The intricate dance between genes and behavior reveals evolution's master choreography at work.
Imagine watching a flock of birds migrating across continents or ants coordinating complex colony tasks and wondering what invisible hand guides these behaviors. For decades, scientists focused exclusively on the brain's neuronal networks as the conductor of this symphony. But a revolutionary perspective is emerging that looks deeper—to the very genes within each cell—to understand how behavior evolves and is orchestrated. Welcome to the world of evolutionary development based on genetic regulatory models for behavior, where we discover that the instructions for behavior are woven into our DNA in ways we never imagined.
For years, the predominant model for understanding animal behavior centered on neuronal networks (NNs)—the complex circuits of brain cells communicating through electrochemical signals that control sensory, integrative, and motor functions4 . While this explanation remains valuable, it presents an incomplete picture.
The turning point came when scientists noticed something unexpected. Research on eusocial insects revealed that highly social species showed unexpected evolutionary signatures in their regulatory genome—the parts of DNA that control when and how genes are switched on and off1 . This discovery suggested that complex social behavior left marks on the regulatory regions of genomes, hinting at a deeper layer of control beyond neuronal firing patterns.
This realization sparked a new approach: integrating gene regulatory networks (GRNs)—collections of regulatory interactions between genes—into our understanding of behavior1 . Just as developmental biologists had used GRNs to explain how a single fertilized egg transforms into a complex organism with diverse cell types, researchers began exploring how similar networks might guide behavioral transitions.
The emerging framework reveals that behavior arises from the interplay of three distinct but interconnected network types operating across different timescales.
The rapid-fire electrochemical signaling between brain cells that processes information in real-time4 .
Intracellular networks that coordinate the expression of hundreds to thousands of genes in behaviorally relevant patterns4 .
Networks that guide the development of new cells and neural connections over longer timescales1 .
The relationship between these networks represents a fundamental shift in perspective. bGRNs don't replace neuronal networks—they work in concert with them, influencing NN function by controlling the production of key proteins, peptides, and signaling molecules while being influenced by neuronal activity in return1 4 .
| Network Type | Primary Components | Timescale | Primary Function |
|---|---|---|---|
| Neuronal Networks (NNs) | Neurons, synapses | Milliseconds to seconds | Rapid information processing and signal transmission |
| Behavioral GRNs (bGRNs) | Transcription factors, regulatory DNA | Hours to days | Coordinating gene expression changes associated with behavioral states |
| Developmental GRNs (dGRNs) | Morphogens, transcription factors | Days to lifetime | Guiding development of neural structures and connections |
One of the most remarkable discoveries in this field is the existence of behavioral genetic toolkits—highly conserved genes that consistently regulate the development of similar behavioral phenotypes across diverse species2 . This phenomenon was first well-documented for morphological traits but surprisingly proves relevant for complex behaviors as well, despite behavior being regulated by many genes operating in diverse tissues.
The identification of these toolkits represents a groundbreaking development, offering new approaches to understanding the evolutionary genetic basis of behaviors, some of the most complex phenotypes known2 . Creative research approaches fueled by advances in behavioral genomics have driven these successes, elevating the use of integrative and comparative approaches in the study of behavior.
While the theoretical framework was developing, a crucial experiment in 2022 provided stunning empirical evidence for how gene regulation evolves. Researchers at several institutions tackled a long-standing goal in biology: constructing complete fitness landscapes where DNA sequences are mapped to organismal fitness3 .
Scientists began by randomly sampling millions of promoter DNA sequences—the regulatory regions that control gene expression—in the yeast Saccharomyces cerevisiae3 .
For each of these millions of DNA sequences, researchers measured the exact expression levels they produced3 .
Using this enormous dataset, the team trained deep neural network models that learned to predict expression levels from DNA sequence alone3 .
These models enabled the researchers to visualize and explore fitness landscapes—mappings between DNA sequences and their functional consequences—and study how gene regulation evolves under different evolutionary scenarios3 .
The study yielded several transformative insights into regulatory evolution:
The researchers found that gene expression can diverge rapidly between populations, with most changes occurring early in evolutionary trajectories3 .
As gene sequences evolved toward optimal expression levels, each subsequent beneficial mutation tended to provide smaller fitness advantages than previous ones3 .
When genes needed to perform different expression objectives in various environments, these conflicting demands constrained how expression could adapt3 .
Under stabilizing selection, regulatory complexity tended to become moderated over evolutionary time3 .
| Finding | Description | Evolutionary Implication |
|---|---|---|
| Rapid Early Divergence | Most expression changes occur early in evolution | Regulatory evolution can proceed quickly when populations face new environments |
| Diminishing Returns | Later beneficial mutations have smaller effects | Evolution slows as populations approach adaptive peaks |
| Conflicting Objectives | Different environmental needs constrain adaptation | Compromises must be reached for generalist strategies |
| Moderated Complexity | Regulatory complexity decreases under stabilizing selection | Simpler, more robust solutions evolve when consistency matters |
Perhaps most remarkably, the researchers demonstrated that their models could detect signatures of selection from natural variation in regulatory sequences and even discover instances of convergent regulatory evolution—where different species evolve similar regulatory solutions independently3 .
The revolution in understanding behavior through genetic regulatory models relies on increasingly sophisticated research tools and technologies:
| Research Tool | Function | Application in Behavioral GRN Research |
|---|---|---|
| Single-Cell RNA Sequencing | Measures gene expression in individual cells | Resolves cellular heterogeneity in brain tissues and identifies cell-type-specific GRNs1 |
| Multispecies DNA Language Models | AI trained on multiple genomes to predict regulatory elements | Captures conserved regulatory elements across evolutionary distances where alignment fails6 |
| Evolutionary Algorithms | Computer programs that simulate natural selection | Models GRN evolution and designs artificial regulatory networks8 |
| Deep Neural Networks | Complex machine learning models | Predicts expression levels from DNA sequence and maps fitness landscapes3 |
| Comparative Genomics | Compares genomes across species | Identifies conserved regulatory elements and behavioral toolkits2 7 |
The integration of gene regulatory networks into our understanding of behavior represents a fundamental shift in perspective. We're moving beyond seeing behavior as solely the product of electrical impulses in the brain to understanding it as the output of multiple interacting networks operating across different biological scales and timescales.
This expanded framework helps explain some of the most puzzling aspects of behavior—its evolutionary origins, its plasticity in response to environmental changes, and the complex interplay between innate tendencies and learned experiences.
The emerging picture reveals that the genetic code contains not just instructions for building organisms, but for conducting the complex behavioral symphony of life itself—a symphony where every note represents the precisely regulated expression of genes in a grand evolutionary composition.
The Social Brain: A Special Case for Gene Regulation
Research into social behavior has revealed particularly intriguing patterns. Studies comparing species with different social complexities found that eusocial insects showed distinctive signatures in their regulatory genomes1 . This discovery opens the intriguing possibility that social behavior, one of the most complex behavioral phenotypes, may have unique characteristics in its GRNs1 4 .
"Our goal was to highlight another level of dynamics that accompany behavior and not just the dynamics of the NN" - Saurabh Sinha1
The transcriptomic states associated with behavior also differ from developmental states in important ways. While both involve stable gene expression profiles, behavioral states are more fluid—animals can rapidly transition between different behaviors based on context, whereas developmental progression is typically more unidirectional4 . This difference in plasticity likely reflects corresponding differences in the underlying GRN architectures, such as more frequent use of specific regulatory circuits or feedback loops that maintain stability while allowing flexibility.
Environmental Interplay: How Experience Shapes Genetic Regulation
The GRN perspective provides a powerful framework for understanding how environmental factors influence behavior through genetic regulation. Environmental stimuli can induce epigenetic changes that alter how GRNs function, which then modulate neuronal network activity and behavior1 . Looking at brain function through both neuronal networks and GRNs allows researchers to "bring in the environment in a credible way"1 .
This environment-GRN interplay operates in both directions—environmental experiences can reshape GRN function, and an organism's GRN composition influences how it responds to environmental stimuli. This bidirectional relationship creates a dynamic feedback loop that allows for adaptive behavioral responses to changing conditions while maintaining behavioral stability across an organism's lifespan.