Automatic Construction of Petri Net Models for Computational Simulations of Molecular Interaction Network

Overview

Journal NPJ Syst Biol Appl

Specialty Biology

Date 2024 Nov 9

PMID 39521772

Authors

Xuefei Lin

Xiao Chang

Yizheng Zhang

Zhanyu Gao

Xu Chi

Affiliations

Soon will be listed here.

Abstract

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1's function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

References

Paczkowska M, Barenboim J, Sintupisut N, Fox N, Zhu H, Abd-Rabbo D . Integrative pathway enrichment analysis of multivariate omics data. Nat Commun. 2020; 11(1):735. PMC: 7002665. DOI: 10.1038/s41467-019-13983-9. View

Thomas P, Ebert D, Muruganujan A, Mushayahama T, Albou L, Mi H . PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 2021; 31(1):8-22. PMC: 8740835. DOI: 10.1002/pro.4218. View

Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M . KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2022; 51(D1):D587-D592. PMC: 9825424. DOI: 10.1093/nar/gkac963. View

Gutmann D, Ferner R, Listernick R, Korf B, Wolters P, Johnson K . Neurofibromatosis type 1. Nat Rev Dis Primers. 2017; 3:17004. DOI: 10.1038/nrdp.2017.4. View

Zhao Q, Savoie B . Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nat Comput Sci. 2024; 1(7):479-490. DOI: 10.1038/s43588-021-00101-3. View

Gutowska K, Kogut D, Kardynska M, Formanowicz P, Smieja J, Puszynski K . Petri nets and ODEs as complementary methods for comprehensive analysis on an example of the ATM-p53-NF-[Formula: see text]B signaling pathways. Sci Rep. 2022; 12(1):1135. PMC: 8782877. DOI: 10.1038/s41598-022-04849-0. View

Liu F, Heiner M, Gilbert D . Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems. Brief Bioinform. 2017; 20(3):877-886. PMC: 6585149. DOI: 10.1093/bib/bbx150. View

Pennisi M, Cavalieri S, Motta S, Pappalardo F . A methodological approach for using high-level Petri Nets to model the immune system response. BMC Bioinformatics. 2017; 17(Suppl 19):498. PMC: 5259858. DOI: 10.1186/s12859-016-1361-6. View

Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E . MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2011; 40(Database issue):D857-61. PMC: 3244991. DOI: 10.1093/nar/gkr930. View

10.

Brinkrolf C, Janowski S, Kormeier B, Lewinski M, Hippe K, Borck D . VANESA - a software application for the visualization and analysis of networks in system biology applications. J Integr Bioinform. 2014; 11(2):239. DOI: 10.2390/biecoll-jib-2014-239. View

11.

Brinkrolf C, Ochel L, Hofestadt R . VANESA: An open-source hybrid functional Petri net modeling and simulation environment in systems biology. Biosystems. 2021; 210:104531. DOI: 10.1016/j.biosystems.2021.104531. View

12.

Ruths D, Muller M, Tseng J, Nakhleh L, Ram P . The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks. PLoS Comput Biol. 2008; 4(2):e1000005. PMC: 2265486. DOI: 10.1371/journal.pcbi.1000005. View

13.

Martinez-Nunez E, Barnes G, Glowacki D, Kopec S, Pelaez D, Rodriguez A . AutoMeKin2021: An open-source program for automated reaction discovery. J Comput Chem. 2021; 42(28):2036-2048. DOI: 10.1002/jcc.26734. View

14.

Theocharidis A, van Dongen S, Enright A, Freeman T . Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat Protoc. 2009; 4(10):1535-50. DOI: 10.1038/nprot.2009.177. View

15.

Kandasamy K, Mohan S, Raju R, Keerthikumar S, Sameer Kumar G, Venugopal A . NetPath: a public resource of curated signal transduction pathways. Genome Biol. 2010; 11(1):R3. PMC: 2847715. DOI: 10.1186/gb-2010-11-1-r3. View

16.

Romero P, Wagg J, Green M, Kaiser D, Krummenacker M, Karp P . Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005; 6(1):R2. PMC: 549063. DOI: 10.1186/gb-2004-6-1-r2. View

17.

Duarte N, Becker S, Jamshidi N, Thiele I, Mo M, Vo T . Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007; 104(6):1777-82. PMC: 1794290. DOI: 10.1073/pnas.0610772104. View

18.

Steiner M, Reiher M . A human-machine interface for automatic exploration of chemical reaction networks. Nat Commun. 2024; 15(1):3680. PMC: 11063077. DOI: 10.1038/s41467-024-47997-9. View

19.

Radom M, Rybarczyk A, Szawulak B, Andrzejewski H, Chabelski P, Kozak A . Holmes: a graphical tool for development, simulation and analysis of Petri net based models of complex biological systems. Bioinformatics. 2017; 33(23):3822-3823. DOI: 10.1093/bioinformatics/btx492. View

20.

Hornbeck P, Zhang B, Murray B, Kornhauser J, Latham V, Skrzypek E . PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2014; 43(Database issue):D512-20. PMC: 4383998. DOI: 10.1093/nar/gku1267. View