14 NSFBIO: Bilateral NSF/BIO-BBSRC - Modeling the effects of intrinsic and extrinsic signaling on cellular differentiation in plants

尽管植物发育具有可塑性，但即使在扰动存在的情况下，植物的细胞分化也是非常强大的。因此，迫切需要更好地了解控制细胞发育的复杂调控网络，以及它们如何运作、相互作用和响应细胞间和细胞外信号，以产生具有更高生物量和耐受性的植物。我们提出，参与细胞分化的基因调控网络的突发性特性可以通过对不同条件的发育特异性转录组特征的响应获得的信息来表示和数学测试。为了验证这一假设，我们使用拟南芥根韧皮筛元素作为模型，为此我们开发了一套独特的分子、数学和生理工具。由于去核筛元的完全分化以非常压缩的方式进行，在20个细胞内，se提供了前所未有的细胞分辨率的细胞分化模型。我们的目标是利用常微分方程和基于代理的建模系统，将发育特异性转录组学、分子和成像数据与已知关键调节因子的信息整合在一起。这种数学方法将使我们能够从一个全面和定量的角度来看待控制从干细胞到分化的最后阶段的发展的设计规则。我们的目标是在将这个细胞过程暴露于两个不同的扰动后，通过整合额外的数据来迭代地提高我们模型的准确性和强度；共塑隔离和缺铁。

Despite the plasticity of plant development, cellular differentiation in plants is remarkablyrobust, even in the presence of perturbations. Thus, a greater understanding of the complexregulatory networks that control cellular development, and how they operate, interact, andrespond to intercellular and extracellular signals is urgently needed to generate plants withincreased biomass and stress-tolerance. We propose that emergent properties of gene regulatorynetworks involved in cellular differentiation can be represented and tested mathematicallyusing information gained from development-specific transcriptomic signatures in response tovarying conditions. To test this hypothesis we are using as a model the Arabidopsis root phloemsieve element, for which we have developed a unique set of molecular, mathematical, and physiologicaltools. Because complete differentiation of the enucleated sieve elements takes place in avery compressed manner, within 20 cells, SEs provide a cell differentiation model of unprecedentedcellular resolution. Our goal is to integrate development-specific transcriptomic, molecularand imaging data, with information about known key regulators using a system of ordinary differentialequations and agent based modeling. This mathematical approach will allow us to gain a comprehensiveand quantitative perspective of the design rules governing the progression from stem cellsto their final stage of differentiation. We aim to iteratively improve the accuracy and strengthof our models by integrating additional data after exposing this cellular process to two disparateperturbations; symplastic isolation and iron deficiency.