Work

Analysis of biological networks using Krylov subspace trajectories

Year: 2026

Type: preprint

Abstract: We describe an approach for analyzing biological networks using rows of the Krylov subspace of the adjacency matrix. Specifically, we explore the scenario where the Krylov subspace matrix is computed via power iteration using a non-random and potentially non-uniform initial vector that captures a specific biological state or perturbation. In this case, the rows the Krylov subspace matrix (i.e., Krylov trajectories) carry important functional information about the network nodes in the biological context represented by the initial vector. We demonstrate the utility of this approach for community detection and perturbation analysis using the C. Elegans neural network.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.03.29.715092

Author: H. Robert Frost

Institution: Dartmouth College

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Dartmouth College, National Institutes of Health, Audrey and Theodor Geisel School of Medicine at Dartmouth

Awards: G9000017064, G9000123209, G9000235516