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：
Purushottam Dixit
金额：
$ 38.07万
依托单位：
YALE UNIVERSITY
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：
https://reporter.nih.gov/project-details/10953177
关键词：
MANET Maximum Entropy Neural Networks

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.

项目摘要/摘要尽管最近在单细胞技术方面取得了进步和同时发展统计方法，我们预测单细胞动力学并识别生化的能力决定细胞对细胞变异性的过程仍然是基本的。我们已经确定了钥匙在实现对单细胞行为的机械理解方面的障碍：我们没有将单个单元数据与机械信号网络模型集成的计算方法。基于我们以前的工作并利用神经网络中的尖端发展，我们提出了一项综合研究计划，以弥合这一差距。将单细胞数据与机械模型集成的核心问题是，即使很大比例数据仅部分约束模型，导致一个模型家族，该系列适合数据出色地。然后，我们如何从模型中选择？我们的策略是使用最大熵（最大介绍最不复杂模型的方法：一种不影响任何结果的方法除非数据和机械约束保证。在过去的十年中，我们有开创了最大用户对生物网络动力学建模的新颖使用。在接下来的五个多年来，我们计划实现两个主要的研究目标。（1）构建和验证计算将单元数据与模型集成到（2）实验者，使用开发的框架研究两个重要的变异性途径；有丝分裂原激活的蛋白激酶（MAPK）途径和机械转导。我们设想该框架在探索细胞到细胞的机械起源时必不可少跨广泛的信号网络的细胞变异性。值得注意的是，受约束的模型是在许多定量生物学领域，无处不在，包括实验室的其他两项研究焦点：代谢和微生物组动力学。这里提出的计划将直接受益大规模数据与机械模型的集成以及否则的主要探索隐藏的假设。