[2509.19601] Learning Genetic Circuit Modules with Neural Networks: Full Version

Computer Science > Machine Learning arXiv:2509.19601 (cs) [Submitted on 23 Sep 2025 (v1), last revised 29 Mar 2026 (this version, v2)] Title:Learning Genetic Circuit Modules with Neural Networks: Full Version Authors:Jichi Wang, Eduardo D. Sontag, Domitilla Del Vecchio View a PDF of the paper titled Learning Genetic Circuit Modules with Neural Networks: Full Version, by Jichi Wang and 2 other authors View PDF HTML (experimental) Abstract:In several applications, including in synthetic biology, one often has input/output data on a system composed of many modules, and although the modules' input/output functions and signals may be unknown, knowledge of the composition architecture can significantly reduce the amount of training data required to learn the system's input/output mapping. Learning the modules' input/output functions is also necessary for designing new systems from different composition architectures. Here, we propose a modular learning framework, which incorporates prior knowledge of the system's compositional structure to (a) identify the composing modules' input/output functions from the system's input/output data and (b) achieve this by using a reduced amount of data compared to what would be required without knowledge of the compositional structure. To achieve this, we introduce the notion of modular identifiability, which allows recovery of modules' input/output functions from a subset of the system's input/output data, and provide theoretical guarantees on...