Modular Machine Learning with Applications to Genetic Circuit Composition

• URL: http://arxiv.org/abs/2509.19601v1
• Date: Tue, 23 Sep 2025 21:49:13 GMT
• Title: Modular Machine Learning with Applications to Genetic Circuit Composition
• Abstract summary: Learning the modules' input/output functions is also necessary for designing new systems from different composition architectures.<n>We propose a modular learning framework, which incorporates prior knowledge of the system's compositional structure to identify the composing modules' input/output functions.<n>By reducing the need for experimental data and allowing module identification, this framework offers the potential to ease the design of synthetic biological circuits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 a class of systems motivated by genetic circuits. We demonstrate the theory on computational studies showing that a neural network (NNET) that accounts for the compositional structure can learn the composing modules' input/output functions and predict the system's output on inputs outside of the training set distribution. By contrast, a neural network that is agnostic of the structure is unable to predict on inputs that fall outside of the training set distribution. By reducing the need for experimental data and allowing module identification, this framework offers the potential to ease the design of synthetic biological circuits and of multi-module systems more generally.

Related papers

• AXLearn: Modular Large Model Training on Heterogeneous Infrastructure [64.33868455931301]
  AXLearn is a production deep learning system that facilitates scalable and high-performance training of large deep learning models.<n>Compared to other state-of-the-art deep learning systems, AXLearn has a unique focus on modularity and support for heterogeneous hardware infrastructure.
  arXiv  Detail & Related papers  (2025-07-07T18:50:58Z)
• Adaptive Orchestration of Modular Generative Information Access Systems [59.102816309859584]
  We argue that the architecture of future modular generative information access systems will not just assemble powerful components, but enable a self-organizing system.<n>This perspective urges the IR community to rethink modular system designs for developing adaptive, self-optimizing, and future-ready architectures.
  arXiv  Detail & Related papers  (2025-04-24T11:35:43Z)
• On The Specialization of Neural Modules [16.83151955540625]
  We study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization. Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity.
  arXiv  Detail & Related papers  (2024-09-23T12:58:11Z)
• Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks [32.707730631343416]
  We present a framework for learning composite models of dynamical systems from data. neural network submodels are trained on trajectory data generated by relatively simple subsystems. We demonstrate the novel capabilities of the proposed framework through numerical examples.
  arXiv  Detail & Related papers  (2022-12-01T22:22:38Z)
• Learning Modular Simulations for Homogeneous Systems [23.355189771765644]
  We present a modular simulation framework for modeling homogeneous multibody dynamical systems. An arbitrary number of modules can be combined to simulate systems of a variety of coupling topologies. We show that our models can be transferred to new system configurations lower with data requirement and training effort, compared to those trained from scratch.
  arXiv  Detail & Related papers  (2022-10-28T17:48:01Z)
• Is a Modular Architecture Enough? [80.32451720642209]
  We provide a thorough assessment of common modular architectures, through the lens of simple and known modular data distributions. We highlight the benefits of modularity and sparsity and reveal insights on the challenges faced while optimizing modular systems.
  arXiv  Detail & Related papers  (2022-06-06T16:12:06Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Neural-Symbolic Integration: A Compositional Perspective [8.566457170664926]
  The question of how to integrate a neural and a symbolic system in a emphcompositional manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture. We are able to show that a symbolic module can be cleanly integrated with a neural module, and facilitate the latter's efficient training.
  arXiv  Detail & Related papers  (2020-10-22T17:55:44Z)
• Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers [84.57980167400513]
  Neural Function Modules (NFM) aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm.
  arXiv  Detail & Related papers  (2020-10-15T20:43:17Z)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.