» Articles » PMID: 35442944

BioCRNpyler: Compiling Chemical Reaction Networks from Biomolecular Parts in Diverse Contexts

Overview
Specialty Biology
Date 2022 Apr 20
PMID 35442944
Authors
Affiliations
Soon will be listed here.
Abstract

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions.

Citing Articles

Fine-tuned protein-lipid interactions in biological membranes: exploration and implications of the ORMDL-ceramide negative feedback loop in the endoplasmic reticulum.

Dingjan T, Futerman A Front Cell Dev Biol. 2024; 12:1457209.

PMID: 39170919 PMC: 11335536. DOI: 10.3389/fcell.2024.1457209.


Catalyst: Fast and flexible modeling of reaction networks.

Loman T, Ma Y, Ilin V, Gowda S, Korsbo N, Yewale N PLoS Comput Biol. 2023; 19(10):e1011530.

PMID: 37851697 PMC: 10584191. DOI: 10.1371/journal.pcbi.1011530.


Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations.

Deng Y, Beahm D, Ran X, Riley T, Sarpeshkar R Front Bioeng Biotechnol. 2022; 10:947508.

PMID: 36246369 PMC: 9554301. DOI: 10.3389/fbioe.2022.947508.


Vivarium: an interface and engine for integrative multiscale modeling in computational biology.

Agmon E, Spangler R, Skalnik C, Poole W, Peirce S, Morrison J Bioinformatics. 2022; 38(7):1972-1979.

PMID: 35134830 PMC: 8963310. DOI: 10.1093/bioinformatics/btac049.


Competitive dCas9 binding as a mechanism for transcriptional control.

Anderson D, Voigt C Mol Syst Biol. 2021; 17(11):e10512.

PMID: 34747560 PMC: 8574044. DOI: 10.15252/msb.202110512.


References
1.
Perkel J . Why Jupyter is data scientists' computational notebook of choice. Nature. 2018; 563(7729):145-146. DOI: 10.1038/d41586-018-07196-1. View

2.
Hu C, Varner J, Lucks J . Generating Effective Models and Parameters for RNA Genetic Circuits. ACS Synth Biol. 2015; 4(8):914-26. DOI: 10.1021/acssynbio.5b00077. View

3.
Smith L, Hucka M, Hoops S, Finney A, Ginkel M, Myers C . SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3. J Integr Bioinform. 2015; 12(2):268. PMC: 5451323. DOI: 10.2390/biecoll-jib-2015-268. View

4.
Singhal V, Tuza Z, Sun Z, Murray R . A MATLAB toolbox for modeling genetic circuits in cell-free systems. Synth Biol (Oxf). 2021; 6(1):ysab007. PMC: 8102020. DOI: 10.1093/synbio/ysab007. View

5.
Nielsen A, Der B, Shin J, Vaidyanathan P, Paralanov V, Strychalski E . Genetic circuit design automation. Science. 2016; 352(6281):aac7341. DOI: 10.1126/science.aac7341. View