Systems Biology of Shape and Size Regulation

摘要 在发育和再生过程中，体型和大小的分子调控涉及 许多途径与细胞的生物物理特性精确地结合在一起， 组织动力学，一个在整个动物水平上知之甚少的复杂过程。整体 该项目的目标是获得对遗传调节的机械理解， 通过开发和应用新的集成系统协调大规模组织生长 生物学方法。结合体内实验及其形态学形式化， 数学生物物理模型的机器学习，我们将辨别分子机制 控制着生长，形状和大小的调节我们将利用涡虫的健壮性 蠕虫解决分子和物理机制调节其非凡的 自我平衡和再生能力的增长，退化，并再生他们的整个身体的形状 和几乎所有截肢的器官，大小都在一个数量级上。 这项工作将开发新的计算系统生物学方法，并将其与 全身基因表达成像以及手术和遗传操作分析，以阐明 身体形状和大小的分子调节器。形态学遗传学和手术数据 用新的数学本体进行形式化，这将作为新机器的输入。 学习方法，能够推断机制基因调控网络。监管网络 将采用一种新的全身数学连续方法进行定量建模 生物物理模拟，包括组织生长，粘附分子和基因调控。这 结合机器学习和生物物理建模的计算框架将能够 从大规模的形式化实验中发现生长和形状调控的机制 数据集。机器学习方法将发现新的遗传相互作用， 根据遗传学导致的形态学和基因表达结果的预测 手术操作将在实验室通过RNAi和原位杂交试验进行验证。 集成机器学习、生物物理数学建模、本体形式化和 体内手术和分子测定代表了一种全面的系统生物学方法， 阐明了形状和大小的规则。这项工作将提供一个机械的理解 调节组织生长动力学的不同遗传途径以及它们如何相互作用 精确地在它们之间，并与组织生物物理学一起创建和维持全身规模 有针对性的形状和大小。这项工作将为新的应用和新的疗法铺平道路 在人类发育、再生和癌症医学方面。

Abstract The molecular regulation of body shape and size during development and regeneration involves numerous pathways precisely integrated together with the biophysical properties of cellular and tissue dynamics, a complex process poorly understood at the level of whole animals. The overall goal of this project is to gain a mechanistic understanding of the genetic regulation and coordination of large-scale tissue growth by developing and applying a novel integrated systems biology approach. Combining in vivo experiments and their morphological formalization with machine learning of mathematical biophysical models, we will discern the molecular mechanisms that control growth, shape, and size regulation. We will leverage the robustness of the planarian worm to address the molecular and physical mechanisms regulating their extraordinary homeostatic and regenerative capacity to grow, degrow, and regenerate their whole-body shapes and organs from almost any amputation and across one order of magnitude in sizes. This work will develop novel computational systems biology methods and integrate them with whole-body gene expression imaging and surgical and genetic manipulations assays to elucidate the molecular regulators of body shape and size. Morphological, genetic, and surgical data will be formalized with novel mathematical ontologies, which will serve as input to new machine learning methods able to infer mechanistic gene regulatory networks. The regulatory networks will be quantitatively modeled with a novel mathematical continuous approach for whole-body biophysical simulation, including tissue growth, adhesion molecules, and gene regulation. This computational framework combining machine learning with biophysical modeling will be able to discover the mechanisms of growth and shape regulation from large formalized experimental datasets. Novel genetic interactions will be discovered by the machine learning methodology, which predictions in terms of morphological and gene expression outcomes resulting from genetic and surgical manipulations will be validated at the bench via RNAi and in situ hybridization assays. Integrating machine learning, biophysical mathematical modeling, ontological formalizations, and in vivo surgical and molecular assays represents a comprehensive systems biology approach for elucidating the regulation of shape and size. This work will provide a mechanistic understanding of the diverse genetic pathways that regulate tissue growth dynamics and how they interact precisely between them and with tissue biophysics to create and maintain whole-body scale targeted shapes and sizes. This work will pave the way for new applications and novel therapies in human developmental, regenerative, and cancer medicine.