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

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/文摘

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.
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Purushottam Dixit其他文献

Purushottam Dixit的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ 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:

相似海外基金

A principled generalization of the maximum entropy principle for non-Shannon systems
非香农系统最大熵原理的原则概括
  • 批准号:
    23K16855
  • 财政年份:
    2023
  • 资助金额:
    $ 38.07万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Maximum Entropy Reinforcement Learning
最大熵强化学习
  • 批准号:
    573997-2022
  • 财政年份:
    2022
  • 资助金额:
    $ 38.07万
  • 项目类别:
    University Undergraduate Student Research Awards
AI and robotics - applying the maximum entropy framework to real-world robotics tasks
人工智能和机器人——将最大熵框架应用于现实世界的机器人任务
  • 批准号:
    2745856
  • 财政年份:
    2022
  • 资助金额:
    $ 38.07万
  • 项目类别:
    Studentship
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:
Tyre-Road Friction Coefficient Estimation using Maximum Entropy
使用最大熵估计轮胎-路面摩擦系数
  • 批准号:
    EP/V010778/1
  • 财政年份:
    2021
  • 资助金额:
    $ 38.07万
  • 项目类别:
    Research Grant
Maximum Entropy on Mean Methods in Data Science
数据科学中平均方法的最大熵
  • 批准号:
    562371-2021
  • 财政年份:
    2021
  • 资助金额:
    $ 38.07万
  • 项目类别:
    University Undergraduate Student Research Awards
CAREER: A cross-scale, data-efficient approach to understanding plant hydraulic regulation using optimization and maximum entropy
职业:一种跨尺度、数据高效的方法,利用优化和最大熵来理解工厂水力调节
  • 批准号:
    2045610
  • 财政年份:
    2021
  • 资助金额:
    $ 38.07万
  • 项目类别:
    Continuing Grant
Searching for the mechanism of river channel network formation using the maximum entropy production principle and challenge to mathematical morphology
利用最大熵产生原理寻找河道网络形成机制并对数学形态学提出挑战
  • 批准号:
    21K18740
  • 财政年份:
    2021
  • 资助金额:
    $ 38.07万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Exploratory)
A spline maximum entropy method for integral equations and boundary value problems
积分方程和边值问题的样条最大熵法
  • 批准号:
    540031-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 38.07万
  • 项目类别:
    University Undergraduate Student Research Awards
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了