{"title":"Inferring gene regulatory networks using pre- and post-perturbation data.","authors":"Menghan Peng, Qing Hu, Ruiqi Wang","doi":"10.1007/s10867-025-09688-4","DOIUrl":null,"url":null,"abstract":"<p><p>The inference of biological networks is essential for understanding the complex regulations among biomolecules. Jacobian matrices serve as an effective means for uncovering network topologies by providing linear approximations of nonlinear regulations. Reconstructing Jacobian matrices often requires integrating experimental data with mathematical modeling techniques. A significant challenge is determining the type of experimental data required and the adequate amount of data to accurately reconstruct the Jacobian matrices. In this paper, we employ multiple pre- and post-perturbation data to infer the Jacobian matrices with the help of Taylor expansions. Furthermore, we integrate the expansions with differential approximations of the partial derivative to offer supplementary information. These data enable accurate inference of not only the signs and directions of regulations but also the strength of self-feedback in both steady-state and oscillatory systems. Comparative analysis reveals that incorporating differential approximations can significantly improve the accuracy of inference.</p>","PeriodicalId":612,"journal":{"name":"Journal of Biological Physics","volume":"51 1","pages":"23"},"PeriodicalIF":2.2000,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12222606/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biological Physics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s10867-025-09688-4","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
The inference of biological networks is essential for understanding the complex regulations among biomolecules. Jacobian matrices serve as an effective means for uncovering network topologies by providing linear approximations of nonlinear regulations. Reconstructing Jacobian matrices often requires integrating experimental data with mathematical modeling techniques. A significant challenge is determining the type of experimental data required and the adequate amount of data to accurately reconstruct the Jacobian matrices. In this paper, we employ multiple pre- and post-perturbation data to infer the Jacobian matrices with the help of Taylor expansions. Furthermore, we integrate the expansions with differential approximations of the partial derivative to offer supplementary information. These data enable accurate inference of not only the signs and directions of regulations but also the strength of self-feedback in both steady-state and oscillatory systems. Comparative analysis reveals that incorporating differential approximations can significantly improve the accuracy of inference.
期刊介绍:
Many physicists are turning their attention to domains that were not traditionally part of physics and are applying the sophisticated tools of theoretical, computational and experimental physics to investigate biological processes, systems and materials.
The Journal of Biological Physics provides a medium where this growing community of scientists can publish its results and discuss its aims and methods. It welcomes papers which use the tools of physics in an innovative way to study biological problems, as well as research aimed at providing a better understanding of the physical principles underlying biological processes.