Systems Biology of Shape and Size Regulation

形状和尺寸调节的系统生物学

• 批准号: 10437870
• 负责人: Daniel Lobo
• 金额: $ 37.82万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE COUNTY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10437870
• 关键词: Address; Amputation; Animals; Biological Assay; Biophysics; Cell Adhesion Molecules; Complex; Congenital Abnormality; Data; Data Set; Development; Gene Expression; Gene Expression Regulation; Genetic; Goals; Growth; Homeostasis; Human; Human Development; Image; In Situ Hybridization; Machine Learning; Malignant Neoplasms; Mathematics; Medicine; Methods; Modeling; Molecular; Morphology; Natural regeneration; Ontology; Operative Surgical Procedures; Organ; Organism; Outcome; Pathway interactions; Planarians; Process; RNA Interference; Regenerative capacity; Regulation; Shapes; Systems Biology; Tissues; Traumatic injury; Work; biophysical model; biophysical properties; computer framework; experimental study; gene regulatory network; genetic manipulation; in vivo; machine learning method; mathematical learning; mathematical model; novel; novel therapeutic intervention; novel therapeutics; regenerative; simulation

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.

抽象的 发育和再生过程中身体形状和大小的分子调节涉及 许多途径与细胞和细胞的生物物理特性精确整合在一起 组织动力学，这是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基因调控和 通过开发和应用新型集成系统协调大规模组织生长 生物学方法。将体内实验及其形态学形式化与 数学生物物理模型的机器学习，我们将辨别分子机制 控制生长、形状和大小调节。我们将利用涡虫的稳健性 蠕虫解决调节其非凡的分子和物理机制 生长、去生长和再生全身形状的稳态和再生能力 以及几乎所有截肢的器官，其大小相差一个数量级。 这项工作将开发新颖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基因操作分析来阐明 身体形状和尺寸的分子调节剂。形态学、遗传和手术数据将 用新颖的数学本体进行形式化，这将作为新机器的输入 能够推断机械基因调控网络的学习方法。监管网络 将采用一种新颖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 将机器学习与生物物理建模相结合的计算框架将能够 从大型正式实验中发现生长和形状调节的机制 数据集。机器学习方法将发现新的遗传相互作用， 根据遗传结果对形态和基因表达结果进行预测 手术操作将通过 RNAi 和原位杂交测定在实验室进行验证。 集成机器学习、生物物理数学建模、本体形式化和 体内手术和分子检测代表了一种全面的系统生物学方法 阐明形状和大小的规则。这项工作将提供机械理解 调节组织生长动力学的多种遗传途径及其相互作用 精确地在它们之间并利用组织生物物理学来创建和维持全身规模 目标形状和尺寸。这项工作将为新应用和新疗法铺平道路 在人类发育、再生和癌症医学领域。