首页> 外文会议>Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; Lecture Notes in Computer Science; 4447 >Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation
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Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation

机译:拟南芥芽顶分生模型:空间格局形成的参数估计。

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Understanding the self-regulatory mechanisms controlling the spatial and temporal structure of multicellular organisms represents one of the major challenges in molecular biology. In the context of plants, shoot apical meristems (SAMs), which are populations of dividing, undifferentiated cells that generate organs at the tips of stems and branches throughout the life of a plant, are of particular interest and currently studied intensively. Here, one key goal is to identify the genetic regulatory network organizing the structure of a SAM and generating the corresponding spatial gene expression patterns.This paper addresses one step in the design of SAM models based on ordinary differential equations (ODEs): parameter estimation for spatial pattern formation. We assume that the topology of the genetic regulatory network is given, while the parameters of an ODE system need to be determined such that a particular stable pattern over the SAM cell population emerges. To this end, we propose an evolutionary algorithm-based approach and investigate different ways to improve the efficiency of the search process. Preliminary results are presented for the Brusselator, a well-known reaction-diffusion system.
机译:理解控制多细胞生物的时空结构的自我调节机制代表了分子生物学的主要挑战之一。在植物中,芽顶分生组织(SAMs)是一种正在分裂的,未分化的细胞群体,在整个植物的生命周期中都会在茎和枝的尖端产生器官,这是引起人们特别关注的问题,目前正在深入研究。在这里,一个关键目标是识别组织SAM结构并生成相应空间基因表达模式的遗传调控网络。本文着重介绍基于常微分方程(ODE)的SAM模型设计的一个步骤:空间格局的形成。我们假设已经给出了遗传调控网络的拓扑结构,同时需要确定ODE系统的参数,以便在SAM细胞群体上出现特定的稳定模式。为此,我们提出了一种基于进化算法的方法,并研究了提高搜索过程效率的不同方法。给出了Brusselator(一种著名的反应扩散系统)的初步结果。

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