Characterizing Regulatory Interactions and Dimensionality in Gene Networks Driving Cell-Fate Choices

PradyumnaHarlapur

Indian Institute of Science

"Characterizing Regulatory Interactions and Dimensionality in Gene Networks Driving Cell-Fate Choices"

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes (Hari*, Harlapur* et al. iScience 2025). We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. We then examined how biological networks are organized with specific topologies that allow them to remain sparse while effectively coordinating decision-making under various levels of coherent interactions (i.e., structural balance). We found that networks with low coherence needed higher densities to show coordinated expression profiles. The balance between sparsity and coordinated control highlights the role of network architecture in ensuring stable and robust phenotypic outcomes, providing new insights into how GRNs guide cellular behavior precisely yet adaptable. These results shed light on how, despite being very sparse, the networks that govern various cellular decisions follow certain basic design principles to ensure the expression between the nodes involved is well coordinated.
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