“Similar to a car manual, networks show you the relationship between the parts,” said Tijana Milenkovic, network biology expert and Frank M. Freimann Collegiate Professor of Engineering in the Department of Computer Science and Engineering at the University of Notre Dame.

“You see how each part of a biological system—gene, protein, neuron—contributes to the system’s overall performance and what happens if a part fails or is removed.”

In a paper published in Bioinformatics Advances, Milenkovic and 36 other researchers from over 25 universities and labs chart a comprehensive roadmap for network biology’s current state and future directions.

Networks allow researchers to represent biological systems as interconnected entities rather than collections of individual components. Biomolecules, such as DNA, amino acids, or proteins, are represented as points on a network while the links between the points indicate their interactions—physical, functional, or chemical.

Network biology promises to move medicine beyond its current one-size-fits-all approach. Instead of giving the same treatment to all patients with the same disease, treatments could be tailored to each individual’s unique molecular profile.

Also, networks show how a drug interacts with multiple biological targets, thereby revealing the ways in which a drug previously used to treat one condition might have the potential to treat another.

The paper, which originated from an NSF-funded workshop at Notre Dame, identifies five prominent research subfields within network biology: inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine.

Milenkovic noted that, despite numerous advances, challenges remain in the field, such as enhanced data generation, developing novel algorithms, refining evaluation frameworks, and a wider adoption of innovations.

“The goal is not just to predict likely interactions between biomolecules or identify interesting patterns in complex data,” said Milenkovic. “We want to help explain the underlying biological mechanisms that yield these patterns.”