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<p>Transcription factors (TFs) are essential players in the regulation of gene expression and thus have been the subject of interest in the context of diseases and the manipulation of specific cell functions or pathways. The complex interplay of TFs and genes can be modeled as a network, in which each edge represents a regulatory influence. A challenge in such networks is the identification of a set of TFs with the maximum regulatory influence, which is of particular importance in the context of perturbation studies. We model the task of finding a set of TFs to maximize the influence on a set of genes as a probing problem in a bipartite graph. We test different adaptive and non-adaptive algorithms on simulated graphs and discuss their properties and adaptivity gap. Then, we apply the algorithms on real-life data to find TFs regulating genes involved in T-cell mediated immunity and lymphoid leukemia. The approach has minimal data requirements and can be readily applied to all other types of bipartite networks.</p>

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Probing transcription factor subsets in gene regulatory networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Lukas Geis,
  • Dennis Hecker,
  • Martin Hoefer,
  • Ulrich Meyer,
  • Marcel H. Schulz

摘要

Transcription factors (TFs) are essential players in the regulation of gene expression and thus have been the subject of interest in the context of diseases and the manipulation of specific cell functions or pathways. The complex interplay of TFs and genes can be modeled as a network, in which each edge represents a regulatory influence. A challenge in such networks is the identification of a set of TFs with the maximum regulatory influence, which is of particular importance in the context of perturbation studies. We model the task of finding a set of TFs to maximize the influence on a set of genes as a probing problem in a bipartite graph. We test different adaptive and non-adaptive algorithms on simulated graphs and discuss their properties and adaptivity gap. Then, we apply the algorithms on real-life data to find TFs regulating genes involved in T-cell mediated immunity and lymphoid leukemia. The approach has minimal data requirements and can be readily applied to all other types of bipartite networks.