2 kB view details)

Uploaded Source

Built Distribution

BioCRNpyler-1.1.2-py2.py3-none-any.whl (140.3 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file biocrnpyler-1.1.2.tar.gz.

File metadata

  • Download URL: biocrnpyler-1.1.2.tar.gz
  • Upload date:
  • Size: 124.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for biocrnpyler-1.1.2.tar.gz
Algorithm Hash digest
SHA256 c51cf3afeeb9ede5dfd678e6f00a3921c24d3a3eb3229970e2e53257fc6873de
MD5 5ca2dabdc33283e29a7eb06d9efb961d
BLAKE2b-256 c3a0fa7d5950f916424deab94d62bc31afdb4ccee945ec057c58de3215b8b53d

See more details on using hashes here.

File details

Details for the file BioCRNpyler-1.1.2-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for BioCRNpyler-1.1.2-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 8d4abe7b1c469258ecb034df47e6fb65b333983a4744d09ab4bb3d7502c02650
MD5 d7e22412e00dc7d420e8bb46b1b13d66
BLAKE2b-256 b71376acf265ab8b1ae6cf586a8300f65e6e4f54ae84035ed50ed283599a59fb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page