Mapping network connectivity within zebrafish segmentation clock and wavefront

描述（由申请人提供）：脊椎动物前后轴的分割依赖于她/他的基因的振荡。在人类中，HES7的突变会导致脊柱脊柱发育不良，这是一种以椎骨和肋骨融合或畸形为特征的先天性疾病。在斑马鱼片段时钟中，her/hes家族的6个成员形成异二聚体和同二聚体，并负向调节其自身的转录。即使在这个简单的网络中，也有36种可能的二聚体- dna相互作用，并且，给定3个抑制水平，1017种可能的网络拓扑结构。先前基于转录因子（TF）结合的基因网络模型包括结合亲和力、细胞特异性蛋白浓度和染色质可及性的数据。然而，TF结合可能并不总是导致转录的显著变化，并且在许多情况下生化数据可能不完整。在这里，将开发一种不需要大量生化数据集的确定转录因子网络的方法。绘制网络将依赖于基因敲除和转录的定量测量，并结合数学建模和全局优化算法。从网络模型得出的预测将通过复合基因敲除、qPCR和原位杂交进行实验测试。将获得几种不协调表型的基于网络的解释。最后，这些相互作用的建模将用于阐明网络如何在物种之间或生物体内部进化，以最小的修改完成不同的任务。了解基因网络，例如这里研究的分割时钟，可以对生物过程进行建模。这些模型可用于识别细胞通路的节点，多种基因在疾病表型中的作用，以及洞察药物相互作用。

DESCRIPTION (provided by applicant): Segmentation of the vertebrate anterior-posterior axis is dependent on oscillations of her/he's genes. In humans, mutation of HES7 results in spondylocostal dysostosis, a congenital disorder characterized by fusion or malformation of the vertebrae and ribs. Within the zebrafish segmentation clock, six members of the her/hes family form both hetero- and homodimers and negatively regulate their own transcription. Even within this simple network, there are 36 possible dimer-DNA interactions and, given 3 levels of repression, 1017 possible network topologies. Previous models of gene networks based on transcription factor (TF) binding included data on binding affinities, cell-specific protein concentrations and chromatin accessibility. However, TF binding may not always lead to significant changes in transcription and in many cases biochemical data may be incomplete. Here, a method for determining transcription factor networks that does not need extensive biochemical datasets will be developed. Mapping the network will rely on gene knockdown and quantitative measurement of transcription combined with mathematical modeling and global optimization algorithms. Predictions derived from the network model will be tested experimentally via compound gene knockdown, qPCR and in situ hybridization. Network-based explanations for several incongruous phenotypes will be attained. Lastly, modeling these interactions will be used to shed light on how networks can evolve between species or within an organism to complete a different task with minimal modifications. Understanding gene networks, such as the segmentation clock studied here, enables modeling of biological processes. These models can lead to identification of nodes of cellular pathways, the role of multiple genes in disease phenotypes, and insight into drug interactions.