The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India.
Homi Bhabha National Institute (HBNI), Mumbai, 400094, India.
Sci Rep. 2022 Oct 28;12(1):18156. doi: 10.1038/s41598-022-22654-7.
Boolean networks have been widely used to model gene networks. However, such models are coarse-grained to an extent that they abstract away molecular specificities of gene regulation. Alternatively, bipartite Boolean network models of gene regulation explicitly distinguish genes from transcription factors (TFs). In such bipartite models, multiple TFs may simultaneously contribute to gene regulation by forming heteromeric complexes, thus giving rise to composition structures. Since bipartite Boolean models are relatively recent, an empirical investigation of their biological plausibility is lacking. Here, we estimate the prevalence of composition structures arising through heteromeric complexes. Moreover, we present an additional mechanism where composition structures may arise as a result of multiple TFs binding to cis-regulatory regions and provide empirical support for this mechanism. Next, we compare the restriction in BFs imposed by composition structures and by biologically meaningful properties. We find that though composition structures can severely restrict the number of Boolean functions (BFs) driving a gene, the two types of minimally complex BFs, namely nested canalyzing functions (NCFs) and read-once functions (RoFs), are comparatively more restrictive. Finally, we find that composition structures are highly enriched in real networks, but this enrichment most likely comes from NCFs and RoFs.
布尔网络已被广泛用于模拟基因网络。然而，这些模型在一定程度上是粗糙的，它们抽象掉了基因调控的分子特异性。或者，基因调控的二部布尔网络模型明确区分基因和转录因子 (TF)。在这种二部模型中，多个 TF 可以通过形成异源复合物来同时对基因调控做出贡献，从而产生组成结构。由于二部布尔模型相对较新，因此缺乏对其生物学合理性的经验性研究。在这里，我们估计了通过异源复合物产生的组成结构的普遍性。此外，我们还提出了另一种机制，即组成结构可能是由于多个 TF 结合到顺式调控区域而产生的，并为该机制提供了经验支持。接下来，我们比较了组成结构和具有生物学意义的属性对 BF 的限制。我们发现，尽管组成结构可以严重限制驱动基因的布尔函数 (BF) 的数量，但两种类型的最小复杂 BF，即嵌套 canalyzing 函数 (NCF) 和只读函数 (RoF)，则具有更严格的限制。最后，我们发现组成结构在真实网络中高度丰富，但这种富集很可能来自于 NCF 和 RoF。
