{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:08:07Z","timestamp":1760234887913,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11871238, 11931019, 61773401"],"award-info":[{"award-number":["11871238, 11931019, 61773401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Foundation of Wuhan Institute of Technology","award":["20QD47"],"award-info":[{"award-number":["20QD47"]}]},{"name":"Foundation of Zhongnan University of Economics and Law","award":["3173211205"],"award-info":[{"award-number":["3173211205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.<\/jats:p>","DOI":"10.3390\/sym13061097","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T13:29:58Z","timestamp":1624282198000},"page":"1097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5833-4607","authenticated-orcid":false,"given":"Yan","family":"Yan","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6191-0209","authenticated-orcid":false,"given":"Tianhai","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Mathematics, Monash University, Melbourne 3800, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/nrm1857","article-title":"The model organism as a system: Integrating \u2018omics\u2019 data sets","volume":"7","author":"Joyce","year":"2006","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"Laehnemann","year":"2020","journal-title":"Genome Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"c221","DOI":"10.1136\/bmj.c221","article-title":"Meta-analysis of individual participant data: Rationale, conduct, and reporting","volume":"340","author":"Riley","year":"2010","journal-title":"BMJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/nrg3185","article-title":"Insights into the regulation of protein abundance from proteomic and transcriptomic analyses","volume":"13","author":"Vogel","year":"2012","journal-title":"Nat. Rev. Genet."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.copbio.2019.12.002","article-title":"Network inference in systems biology: Recent developments, challenges, and applications","volume":"63","author":"Singh","year":"2020","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1038\/nrm2503","article-title":"Modelling and analysis of gene regulatory networks","volume":"9","author":"Karlebach","year":"2008","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s42005-020-0345-z","article-title":"Exploiting symmetry in network analysis","volume":"3","year":"2020","journal-title":"Commun. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhao, Y., and Han, X. (2019). Characterization of symmetry of complex networks. Symmetry, 11.","DOI":"10.3390\/sym11050692"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for robust gene network inference","volume":"9","author":"Marbach","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, M., He, W., Tang, J., Zou, Q., and Guo, F. (2021). A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Brief. Bioinform.","DOI":"10.1093\/bib\/bbab009"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1093\/bib\/bbz017","article-title":"Network-based methods for predicting essential genes or proteins: A survey","volume":"21","author":"Li","year":"2020","journal-title":"Brief. Bioinform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3389\/fgene.2017.00096","article-title":"Quantifying gene regulatory relationships with association measures: A comparative study","volume":"8","author":"Liu","year":"2017","journal-title":"Front. Genet."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1126\/science.1087447","article-title":"A gene-coexpression network for global discovery of conserved genetic modules","volume":"302","author":"Stuart","year":"2003","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1038\/s41467-017-02288-4","article-title":"Model-free inference of direct network interactions from nonlinear collective dynamics","volume":"8","author":"Casadiego","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"59152","DOI":"10.1109\/ACCESS.2018.2873013","article-title":"Discovery of relationships between long non-coding rnas and genes in human diseases based on tensor completion","volume":"6","author":"Peng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TCBB.2018.2866836","article-title":"Integration of multi-omics data for gene regulatory network inference and application to breast cancer","volume":"16","author":"Yuan","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, B., Chen, Y., Zhang, W., Lv, J., Bao, W., and Huang, D. (2018). Hscvfnt: Inference of time-delayed gene regulatory network based on complex-valued flexible neural tree model. Int. J. Mol. Sci., 19.","DOI":"10.3390\/ijms19103178"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2007\/79879","article-title":"Information-theoretic inference of large transcriptional regulatory networks","volume":"2007","author":"Meyer","year":"2007","journal-title":"EURASIP J. Bioinform. Syst. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1093\/bioinformatics\/btn081","article-title":"Network-constrained regularization and variable selection for analysis of genomic data","volume":"24","author":"Li","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20533","DOI":"10.1038\/srep20533","article-title":"Gene regulatory network inference using fused lasso on multiple data sets","volume":"6","author":"Omranian","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1093\/bioinformatics\/bts514","article-title":"Bayesian inference of signaling network topology in a cancer cell line","volume":"28","author":"Hill","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_22","first-page":"613","article-title":"Estimating high-dimensional directed acyclic graphs with the pc-algorithm","volume":"8","author":"Kalisch","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1093\/bioinformatics\/btr626","article-title":"Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information","volume":"28","author":"Zhang","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1093\/nar\/gku1315","article-title":"Conditional mutual inclusive information enables accurate quantication of associations in gene regulatory networks","volume":"43","author":"Zhang","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1073\/pnas.1522586113","article-title":"Part mutual information for quantifying direct associations in networks","volume":"113","author":"Zhao","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.2174\/1389203721666200213103350","article-title":"Overview of gene regulatory network inference based on differential equation models","volume":"21","author":"Yang","year":"2020","journal-title":"Curr. Protein Pept. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cels.2017.08.014","article-title":"Gene regulatory network inference from single-cell data using multivariate information measures","volume":"5","author":"Chan","year":"2017","journal-title":"Cell Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4885","DOI":"10.1093\/bioinformatics\/btaa032","article-title":"Inference of gene regulatory networks based on nonlinear ordinary differential equations","volume":"36","author":"Ma","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20180943","DOI":"10.1098\/rsif.2018.0943","article-title":"Simulation and inference algorithms for stochastic biochemical reaction networks: From basic concepts to state-of-the-art","volume":"16","author":"Warne","year":"2019","journal-title":"J. R. Soc. Interface"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ymeth.2016.08.001","article-title":"An integrated platform for reverse-engineering protein-gene interaction network","volume":"110","author":"Wang","year":"2016","journal-title":"Methods"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, J., Hu, X., Zou, X., and Tian, T. (2017). Reverse-engineering of gene networks for regulating early blood development from single-cell measurements. BMC Med. Genom., 10.","DOI":"10.1186\/s12920-017-0312-z"},{"key":"ref_33","first-page":"2174","article-title":"Inference of protein-protein networks for triple-negative breast cancer using single-patient proteomic data","volume":"2018","author":"Yan","year":"2018","journal-title":"Proc. BIBM"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"58255","DOI":"10.1109\/ACCESS.2019.2913084","article-title":"Rndetree: Regulatory network with differential equation based on flexible neural tree with novel criterion function","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"27151","DOI":"10.1073\/pnas.1911536116","article-title":"Deep learning for inferring gene relationships from single-cell expression data","volume":"116","author":"Yuan","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kishan, K., Li, R., Cui, F., Yu, Q., and Haake, A.R. (2019). Gne: A deep learning framework for gene network inference by aggregating biological information. BMC Syst. Biol., 13.","DOI":"10.1186\/s12918-019-0694-y"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e117","DOI":"10.1093\/nar\/gkw430","article-title":"TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis","volume":"44","author":"Ji","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compbiolchem.2019.03.013","article-title":"SCOUT: A new algorithm for the inference of pseudo-time trajectory using single-cell data","volume":"80","author":"Wei","year":"2019","journal-title":"Comput. Biol. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1038\/nrg.2017.38","article-title":"Network propagation: A universal amplifier of genetic associations","volume":"18","author":"Cowen","year":"2017","journal-title":"Nat. Rev. Genet."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1038\/nmeth.3971","article-title":"Diffusion pseudotime robustly reconstructs lineage branching","volume":"13","author":"Haghverdi","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"Saelens","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1093\/bioinformatics\/btv257","article-title":"Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data","volume":"31","author":"Ocone","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Tran, D., Tran, B., Pehlivan, B., and Nguyen, T. (2021). A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief. Bioinform.","DOI":"10.1093\/bib\/bbaa190"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.celrep.2015.03.050","article-title":"The proteomic landscape of triple-negative breast cancer","volume":"11","author":"Lawrence","year":"2015","journal-title":"Cell Rep."},{"key":"ref_48","first-page":"153","article-title":"Mitogen-activated protein (map) kinase pathways: Regulation and physiological functions","volume":"22","author":"Pearson","year":"2021","journal-title":"Endocr. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"D277","DOI":"10.1093\/nar\/gkh063","article-title":"The kegg resource for deciphering the genome","volume":"32","author":"Kanehisa","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/msb4100179","article-title":"Towards a theory of biological robustness","volume":"3","author":"Kitano","year":"2007","journal-title":"Mol. Syst. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v070.i01","article-title":"Missmda: A package for handling missing values in multivariate data analysis","volume":"70","author":"Josse","year":"2016","journal-title":"J. Stat. Softw."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/TCBB.2018.2880974","article-title":"Inference of Model Parameters Using Particle Filter Algorithm and Copula Distributions","volume":"17","author":"Deng","year":"2020","journal-title":"IEEE\/ACM Trans Comput. Biol. Bioinform."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/6\/1097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:20:07Z","timestamp":1760163607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/6\/1097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["sym13061097"],"URL":"https:\/\/doi.org\/10.3390\/sym13061097","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,6,21]]}}}