{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-a-nonlinear-dynamical-system-model","title":"Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach","arxiv_id":"1207.3137","date":"2012-07-13","proceeding":null,"authors":["Arwen Vanice Bradley","Ye Henry Li","Bokyung Choi","Wing Hung Wong"],"abstract":"Biological structure and function depend on complex regulatory interactions\nbetween many genes. A wealth of gene expression data is available from\nhigh-throughput genome-wide measurement technologies, but effective gene\nregulatory network inference methods are still needed. Model-based methods\nfounded on quantitative descriptions of gene regulation are among the most\npromising, but many such methods rely on simple, local models or on ad hoc\ninference approaches lacking experimental interpretability. We propose an\nexperimental design and develop an associated statistical method for inferring\na gene network by learning a standard quantitative, interpretable, predictive,\nbiophysics-based ordinary differential equation model of gene regulation. We\nfit the model parameters using gene expression measurements from perturbed\nsteady-states of the system, like those following overexpression or knockdown\nexperiments. Although the original model is nonlinear, our design allows us to\ntransform it into a convex optimization problem by restricting attention to\nsteady-states and using the lasso for parameter selection. Here, we describe\nthe model and inference algorithm and apply them to a synthetic six-gene\nsystem, demonstrating that the model is detailed and flexible enough to account\nfor activation and repression as well as synergistic and self-regulation, and\nthe algorithm can efficiently and accurately recover the parameters used to\ngenerate the data.","url_abs":"http://arxiv.org/abs/1207.3137v4","url_pdf":"http://arxiv.org/pdf/1207.3137v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-a-nonlinear-dynamical-system-model","repo_url":"https://github.com/2019020826/SDSMCMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"learning-a-nonlinear-dynamical-system-model","repo_url":"https://github.com/cbskust/SDS_Epidemic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"experimental-design","task_name":"Experimental Design"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}