Complex Networks in Biological Systems
Summary
Complex networks in biological systems provide a unifying framework for understanding interactions among genes, proteins, metabolites and cells. These networks exhibit non-random topologies characterised by modular organisation, hub nodes, recurring motifs and hierarchical layers. Advances in high-throughput experimental mapping and computational inference have revealed how biological networks rewire in response to environmental cues, developmental programmes and pathological insults. Analyses of network dynamics—incorporating temporal and multilayer representations—have elucidated mechanisms of robustness, adaptability and failure in cellular regulatory circuits, neural ensembles and ecological communities. The integration of graph theory with statistical physics and machine learning is driving predictive models of signalling cascades, metabolic fluxes and synaptic connectivity. Practical applications range from identifying drug targets in disease-associated subnetworks to guiding the design of synthetic gene circuits and optimising ecosystem management strategies.
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Complex Networks in Biological Systems publication trend
The graph below shows the total number of articles in complex networks in biological systems across all publications each year (not limited to Nature Index journals).
Technical terms
Complex network: A system of interconnected elements whose structure deviates from random graphs, often displaying modular, hierarchical and scale-free properties.
Feedback interaction: A connection in which the output of a pathway or reaction influences its own upstream activity, stabilising or amplifying network dynamics.
Temporal network: A representation of a network whose nodes or edges change over time, capturing dynamic rewiring and activity patterns.
Attractor entropy: A measure of the diversity and distribution of long-term states or cycles in a network’s dynamic landscape, reflecting complexity.
Robustness: The capacity of a network to maintain functional output and structural integrity in the face of internal or external perturbations.
References
- Identify structures underlying out-of-equilibrium reaction networks with random graph analysis. Chemical Science (2025).
- Entropy as a Robustness Marker in Genetic Regulatory Networks. Entropy (2020).
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