Year: 2026
Type: preprint
Abstract: We present a learning algorithm for inferring threshold Boolean networks (TBNs) with a prescribed set of fixed points. The proposed method employs a custom differentiable loss function that jointly enforces fixed point preservation, penalizes spurious attractors, encourages binary outputs, and promotes sparsity through L1 regularization. Applied to the FOS-GRN model of Arabidopsis thaliana, the approach achieved perfect reconstruction (i.e., all 10 desired fixed points and no spurious ones) in 5 out of 30 independent runs, recovering on average 8.53 $\pm$ 0.90 correct fixed points with no spurious attractors. In contrast, standard methods such as the Perceptron and Logistic Regression recovered up to 10 fixed points but introduced between 8 and 31 spurious ones. An additional analysis varying the sparsity coefficient ($λ$) confirmed that the method's performance and the structural properties of the inferred networks remain robust within a practical range (up to 0.01) of regularization strengths. Overall, the results demonstrate the effectiveness and stability of the proposed algorithm in capturing meaningful network dynamics under prescribed dynamical constraints.
Source: arXiv (Cornell University)
Author: Ruz, Gonzalo A.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: MILLENIUM NUCLEUS IN DATA SCIENCE FOR PLANT RESILIENCE, MILLENNIUM NUCLEUS FOR SOCIAL DATA SCIENCE, G7176096281, G7621791636, CENTRO DE ECOLOGIA APLICADA Y SUSTENTABILIDAD, G7852296049, CENTER FOR MATHEMATICAL MODELING (CMM), G8410773221, LEARNING THRESHOLD NETWORKS FOR SOCIAL AND BIOLOGICAL APPLICATIONS
OpenAlex ID: W7213601294