Geometry, Genetics and Development

几何、遗传学和发育

基本信息

批准号：
2013131
负责人：
Eric Siggia
金额：
$ 89.93万
依托单位：
Rockefeller University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013131&HistoricalAwards=false
关键词：
Geometry Genetics Development

项目摘要

Developmental biology has been immensely successful in reducing the seeming miraculous self-organization of a fertilized egg to a fetus and adult to a list of genes and the instructions for their regulation in the noncoding genome. But knowing the parts (essentially all genes) is not commensurate with understanding their capacity to self-organize. Thus, we need to move from reductionism to integration, and physics, particularly condensed matter and statistical physics, has a long and successful history in phenomenological but still quantitative descriptions of Nature. Stem cell technologies allow one to build embryos from cells and thus test one's understanding. New imaging modalities and genetic interventions provide the means to reprogram the earliest steps of development in simple model organisms and quantify the outcomes. Development is about morphogenesis and the progressive specialization of cells as a function of time. The natural mathematical language for dynamics is geometric and the key ideas were formulated in the 20th century. Elements of that work provide a mathematically rigorous landscape analogy to development that enables a very compact phenomenological description that can be fit to data. Prior work by the PI has shown how simply confining stem cells in two dimensions elicits their potential for self-organization, which is surprisingly complex and employs the same cellular communications systems as in the embryo. This project will use the simplicity of synthetic systems and geometrical methods to quantify the genetic systems responsible for self-organization in mammals. Similar mathematical machinery is required to understand recent experiments in the nematode C. elegans that uses RNA interference to reprogram the founder cells in the embryo to new fates. How these new fates accommodate to their ectopic environment provide a strong quantifiable constraint on how the embryo self-organizes and are amenable to phenomenological treatments. The PI will continue to study the exact mutation that leads to Huntington disease in humans, which has a dramatic phenotype in stem cells differentiated on micropatterns.The Waddington landscape is an oft-cited metaphor for development, particularly when discussing stem cells. A theorem of Smale, plausibly applies to the gene regulatory networks as imagined by Waddington and proves that such systems can be represented by potential flow on a Riemannian manifold. The PI will show the utility of this representation in several examples. It is the only general way to implement our intuition that developmental ‘decisions’ take place in a low dimensional space, and it provides a compact functional form with which to challenge that intuition with data, thus reducing the number of dimensions in which the essential degrees of freedom reside, compared to the standard Michaelis-Menten representations of gene regulatory networks, which typically have many more variables than the dimension of the attractor they describe. The PI's experimental collaborations in stem cells and C.elegans development will provide the means to apply these theoretical representations to data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

发育生物学在将受精卵到胎儿和成人的看似神奇的自组织简化为一系列基因及其在非编码基因组中的调控指令方面取得了巨大成功，但了解这些部分（本质上是所有基因）却并非如此。因此，我们需要从还原论转向整合论，而物理学，特别是凝聚态物理学和统计物理学，在现象学方面有着悠久而成功的历史，但仍然是定量的。干细胞技术允许人们从细胞中构建胚胎，从而测试人们的理解，从而提供了重新编程简单模型生物体的最早发育步骤并量化其结果的方法。细胞随着时间的变化而逐渐专业化。动力学的自然数学语言是几何学，该工作的关键思想是在 20 世纪提出的，它为发展提供了数学上严格的景观类比，从而实现了非常紧凑的发展。 PI之前的工作已经表明，将干细胞简单地限制在二维空间中可以激发它们的自组织潜力，这一过程非常复杂，并且采用了与胚胎中相同的细胞通信系统。将使用合成系统和几何方法的简单性来量化负责哺乳动物自组织的遗传系统，这需要与最近在线虫线虫实验中使用类似的数学机制，该实验使用 RNA 干扰来了解胚胎中创始细胞的重新编程。到这些新的命运如何适应异位环境，对胚胎如何自组织以及如何接受现象学治疗提供了强有力的量化限制，PI将继续研究导致人类亨廷顿病的确切突变。沃丁顿景观是一个经常被引用的发育隐喻，特别是在讨论干细胞时，斯梅尔定理似乎适用于干细胞。沃丁顿（Waddington）想象的基因网络调节，并证明这种系统可以用黎曼流形上的势流来表示。PI将在几个例子中展示这种表示的实用性，这是实现我们的直觉的唯一通用方法。 ”发生在低维空间中，它提供了一种紧凑的函数形式，可以用数据挑战直觉，从而与基因的标准 Michaelis-Menten 表示相比，减少了基本自由度所在的维度数监管网络，这些变量通常比他们所描述的吸引子的维度多得多。 PI 在干细胞和线虫发育方面的实验合作将提供将这些理论表示应用于数据的方法。该奖项反映了 NSF 的法定使命，并被认为是值得的。通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geometry of gene regulatory dynamics

DOI：
10.1073/pnas.2109729118
发表时间：
2021-09-21
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Rand, David A.;Raju, Archishman;Siggia, Eric D.
通讯作者：
Siggia, Eric D.

Mechanical regulation of early vertebrate embryogenesis