Modularity-based approach identifies small sets of control nodes in synthetic and biological Boolean networks

Modularity-based approach identifies small sets of control nodes in synthetic and biological Boolean networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Modularity-based approach identifies small sets of control nodes in synthetic and biological Boolean networks Fatemeh Sadat Fatemi Nasrollahi, Eli Newby, Réka Albert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7371773/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study investigates the problem of controlling the dynamics of biological systems to achieve desired outcomes (attractors). Specifically, we describe biological systems with Boolean models, which represent the system as a network, characterize system components (nodes) with binary states, and use discrete functions to describe the state changes of the nodes due to their interactions. We build upon the Feedback Vertex Set (FVS) theory, which guarantees that controlling the nodes in an FVS can drive the system toward a target attractor. However, FVS control can be computationally expensive in large networks. To overcome this, we propose two methods that exploit modularity within networks: (1) selecting the top 10% of influential nodes in each module based on structural metrics, and (2) identifying a subset of the FVS by prioritizing the highest-ranked nodes within each module. These approaches are evaluated in synthetic Random Boolean Networks (RBNs) and validated on real biological Boolean models. Our results show that modular control strategies are more efficient than global approaches, particularly in networks with clear modular structures, which is pertinent for controlling biological systems. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Biological sciences/Systems biology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Editor invited by journal 30 Oct, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 19 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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