Gene Regulatory Networks that Support Multi-fate Cellular Decisions

Cell fate decisions are driven by gene regulatory networks (GRNs). While the mutually inhibitory toggle switch effectively models binary fate decisions, fully connected inhibitory networks with more than two nodes fail to capture multi-fate decisions due to the low prevalence of "single high states", where only a single master regulator is highly expressed. The goal of this study is to find network structures that support all single high states. We find that the only network that attains the highest possible prevalence of all single high states within the set of monotone Boolean (MB) models is completely disconnected. Since biological networks typically require connectivity, we investigate network structures that support equipotency, where all single high states have equal prevalence within MB models. Finally, we characterize the networks that support multistability between all single high states, finding that it is possible only in networks in which each node either has self-activations or is inhibited by every other network node. Our findings provide a theoretical framework for understanding the network design principles that can support simultaneous differentiation into multiple distinct cell types.

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Publication Details

Journal
Bulletin of Mathematical Biology
Published
2026-11-06
DOI
https://doi.org/10.1007/s11538-026-01791-4
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Gene Regulatory Networks that Support Multi-fate Cellular Decisions

Tomáš Gedeon, Mohit Kumar Jolly, Harshavardhan BV, Sarah Adigwe
4 links
Bulletin of Mathematical Biology
Gene Regulatory Network Analysis
article

Gene Regulatory Networks that Support Multi-fate Cellular Decisions

Tomáš Gedeon, Mohit Kumar Jolly, Harshavardhan BV, Sarah Adigwe
article en

Abstract

Cell fate decisions are driven by gene regulatory networks (GRNs). While the mutually inhibitory toggle switch effectively models binary fate decisions, fully connected inhibitory networks with more than two nodes fail to capture multi-fate decisions due to the low prevalence of "single high states", where only a single master regulator is highly expressed. The goal of this study is to find network structures that support all single high states. We find that the only network that attains the highest possible prevalence of all single high states within the set of monotone Boolean (MB) models is completely disconnected. Since biological networks typically require connectivity, we investigate network structures that support equipotency, where all single high states have equal prevalence within MB models. Finally, we characterize the networks that support multistability between all single high states, finding that it is possible only in networks in which each node either has self-activations or is inhibited by every other network node. Our findings provide a theoretical framework for understanding the network design principles that can support simultaneous differentiation into multiple distinct cell types.

Bulletin of Mathematical BiologyVol. 88(12)
Montana State University (US), Indian Institute of Science Bangalore (IN)
Air Force Research Laboratory
Openalex Percentile: Top 62%
Gene Regulatory Network Analysis
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Gene Regulatory Networks that Support Multi-fate Cellular Decisions — Tomáš Gedeon, Mohit Kumar Jolly, et al. · Bulletin of Mathematical Biology (2026) | TGRS Research Map | TGRS