Theoretical Modeling of Gene Regulatory Networks in Non-Model Organisms

Abstract

Theoretical modeling of gene regulatory networks (GRNs) in non-model organisms represents a vital frontier in systems biology, where data scarcity and incomplete genomic annotation challenge conventional empirical methods. This study presents a comprehensive theoretical exploration of how mathematical and computational frameworks can be applied to reconstruct, analyze, and interpret GRNs in species beyond the classical model systems. By integrating network theory, information-theoretic inference, and dynamical systems modeling, this paper articulates a conceptual foundation for understanding gene regulation as a process of information exchange and systemic organization. It argues that the abstraction of biological networks into formal models enables the discovery of underlying principles of control, robustness, and adaptability that are conserved across evolution, even in the absence of experimental validation. The paper develops a multilayer theoretical synthesis encompassing topological network structures, dynamic feedback regulation, and probabilistic inference strategies. Through examples from fungal, plant, and microbial systems, it demonstrates how systems-level integration and computational innovation can uncover hidden regulatory logic in underexplored taxa. The study concludes that theoretical modeling is not a substitute for empirical biology but a necessary complement that extends the reach of biological reasoning into domains where direct experimentation remains impractical. The work thus positions theoretical GRN modeling as both a methodological framework and a philosophical approach to understanding life’s organizational complexity across the full spectrum of biodiversity.