Hari Kishore, Harlapur Pradyumna, Saxena Aashna, Haldar Kushal, Girish Aishwarya, Malpani Tanisha, Levine Herbert, Jolly Mohit Kumar
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India.
Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA.
iScience. 2025 Jan 2;28(2):111730. doi: 10.1016/j.isci.2024.111730. eCollection 2025 Feb 21.
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 and corresponding transcriptomic data 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. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.
细胞命运决定涉及全基因组范围内协调的表达变化，通常会导致有限数量的表型。尽管这些变化常被建模为简单的切换开关，但这些相当简单的表示往往忽略了控制这些变化的调控网络的复杂性。在这里，我们揭示了多种情况下复杂细胞决策网络背后的设计原则。我们表明，这些网络的涌现动态以及相应的转录组数据始终是低维的，这通过主成分1（PC1）解释的方差来量化。表型空间中的这种低维性源于这些网络中广泛的反馈回路，这些回路有效地促成了两组相互抑制节点的形成。我们使用团队强度作为一种度量来量化这些反馈相互作用，并表明其与PC1方差密切相关。通过使用不同拓扑结构的人工网络，我们还确定了生成稳定细胞命运格局的条件，为各种二元细胞决策网络提供了见解。
