Grupo de Fundamentos y Enseñanza de la Física y los Sistemas Dinámicos, Instituto de Biología, Facultad de Ciencias Exactas y Naturales, Universidad de Antioquia UdeA, Medellín, Colombia.
Grupo de Fundamentos y Enseñanza de la Física y los Sistemas Dinámicos, Instituto de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Antioquia UdeA, Medellín, Colombia.
PLoS Comput Biol. 2022 Feb 14;18(2):e1009704. doi: 10.1371/journal.pcbi.1009704. eCollection 2022 Feb.
A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell's gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.
组织和合成生物学中的一个核心问题是理解组织或培养皿中的细胞如何处理外部线索，并将这些信息转化为一致的反应，例如，终端分化状态。长期以来，人们一直认为这种位置信息可以完全归因于特定信号分子（即形态发生素）浓度的梯度。然而，实验方法和计算机建模的进步已经证明了细胞基因调控网络（GRN）的动态在解码形态发生素所携带的信息方面起着至关重要的作用，最终这些信息被转化为空间模式。这种形态发生素解释机制在系统生物学中受到了广泛关注，因为它是研究复杂基因型-表型图谱中涌现特性的一种可行系统。在本研究中，我们应用马尔可夫链蒙特卡罗（MCMC）样算法来探测具有在 30 个细胞排列中间产生带状表达模式（目标表型）的三节点 GRN 的设计空间，这类似于发育胚胎中简单的（1-D）形态发生场。与迄今为止发表的大多数建模研究不同，我们在这里探索了具有潜在感知相同输入信号不同能力的节点的 GRN 拓扑结构空间。这使得在搜索空间过程中有更多的灵活性，从而使我们能够识别出更大的一组潜在有趣和可实现的形态发生素解释机制。在使用搜索空间算法选择的 2061 个 GRN 中，我们发现了 714 类可以正确解释形态发生素的网络拓扑结构。值得注意的是，生成目标表型以响应输入信号的主要网络基元是 3 型非相干前馈环（I3-FFL），这与之前的理论预期和实验观察结果一致。特别是，与之前报道的模式形成 GRN 拓扑结构相比，我们发现了各种各样的新网络设计，其中一些可能值得通过合成生物学方法进行研究，以测试具有最小调节复杂性的网络设计解释发育线索的能力。
