MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior
MANET:用于单细胞行为机械建模的最大熵神经网络
基本信息
- 批准号:10953177
- 负责人:
- 金额:$ 38.07万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-01 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Project Summary/Abstract
Despite recent experimental advances in single cell techniques and a concurrent development in
statistical methods, our ability to predict single cell dynamics and identify the biochemical
processes that dictate cell-to-cell variability remains rudimentary. We have identified the key
roadblock in achieving mechanistic understanding of single cell behavior: we do not have
computational methods to integrate single cell data with mechanistic signaling network models.
Building upon our previous work and leveraging cutting-edge developments in neural networks,
we propose a comprehensive research program to bridge this gap.
The central problem in integration of single cell data with mechanistic models is that even large-
scale data only partially constrain the models, leading to a family of models that fit the data equally
well. How do we then choose from the models? Our strategy is to use the Maximum Entropy (Max
Ent) approach which infers the least complex model: one that does not disfavor any outcome
unless warranted by the data and the mechanistic constraints. Over the past decade, we have
pioneered the novel use of Max Ent to model dynamics of biological networks. In the next five
years, we plan to have two main research goals; (1) to build and validate the computational
architecture required to integrate single cell data with models and (2) in close collaboration with
experimentalists, use the developed framework to study the variability in two important
pathways; the mitogen activated protein kinase (MAPK) pathway and mechanotransduction. We
envision that this framework will be indispensable in exploring the mechanistic origins of cell-to-
cell variability across a broad range of signaling networks. Notably, under-constrained models are
ubiquitous in many areas of quantitative biology, including two of the laboratory’s other research
foci: metabolism and microbiome dynamics. The program proposed here will directly benefit
integration of large-scale data with mechanistic models and a principled exploration of otherwise
hidden hypotheses.
项目总结/摘要
尽管最近在单细胞技术方面的实验进展和在细胞培养方面的同时发展,
统计方法,我们预测单细胞动力学和识别生物化学
决定细胞间变异性的过程仍然是基本的。我们已经找到了
在实现单细胞行为的机械理解的障碍:我们没有
将单细胞数据与机械信号网络模型整合的计算方法。
基于我们以前的工作,并利用神经网络的前沿发展,
我们提出了一个全面的研究计划,以弥补这一差距。
单细胞数据与机械模型整合的中心问题是,即使是大的-
比例数据仅部分约束模型,导致模型族与数据拟合相等
好.那么,我们如何从模型中进行选择呢?我们的策略是使用最大熵(Max
Ent)方法,该方法推断出最不复杂的模型:一个不会不利于任何结果的模型
除非由数据和机械约束保证。在过去的十年里,我们已经
开创了使用Max Ent来模拟生物网络动态的新方法。未来五
我们计划有两个主要的研究目标:(1)建立和验证计算
将单个单元数据与模型集成所需的架构,以及(2)与
实验学家,使用开发的框架来研究两个重要的变化
信号转导通路;丝裂原活化蛋白激酶(MAPK)通路和机械转导。我们
设想这一框架将是不可或缺的探索机制的起源细胞到
细胞变异性在广泛的信号网络。值得注意的是,约束不足的模型
在定量生物学的许多领域中无处不在,包括该实验室的其他两项研究
焦点:代谢和微生物组动力学。这里提出的方案将直接受益于
大规模数据与机械模型的整合以及对其他方法的原则性探索
隐藏的假设
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
GENERALIST: A latent space based generative model for protein sequence families.
- DOI:10.1371/journal.pcbi.1011655
- 发表时间:2023-11
- 期刊:
- 影响因子:4.3
- 作者:
- 通讯作者:
EMBED: Essential MicroBiomE Dynamics, a dimensionality reduction approach for longitudinal microbiome studies.
- DOI:10.1038/s41540-023-00285-6
- 发表时间:2023-06-20
- 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
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Purushottam Dixit其他文献
Purushottam Dixit的其他文献
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{{ truncateString('Purushottam Dixit', 18)}}的其他基金
MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior
MANET:用于单细胞行为机械建模的最大熵神经网络
- 批准号:
10680431 - 财政年份:2021
- 资助金额:
$ 38.07万 - 项目类别:
MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell Behavior
MANET:用于单细胞行为机械建模的最大熵神经网络
- 批准号:
10273855 - 财政年份:2021
- 资助金额:
$ 38.07万 - 项目类别:
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