{"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/switched-latent-force-models-for-reverse","title":"Switched latent force models for reverse-engineering transcriptional regulation in gene expression data","arxiv_id":"1511.07334","date":"2015-11-23","proceeding":null,"authors":["Andrés F. López-Lopera","Mauricio A. Álvarez"],"abstract":"To survive environmental conditions, cells transcribe their response\nactivities into encoded mRNA sequences in order to produce certain amounts of\nprotein concentrations. The external conditions are mapped into the cell\nthrough the activation of special proteins called transcription factors (TFs).\nDue to the difficult task to measure experimentally TF behaviours, and the\nchallenges to capture their quick-time dynamics, different types of models\nbased on differential equations have been proposed. However, those approaches\nusually incur in costly procedures, and they present problems to describe\nsudden changes in TF regulators. In this paper, we present a switched dynamical\nlatent force model for reverse-engineering transcriptional regulation in gene\nexpression data which allows the exact inference over latent TF activities\ndriving some observed gene expressions through a linear differential equation.\nTo deal with discontinuities in the dynamics, we introduce an approach that\nswitches between different TF activities and different dynamical systems. This\ncreates a versatile representation of transcription networks that can capture\ndiscrete changes and non-linearities We evaluate our model on both simulated\ndata and real-data (e.g. microaerobic shift in E. coli, yeast respiration),\nconcluding that our framework allows for the fitting of the expression data\nwhile being able to infer continuous-time TF profiles.","url_abs":"http://arxiv.org/abs/1511.07334v2","url_pdf":"http://arxiv.org/pdf/1511.07334v2.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":"switched-latent-force-models-for-reverse","repo_url":"https://github.com/anfelopera/SDLFM_ReverseEngineering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}